phd biomedical data science

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• Biomedical Data Science, PhD

The current explosion of biomedical data provides an awesome opportunity to improve understanding of the mechanisms of disease and ultimately to improve human health care. However, fully harnessing the power of high-dimensional, heterogeneous data requires a new blend of skills including programming, data management, data analysis, and machine learning.

Blending the best of statistics and computer sciences, biostatistics and biomedical informatics, this program provides students the training they need to make sense of large-scale biomedical data, and to be scientific leaders in the team science that invariably accompanies such data. Unique features of the program include cross-training in computer science and biostatistics, and research rotations mentored by a program faculty member jointly with a scientific collaborator.

Please consult the table below for key information about this degree program’s admissions requirements. The program may have more detailed admissions requirements, which can be found below the table or on the program’s website.

Graduate admissions is a two-step process between academic programs and the Graduate School. Applicants must meet the minimum requirements of the Graduate School as well as the program(s). Once you have researched the graduate program(s) you are interested in, apply online .

Potential applicants include both those with bachelor’s degrees in an area of data-science (e.g., computer science, statistics), as well as health professionals and clinicians (e.g., MD's, PharmD's, RN's). It is expected that admitted applicants will have demonstrated an aptitude for computer science and math, fundamental programming skills, knowledge of data structures and algorithms, and at least two semesters of college calculus. We will however consider applicants who have a wide range of undergraduate backgrounds; providing opportunities to develop necessary skills immediately upon entering the program.

Applying to the Program

• A formal online application with required fee through the UW–Madison Graduate School
• Three letters of recommendation
• Transcripts from each higher-education institution attended
• A statement of purpose
• International degree-seeking applicants must prove English proficiency using the Graduate School's requirements .
• Evidence of quantitative preparation, including at least two semesters of college calculus (similar to MATH 221 – MATH 222 ) and either a course in linear algebra (similar to MATH 340 ) or courses in programming and data structures

For additional information about admission to the program, see PhD Program in Biomedical Data Science  on the department website.

Graduate School Resources

Resources to help you afford graduate study might include assistantships, fellowships, traineeships, and financial aid.  Further funding information is available from the Graduate School. Be sure to check with your program for individual policies and restrictions related to funding.

Program Resources

The program is designed such that almost all students who are accepted to the program will receive guaranteed funding for five years. This funding may take a number of forms including, but not limited to training grants, teaching assistantships, and research assistantships. For more information about funding opportunities, see Graduate Assistantships .

Minimum Graduate School Requirements

Major requirements.

Review the Graduate School minimum academic progress and degree requirements , in addition to the program requirements listed below.

Mode of Instruction

Mode of Instruction Definitions

Accelerated: Accelerated programs are offered at a fast pace that condenses the time to completion. Students typically take enough credits aimed at completing the program in a year or two.

Evening/Weekend: ​Courses meet on the UW–Madison campus only in evenings and/or on weekends to accommodate typical business schedules.  Students have the advantages of face-to-face courses with the flexibility to keep work and other life commitments.

Face-to-Face: Courses typically meet during weekdays on the UW-Madison Campus.

Hybrid: These programs combine face-to-face and online learning formats.  Contact the program for more specific information.

Online: These programs are offered 100% online.  Some programs may require an on-campus orientation or residency experience, but the courses will be facilitated in an online format.

Curricular Requirements

Required Courses

Graduate School Policies

The  Graduate School’s Academic Policies and Procedures  provide essential information regarding general university policies. Program authority to set degree policies beyond the minimum required by the Graduate School lies with the degree program faculty. Policies set by the academic degree program can be found below.

Major-Specific Policies

Prior coursework, graduate credits earned at other institutions.

With program approval, students are allowed to transfer no more than 9 credits of graduate course work from other institutions toward the graduate degree credit and graduate course work (50%) requirements. Course work earned ten years or more prior to admission to a doctoral degree is not allowed to satisfy requirements.

Undergraduate Credits Earned at Other Institutions or UW-Madison

Refer to the Graduate School: Transfer Credits for Prior Coursework policy.

Credits Earned as a Professional Student at UW-Madison (Law, Medicine, Pharmacy, and Veterinary careers)

Credits earned as a university special student at uw–madison.

Refer to the Graduate School: Probation policy.

Advisor / Committee

All students are required to conduct a yearly progress report meeting with their advisor, scheduled by December 17 and completed by April 30.

Credits Per Term Allowed

Time Limits

Refer to the Graduate School: Time Limits policy.

Grievances and Appeals

These resources may be helpful in addressing your concerns:

• Bias or Hate Reporting  
• Graduate Assistantship Policies and Procedures
• Office of the Provost for Faculty and Staff Affairs
• Employee Assistance (for personal counseling and workplace consultation around communication and conflict involving graduate assistants and other employees, post-doctoral students, faculty and staff)
• Employee Disability Resource Office (for qualified employees or applicants with disabilities to have equal employment opportunities)
• Graduate School (for informal advice at any level of review and for official appeals of program/departmental or school/college grievance decisions)
• Office of Compliance (for class harassment and discrimination, including sexual harassment and sexual violence)
• Office Student Assistance and Support (OSAS)  (for all students to seek grievance assistance and support)
• Office of Student Conduct and Community Standards (for conflicts involving students)
• Ombuds Office for Faculty and Staff (for employed graduate students and post-docs, as well as faculty and staff)
• Title IX (for concerns about discrimination)

Grievance Policy for Graduate Programs in the School of Medicine and Public Health

Any student in a School of Medicine and Public Health graduate program who feels that they have been treated unfairly in regards to educational decisions and/or outcomes or issues specific to the graduate program, including academic standing, progress to degree, professional activities, appropriate advising, and a program’s community standards by a faculty member, staff member, postdoc, or student has the right to complain about the treatment and to receive a prompt hearing of the grievance following these grievance procedures. Any student who discusses, inquiries about, or participates in the grievance procedure may do so openly and shall not be subject to intimidation, discipline, or retaliation because of such activity. Each program’s grievance advisor is listed on the “Research” tab of the SMPH intranet .

This policy does not apply to employment-related issues for Graduate Assistants in TA, PA and/or RA appointments.  Graduate Assistants will utilize the Graduate Assistantship Policies and Procedures (GAPP) grievance process to resolve employment-related issues.

This policy does not apply to instances when a graduate student wishes to report research misconduct.  For such reports refer to the UW-Madison Policy for Reporting Research Misconduct for Graduate Students and Postdoctoral Research Associates .

Requirements for Programs

The School of Medicine and Public Health Office of Basic Research, Biotechnology and Graduate Studies requires that each graduate program designate a grievance advisor, who should be a tenured faculty member, and will request the name of the grievance advisor annually.  The program director will serve as the alternate grievance advisor in the event that the grievance advisor is named in the grievance.  The program must notify students of the grievance advisor, including posting the grievance advisor’s name on the program’s Guide page and handbook.

The grievance advisor or program director may be approached for possible grievances of all types.  They will spearhead the grievance response process described below for issues specific to the graduate program, including but not limited to academic standing, progress to degree, professional activities, appropriate advising, and a program’s community standards.  They will ensure students are advised on reporting procedures for other types of possible grievances and are supported throughout the reporting process.  Resources on identifying and reporting other issues have been compiled by the Graduate School.

• The student is advised to initiate a written record containing dates, times, persons, and description of activities, and to update this record while completing the procedures described below.
• If the student is comfortable doing so, efforts should be made to resolve complaints informally between individuals before pursuing a formal grievance.
• Should a satisfactory resolution not be achieved, the student should contact the program’s grievance advisor or program director to discuss the complaint. The student may approach the grievance advisor or program director alone or with a UW-Madison faculty or staff member. The grievance advisor or program director should keep a record of contacts with regards to possible grievances.  The first attempt is to help the student informally address the complaint prior to pursuing a formal grievance. The student is also encouraged to talk with their faculty advisor regarding concerns or difficulties.
• If the issue is not resolved to the student’s satisfaction, the student may submit a formal grievance to the grievance advisor or program director in writing, within 60 calendar days from the date the grievant first became aware of, or should have become aware of with the exercise of reasonable diligence, the cause of the grievance.  To the fullest extent possible, a grievance shall contain a clear and concise statement of the grievance and indicate the issue(s) involved, the relief sought, the date(s) the incident or violation took place, and any specific policy involved.
• The grievance advisor or program director will convene a faculty committee composed of at least three members to manage the grievance.  Any faculty member involved in the grievance or who feels that they cannot be impartial may not participate in the committee.  Committee composition should reflect diverse viewpoints within the program.
• The faculty committee, through the grievance advisor or program director, will obtain a written response from the person or persons toward whom the grievance is directed. The grievance advisor or program director will inform this person that their response will be shared with the student filing the grievance.
• The grievance advisor or program director will share the response with the student filing the grievance.
• The faculty committee will make a decision regarding the grievance. The committee’s review shall be fair, impartial, and timely.  The grievance advisor or program director will report on the action taken by the committee in writing to both the student and the person toward whom the grievance was directed.
• The grievant will be notified in writing, within 5 business days of the written appeal, acknowledging receipt of the formal appeal and establishing a timeline for the review to be completed.
• The senior associate dean or their designee may request additional materials and/or arrange meetings with the grievant and/or others.  If meetings occur, the senior associate dean or their designee will meet with both the grievant and the person or persons toward whom the grievance is directed.
• The senior associate dean or their designee will assemble an ad hoc committee of faculty from outside of the student’s graduate program and ask them to prepare a written recommendation on whether to uphold or reverse the decision of the program on the student’s initial grievance.  The committee may request additional materials and/or arrange meetings with the grievant and/or others.  If meetings occur, the committee will meet with both the grievant and the person or persons toward whom the grievance is directed.
• The senior associate dean or their designee will make a final decision within 20 business days of receipt of the committee’s recommendation.
• The SMPH Office of Basic Research, Biotechnology, and Graduate Studies must store documentation of the grievance for seven years. Grievances that set a precedent may be stored indefinitely.
• The student may file an appeal of the School of Medicine and Public Health decision with the Graduate School.  See the Grievances and Appeals section of the Graduate School’s Academic Policies and Procedures .

Steps in the grievance procedures must be initiated and completed within the designated time periods except when modified by mutual consent. If the student fails to initiate the next step in the grievance procedure within the designated time period, the grievance will be considered resolved by the decision at the last completed step.

• Professional Development

Take advantage of the Graduate School's  professional development resources to build skills, thrive academically, and launch your career. 

• Learning Outcomes
• Articulate the biological context of a research question and the scientific relevance of analysis results.
• Communicate with scientific and quantitative (computational and statistical) colleagues about data analysis goals, methods, and results.
• Extract the statistical or computational problems from a scientific problem. Develop, characterize, and implement suitable analysis methods to answer questions from biomedical data. Evaluate the validity of analysis methods.
• Analyze data; extract knowledge and guide decisions based on biomedical data. Organize data and software so that quantitative analyses are meaningful and reproducible.
• Critically evaluate quantitative approaches in the scientific literature.
• Evaluate and develop study designs and recognize limitations and potential biases in research data sets.
• Identify the ethical and regulatory issues surrounding a research project.
• As part of a biological, biomedical or population health investigative team, serve as the leader in the area of rigorous computational and statistical investigation.

Faculty:  Broman, Buchanan, Burnside, Chappell, Chen, Chung, Craven, Dewey, Doan, Dyer, Elwert, Gangnon, Gianola, Gitter, Keles, Kendziorski, Kim, Lu, Mao, Mumford, Newton (chair), Ong,  Palta, Patel, Peissig, Rosa, Rosenberg, Roy, Singh, Sorkness, Tang, Yandell, Velten, Wang, Yu, Zhang, Zhu

• Requirements

Contact Information

Biostatistics and Medical Informatics School of Medicine and Public Health Biomedical Data Science, PhD [email protected] https://www.biostat.wisc.edu/

Shelley Maxted, Graduate Coordinator [email protected] 608-263-4826 4745 Medical Sciences Center 1300 University Ave., Madison, WI 53706

Beth Bierman, Graduate Coordinator [email protected] 608-265-8649 4775b Medical Sciences Center 1300 University Ave., Madison, WI 53706

Sushmita Roy, Director of PhD Program [email protected] 608-316-4453 3168 Discovery Building (WID) 333 N. Randall Ave., Madison, WI 53715

Michael Newton, Biostatistics & Medical Informatics Chair [email protected] 608-262-0086 1245a, K6/434 Medical Sciences Center 1300 University Ave., Madison, WI 53706

Grievance Advisor, Colin Dewey, Professor [email protected] 608-263-7610 2128 Genetics-Biotechnology Center Building 425 Henry Mall, Madison, WI 53706

Graduate Program Handbook View Here

Graduate School grad.wisc.edu

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Health Data Science - PHD

Program Guide

The PhD Program in Health Data Science trains the next generation of data science leaders for applications in public health and medicine. The program advances future leaders in health and biomedical data science by: (i) providing rigorous training in the fundamentals of health and biomedical data science, (ii) fostering innovative thinking for the design, conduct, analysis, and reporting of public health research studies, and (iii) providing practical training through real-world research opportunities at research centers and institutes directed by departmental faculty such as the Biostatistics Center (BSC), the Computational Biology Institute (CBI), and the Biostatistics and Epidemiology Consulting Service (BECS).

The PhD program consists of two concentrations; Biostatistics & Bioinformatics Concentration. Biostatistics is the science of designing, conducting, analyzing, and reporting studies aimed at advancing public health and medicine. Bioinformatics is the science of developing and applying computational algorithms and analysis methodologies to big biological data such as genetic sequences. Together they are foundational sciences for public health research and decision-making and essential to educating the next generation of leaders in health and biomedical data science.

The program takes advantage of the rich biostatistical and bioinformatics resources at GW and in the Nation’s Capital. Faculty in the Department of Biostatistics and Bioinformatics are engaged in a diverse research portfolio that includes areas such as diabetes, infectious diseases, mental health, maternal-fetal medicine, cardiovascular disease, emergency medicine, and oncology. Methodological interests of the faculty include the design and analyses of clinical trials including group-sequential and adaptive design, SMART trials, pragmatic trials, multiple testing, and benefit: risk evaluation; machine learning; meta-analyses; missing data; randomization tests, longitudinal data; the use of real-world data including electronic medical records; and research in biostatistics education methodologies. The Washington DC area is a hub for biostatisticians and bioinformaticians in government and industry, providing a rich source of adjunct faculty with relevant experience.  Specifically, the National Institutes of Health (NIH) and the Food and Drug Administration (FDA) have considerable human resources in these disciplines, many with world-class reputations. Several leading biostatisticians from the NIH are currently serving on doctoral committees and teach courses in the Milken Institute School of Public Health (GWSPH).

The program features a modernized applied curriculum, unique in its emphasis on cutting-edge data science techniques while retaining the rigor of traditional Biostatistics and Bioinformatics programs. The program prepares students to be independent researchers and effective collaborators in interdisciplinary studies.

APPLICATION DEADLINE: DECEMBER 1

Program Co-Directors:  Keith Crandall  (Bioinformatics Concentration)

Guoqing Diao  (Biostatistics Concentration)

Toshi Hamasaki  (Biostatistics Concentration)

GWSPH Doctoral programs admit students for the Fall term each academic year. Applications will be accepted beginning in August and are due no later than December 1st for the next matriculating cohort beginning in the following Fall term.  Find GWSPH graduate admissions information  here .

All applicants for the Biostatistics Concentration are required to submit current GRE scores (within five years of matriculation date). Applicants for the Bioinformatics Concentration are strongly encouraged to submit a GRE score.

Meeting the minimum requirements does not assure acceptance. Applicants must provide evidence of the completion of their undergraduate and/or graduate work before registration in GWSPH is permitted.

Concentration-Specific Prerequisites

Transfer Credits

Graduate courses taken prior to admission while in non-degree status may not be transferable into GWSPH programs. The PhD program is designed to serve students coming directly from an undergraduate degree. Students completing a master’s degree prior to admission to the PhD degree program may be eligible to transfer up to 24 credits toward the PhD coursework requirements. Depending on how many transfer credits are accepted, at minimum, 48 credits of additional coursework and dissertation research will be required.

PUBH 6080  | Pathways to Public Health (0 credits) PUBH 6421 | Responsible Conduct of Research (1 credit) PUBH 6850   | Introduction to SAS for Public Health Research (1 credit) PUBH 6851   | Introduction to R for Public Health Research (1 credit) PUBH 6852   | Introduction to Python for Public Health Research (1 credit)  PUBH 6860   | Principles of Bioinformatics (3 credits)  PUBH 6886   | Statistical and Machine Learning for Public Health Research (3 credits) PUBH 8099 | PhD Seminar: Cross Cutting Concepts in Public Health (1 credit) NOTE: In 23-24, PUBH 8099 was updated to PUBH 8001 PUBH 8870 | Statistical Inference for Public Health Research I* (3 credits)

CORE TOTAL: 14 CREDITS

SPH Course Descriptions

PUBH 6866   | Principles of Clinical Trials (3 credits)  PUBH 6869   | Principles of  Biostatistical Consulting (1 credit) PUBH 6879 | Propensity Score Methods for Causal Inference in Observational Studies (3 credits) PUBH 6887   | Applied Longitudinal Data Analysis for Public Health Research (3 credits) PUBH 8871   | Statistical Inference for Public Health Research II* (3 credits) PUBH 8875 | Linear Models in Biostatistics* (3 credits)  PUBH 8877   | Generalized Linear Models in Biostatistics* (3 credits) PUBH 8878 | Statistical Genetics (3 credits) PUBH 8879 | An Introduction to Causal Inference for Public Health Research (3 credits) PUBH 8880 | Statistical Computing for Public Health Research (3 credits) STAT 6227   | Survival Analysis (3 credits)

BIOSTATISTICS CONCENTRATION-SPECIFIC TOTAL: 28 CREDITS

* Courses are basis of comprehensive exam for the Biostatistics concentration.

PUBH 6854 | Applied Computing in Health Data Science (3 credits) PUBH 6859 | High Performance Cloud Computing (3 credits)  PUBH 6861   |  Public Health Genomics (3 credits)  PUBH 68 84   |  Bioinformatics  Algorithms and Data Structure (3 credits)  PUBH 8885   | Computational Biology (3 credits) 

BIOINFORMATICS CONCENTRATION-SPECIFIC TOTAL: 15 CREDITS

• Applied Biostatistics Concentration: 12 credits minimum
• Bioinformatics Concentration:  18 credits minimum

Both concentrations:  elective selections must include at least*:

• 3 Credits in Biostatistics 
• 3 Credits in Bioinformatics
• 3 Credits in a Cognate Area

All students are expected to work with their Advisor in the selection of their Elective coursework.

*Pre-approved elective courses are shown in the program guide for each category. 

 BIOSTATISTICS ELECTIVES MINIMUM: 12 CREDITS BIOINFORMATICS ELECTIVES MINIMUM: 18 CREDITS

PRACTICUM/TEACHING RESEARCH GTAP**   |   GradTeachingAsst Certification (This includes UNIV 0250 - Graduate Assistant Certification Course (1 credit) (both concentrations) (0; 1 credit) PUBH 8283 | Doctoral Biostatistics Consulting Practicum (Biostatistics concentration only) (2 credits) PUBH 8413 |  Research Leadership (both concentrations (1 credit)

** This is a requirement for TAs .

DISSERTATION RESEARCH PUBH 8999 |  Dissertation Research (varies by concentration - 12 minimum credits)

BIOSTATISTICS PRACTICUM/RESEARCH: 12-15 CREDITS BIOINFORMATICS PRACTICUM/RESEARCH: 12-24 CREDITS

Professional Enhancement

Students in degree programs must participate in eight hours of Professional Enhancement. These activities may be Public Health-related lectures, seminars, or symposia related to your field of study.

Professional Enhancement activities supplement the rigorous academic curriculum of the SPH degree programs and help prepare students to participate actively in the professional community. You can learn more about opportunities for Professional Enhancement via the Milken Institute School of Public Health Listserv, through departmental communications, or by speaking with your advisor.

Students must submit a completed  Professional Enhancement Form  to the student records department  [email protected] .

Collaborative Institutional Training Initiative (CITI) Training

All students are required to complete the Basic  CITI training module  in Social and Behavioral Research prior to beginning the practicum.  This online training module for Social and Behavioral Researchers will help new students demonstrate and maintain sufficient knowledge of the ethical principles and regulatory requirements for protecting human subjects - key for any public health research.

Academic Integrity Quiz

All Milken Institute School of Public Health students are required to review the University’s Code of Academic Integrity and complete the GW Academic Integrity Activity.  This activity must be completed within 2 weeks of matriculation. Information on GWSPH Academic Integrity requirements can be found  here.

Past Program Guides

Students in the PhD in Health and Biomedical Data Science program should refer to the guide from the year in which they matriculated into the program. For the current program guide, click the "PROGRAM GUIDE" button on the right-hand side of the page.

Program Guide 2023-2024 Program Guide 2022-2023 Program Guide 2021-2022

Lizhao (Agnes) Ge Email:   [email protected] Start year: 2021

Lizhao was born and raised in Zhejiang, China. She came to the United States for undergraduate studies at the University of Iowa, where she obtained a BS in Mathematics and a BBA in Finance, and a minor in Music. She earned a Master of Applied Statistics from the Pennsylvania State University and worked there as a Statistical Consultant after graduation. She joined the Antibacterial Resistance Leadership Group (ARLG) at the George Washington University Biostatistics Center as a biostatistician in 2020 and started her PhD journey in the Health and Biomedical Data Science (Applied Biostatistics track) in 2021. Her research interests are clinical trial designs, and application of the Desirability of Outcome Ranking (DOOR) in biomedical studies.

Yijie He Email:   [email protected] Start year: 2021

Yijie was born in China. Before coming to the George Washington University, he received a BS degree in Bioengineering from University of California San Diego and an MS degree in Biostatistics from Duke University. He is currently a PhD student in Health and Biomedical Data Science, in the Applied Biostatistics track, and he also works at the George Washington University Biostatistics Center as a biostatistician. His current research interests include clinical trials, high-dimensional data, and data science.

Shiyu Shu Email:  [email protected] Start year: 2021

Shiyu (Richard) was born and raised in Dalian, China, and has been studying in the United States for the last 7 years. He obtained a BA in Mathematics and in Economics from Vassar College, during which he spent one semester as an exchange student at St Edmund Hall, Oxford University. He then received a Master of Statistical Practice from Carnegie Mellon University, and worked as a data analyst for a healthcare organization in rural Arizona during the peak of the COVID pandemic. The work experience motivated him to pursue a career in public health, and to continue his PhD studies in the Health and Biomedical Data Science program at GWU. He is currently a biostatistician working in the Diabetes Prevention Program team (DPP) at the Biostatistics Center, under the supervision of Dr. Marinella Temprosa. His current research interests include machine learning/data science, genomics data and survival analysis.

Shanshan Zhang Email:  [email protected] Start year: 2021

Shanshan was born and raised in China. She earned a Bachelor of Medicine and a Master of Science in Cell Biology from China Medical University. When she came to the United States in 2018, she transferred her interests to public health, since a doctor can save individuals, whereas a public health expert can save lives on a population level. She obtained a second graduate degree, an MS in Biostatistics from the George Washington University. Shanshan hopes that she can make contributions to the field of public health, especially in designing and conducting clinical trials during the PhD program, and can work as an outstanding biostatistician in the future.

Recent Publications:

Qiongfang Wu, Leizhen Xia, Lifeng Tian, Shanshan Zhang, Jialyu Huang. Hormonal replacement treatment for frozen-thawed embryo transfer with or without GnRH agonist pretreatment: a retrospective cohort study stratified by times of embryo implantation failures. Accepted by Frontiers in Endocrinology. 5 January 2022

Shanshan Zhang. Biostatistics in Clinical Decision Making: What can We Get from a 2× 2 Contingency Table. E3S Web of Conferences (Vol. 233). EDP Sciences. December 2020

Qiqiang Guo, Shanshan Wang, Shanshan Zhang, Hongde Xu, Xiaoman Li, et al. ATM‐CHK 2‐Beclin 1 Axis Promotes Autophagy to Maintain ROS Homeostasis Under Oxidative Stress. The EMBO Journal, 39(10), e103111. 18 March 2020

PhD in Bioinformatics Students

Mahdi Baghbanzadeh Email:  [email protected] ;  [email protected] Start year: 2021

Mahdi Baghbanzadeh is a Ph.D. student in the health and biomedical data science program at the Milken Institute School of Public Health at the George Washington University. Mahdi received his MS in Mathematical Statistics from Shiraz University in 2012, and his BS in Statistics from Shahid Beheshti University in 2010.  Before joining GWU, he had the experience of 7 years performing in an analytical role ranging from data analyst to senior data scientist in multiple companies. His research interests are applying machine learning algorithms in analyzing omics data, developing tools for studying the genotype-phenotype association studies, and the effects of different medications on a certain disease.

Publications:

Baghbanzadeh, Mostafa; Simeone, F. C.; Bowers, C. M.; Liao, K.-C.; Thuo, M. M.;  Baghbanzadeh, Mahdi ; Miller, M.; Carmichael, T. B.; Whitesides, G. M.*  “Odd-even effects in charge transport across n-alkanethiolate-based SAMs”   Journal of American Chemical Society ,  2014 ,  136 , 16919–16925.

Mahdi Baghbanzadeh , Dewesh Kumar, Sare I. Yavasoglu, Sydney Manning, Ahmad Ali Hanafi-Bojd, Hassan Ghasemzadeh, Ifthekar Sikder, Dilip Kumar, Nisha Murmu, Ubydul Haque*  “Malaria Epidemics in India: Role of Climatic Condition and Control Measures”   Science of the Total Environment ,  2020 ,  712 , 136368.

Peeri, Noah C., Nistha Shrestha, Md Siddikur Rahman, Rafdzah Zaki, Zhengqi Tan, Saana Bibi,  Mahdi Baghbanzadeh , Nasrin Aghamohammadi, Wenyi Zhang, and Ubydul Haque.  " The SARS, MERS and novel coronavirus (COVID-19) epidemics, the newest and biggest global health threats: what lessons have we learned? "   International Journal of Epidemiology ,  2020 ,  49 , 717-726.

Md Siddikur Rahman, Ajlina Karamehic-Muratovic,  Mahdi Baghbanzadeh , Miftahuzzannat Amrin, Sumaira Zafar, Nadia Nahrin Rahman, Sharifa Umma Shirina, Ubydul Haque,  “Climate change and dengue fever knowledge, attitudes and practices in Bangladesh: a social media–based cross-sectional survey” ,  Transactions of The Royal Society of Tropical Medicine and Hygiene ,  2021 ,  115 , 85-93.

Nistha Shrestha, Muhammad Yousaf Shad, Osman Ulvi, Modasser Hossain Khan, Ajlina Karamehic-Muratovic, Uyen-Sa D.T. Nguyen,  Mahdi Baghbanzadeh , Robert Wardrup, Nasrin Aghamohammadi, Diana Cervantes, Kh. Md Nahiduzzaman, Rafdzah Ahmed Zaki, Ubydul Haque,  “ The impact of COVID-19 on globalization” ,  One Health ,  2020 , 100180.

Osman Ulvi, Ajlina Karamehic-Muratovic,  Mahdi Baghbanzadeh , Ateka Bashir, Jacob Smith, Ubydul Haque, “ Social Media Use and Mental Health: A Global Analysis ”,  Epidemiologia,   2022 , 3 (1), 11-25 .

Ranojoy Chatterjee Email:  [email protected] ;  [email protected] Start year: 2021

Ranojoy is originally from Kolkata, India. He got his B.Tech in Computer Science from WBUT and has an MS in Computer Science from Kansas State University, specializing in recommendation systems using a multi-armed bandit approach. After graduation he worked at Bellwethr, Inc developing a retention engine which was later patented by the company. After his brief stint in industry, he worked as a research specialist in Rahlab to develop machine learning tools for analyzing Covid-19 data. His current research interests are graph neural networks, single cell data and prediction systems in biomedical data science.  

Amritphale, A., Chatterjee, R., Chatterjee, S.  et al.  Predictors of 30-Day Unplanned Readmission After Carotid Artery Stenting Using Artificial Intelligence.  Adv Ther   38,  2954–2972 (2021).  https://doi.org/10.1007/s12325-021-01709-7

Chow JH, Rahnavard A, Gomberg-Maitland M, Chatterjee R, et al. Association of Early Aspirin Use With In-Hospital Mortality in Patients With Moderate COVID-19.  JAMA Netw Open.  2022;5(3):e223890. doi:10.1001/jamanetworkopen.2022.3890

Clark Gaylord Email:  [email protected] Start year: 2021

After receiving M.S. degrees in Mathematics and Statistics from the University of Virginia and Virginia Tech, respectively, Clark has had a career in information technology, network security, and research computing. Over the last 20 years, Clark has led the design and operation of many research computing and big data research systems, and is a consulting statistician on several research projects. While at Virginia Tech, Clark taught several courses in Statistics, Data Science, and Networking. A PhD candidate in GW's Health and Biomedical Data Science, Bioinformatics Track, Clark is also Director of Research Technology Services in GW IT.

CAAREN:  https://www.caaren.org/clark-gaylord GW High Performance Computing:  https://www.hpc.arc.gwu.edu/

Erika Hubbard Email:  [email protected] Start year: 2021

Erika was born and raised in Fairfax County, Virginia (NOVA) and earned her BSc in Biomedical Engineering with minor concentrations in Applied Mathematics and Engineering Business from the University of Virginia. Upon graduation she went on to intern and work for AMPEL BioSolutions, LLC in Charlottesville, VA, researching autoimmune and inflammatory diseases, primarily systemic lupus erythematosus (SLE). As a dual member of the systems biology and bioinformatics teams at AMPEL she developed an interest in leveraging genomics data to gain insights into mechanisms of autoimmune disease pathogenesis. She continues to work with AMPEL to study lupus and translate findings into novel clinical tools to further precision medicine. 

Hubbard EL, Pisetsky DS, Lipsky PE. Anti-RNP antibodies are associated with the interferon gene signature but not decreased complement levels in SLE. Ann Rheum Dis [Epub ahead of print: 3 Feb 2022]. doi:  https://doi.org/10.1136/annrheumdis-2021-221662

Hubbard EL, Grammer AC, Lipsky PE. Transcriptomics data: pointing the way to subclassification and personalized medicine in systemic lupus erythematosus. Curr Opin Rheumatol [Internet]. 2021 Nov 1;33(6):579-85. doi:  https://doi.org/10.1097/bor.0000000000000833   

Daamen AR, Bachali P, Owen KA, Kingsmore KM, Hubbard EL, Labonte AC, et al. Comprehensive transcriptomic analysis of COVID-19 blood, lung, and airway. Sci Rep [Internet]. 2021 Mar 29;11(1):7052. doi:  https://doi.org/10.1038/s41598-021-86002-x

Hubbard EL, Catalina MD, Heuer S, Bachali P, Geraci NS, et al. Analysis of gene expression from systemic lupus erythematosus synovium reveals myeloid cell-driven pathogenesis of lupus arthritis. Sci Rep [Internet]. 2020 Oct 15;10(1):17361. doi:  https://doi.org/10.1038/s41598-020-74391-4

Xinyang Zhang Email:  [email protected] ;  [email protected] Start year: 2021

Xinyang was born and raised in Jiangsu, China. Before coming to George Washington University, she obtained her MS in Data Informatics at the University of Southern California, Los Angeles. For now, she started her Ph.D. journey in Health and biomedical data science (Applied Bioinformatics track) and works for the Computational Biology Institute (CBI) as a Research Assistant. Her research interest focuses on microbiome analysis, omics data for the COVID-19, and reference-grade pathogen sequences database construction.  

• Whiting School of Engineering
• Johns Hopkins School of Medicine

• Johns Hopkins Biomedical Engineering
• Research Areas

Biomedical Data Science

Biomedical Data Science involves the analysis of large-scale biomedical datasets to understand how living systems function. Our academic and research programs in Biomedical Data Science center on developing new data analysis technologies in order to understand disease mechanisms and provide improved health care at lower costs.

Education in Biomedical Data Science

Our curriculum trains students to extract knowledge from biomedical datasets of all sizes in order to understand and solve health-related problems. Students collaborate with faculty throughout the schools of Medicine and Engineering to develop novel cloud-based technologies and data analysis methods that will improve our ability to diagnose and treat diseases.

Research in Biomedical Data Science

Our students and faculty are pioneering new methods to analyze large-scale biomedical datasets, shedding new light on the function of living systems. Key research areas include:

Computational Science

Machine learning and data science, biomedical data, science as a service, biomedical clouds, core faculty.

Read the Johns Hopkins University privacy statement here .

BIOM-PHD - Biomedical Informatics (PhD)

Program overview.

OUT OF DATE - Refer to program for AY23-24 forward.

The Biomedical Data Science Program is interdisciplinary and offers instruction and research opportunities leading to MS and PhD degrees in Biomedical Data Science. All students must complete the core curriculum requirements and additional coursework to fulfill degree requirements and pursue their technical interests and goals as specified for each degree program.

The program can provide flexibility and complement Stanford’s other applied medical research opportunities. Special arrangements may be made for those with unusual needs or those simultaneously enrolled in other degree programs within the university. Similarly, students with prior relevant training may have the curriculum adjusted to eliminate requirements met as part of prior training.

The GRE is not required for admission.

Individuals wishing to prepare themselves for careers as independent researchers in biomedical data science, with applications experience in bioinformatics, clinical informatics, or imaging informatics, should apply for admission to the doctoral program. Graduate Degrees summarizes the university’s basic requirements (residence, dissertation, examination, and so on) for a doctorate.

PhD in Biomedical Data Science

The current explosion of biomedical data, including Electronic Health Records, biomedical imaging, and genomics/proteomics/metabolomics, provide an opportunity to improve understanding of the mechanisms of disease and ultimately improve  health care. But fully harnessing the power of high-dimensional, heterogeneous data requires a new blend of skills including programming, data management, data analysis and machine learning.

Program Curriculum

The program blends the best of statistics, computer sciences, biostatistics and biomedical informatics. It gives students the training they need to make sense of large-scale biomedical data and to be scientific leaders in the team science that invariably accompanies such data. Unique features of the program include interdisciplinary training and research rotations mentored by program faculty.

Please refer the Graduate Guide for the most current program requirements

PhD Program Plan , a useful guide to help map out course scheduling

Core Topics

Three year-long course sequences (18 credits) will be selected from a set of core topics, including one biostatistics sequence (topics 1-2), and one computer science/informatics sequence (topics 3-6). The third sequence can be selected from any of the listed topics (topics 1-11).

Course requirements include additional credits of electives, which may be taken from the core topics (see above), or other graduate-level courses in biostatistics, computer science, or biomedical sciences. A students’ particular choices will be guided by and subject to the approval of their Academic Advisor.

Biology Training and Breadth

Students will generally specialize in some field of biomedical application (e.g., clinical medicine, genomics, or neuroscience). Thus, their training must include coursework in the biological sciences. In addition, students will need to meet the formal breadth requirements set forth by the Graduate School. These objectives will be achieved by selection of a minor (formal external or distributed), with the further requirement that this minor include at least six credits of biology courses (See list of potential Biology courses Biology Courses  to select from).

Research Ethics Requirement

All students will take a 1-credit Research Ethics course, preferably in Y1 and typically BMI 738 Ethics in Data Science, offered in the spring semester

Additional Requirements

In addition, to contribute to the students’ breadth of knowledge, to build cohesiveness among the students, to train the students in the critical evaluation of the biostatistical, computational, and scientific literature, and to build their professional skills, all students will participate in two year long seminar style courses in Y2 and Y3:

• Biodata Science Scholarly Literature (BMI 881-882, 4 credits): including readings, discussion, and presentations on a selected set of primary journal articles from the biostatistics, biomedical informatics, computer science, and biomedical literature. (taken in Y2)
• Biodata Science Profession Skills (BMI 883-884, 2 credits): covering such topics as giving scientific presentations, writing research grants, the publication process (writing scientific articles, reviewing such articles, and responding to reviewers), applying for jobs, employment opportunities in academics and industry, and working with scientific collaborators as part of interdisciplinary teams. (taken in Y3)

Research Rotations

Students will carryout three semester- or summer-long research rotations in the first year (fall-spring-summer terms) concerning a substantive problem in biomedical data science, advised by a Program Faculty member, in collaboration with an additional UW faculty member from the biological, biomedical, or population health sciences. The aim is for the students to begin to learn the craft of data science research, to expand their understanding of specific biomedical application areas, to gain a deeper exposure to a broad set of problems in biomedical data science, and to ultimately identify an appropriate advisor and to begin to identify a dissertation research topic. Please see the BDS_PhD_Rotation Policy  for details.

Prelim Exam

The PhD training includes an  Oral Preliminary Exam , ideally taken in the student’s third year, on a topic selected with the approval of the student’s advisor. A student is expected to have completed nearly all other course requirements by this time. Please see BDS PhD Prelim Exam Guidelines for details.

Dissertation

In addition, and in accordance with requirements set by the Graduate School at UW- Madison, students must pass a Final Oral Exam (i.e., a Dissertation Defense), following completion of their dissertation research. The primary requirement for the PhD degree is the completion of a significant body of original research and the presentation of this research in a dissertation. The research is carried out under the guidance of a member or members of the Program Faculty. The candidate must defend the dissertation in a Final Oral Exam. The rules for the composition of the Final Oral Exam committee are the same as for the Oral Preliminary Exam, except that, following Graduate School policy, the committee must have at least four members and at least one must be from outside the program.

The highly collaborative faculty in Biostatistics and Medical Informatics offer training to undergraduate, graduate and other students through a variety of flexible mechanisms aimed at attracting the best and brightest students into our innovative fields of quantitative methodology for biomedical investigation, and at training biomedical scientists in the optimal application of our methods to their chosen field of study.

For undergraduates, our summer programs offer the opportunity to gain a foundation in biostatistics or biomedical informatics and to explore research in these areas. Other undergraduates pursue honors work or research rotations with our faculty, either informally or as part of their major.

For students wishing to pursue Master’s or PhD level graduate work in quantitative methodologies in biomedical science, we offer both a  MS Degree Program in Biomedical Data Science   and a Doctoral Degree Program in Biomedical Data Science .

Other opportunities to train with our faculty are available through the graduate programs in Statistics ( Biostatistics Degree Option ) and in Computer Sciences, but also through graduate programs in Clinical Investigation, Genetics, and Population Health Sciences.

To learn more about our graduate and summer programs please contact:

Shelley Maxted (608) 263-4825 [email protected]

Beth Bierman (608) 265-8649 [email protected]

• PhD in Biomedical Data Science More
• MS in Biomedical Data Science More
• Biostatistics Degree Option for Statistics Students More
• BDS Graduate Student Handbook More
• Prospective Students More
• Summer Research Opportunity Program More
• BMI Course list More

Division of Biology & Biomedical Sciences

• Biomedical Informatics & Data Science

The Biomedical Informatics and Data Science (BIDS) PhD program will provide opportunities for graduate students with diverse backgrounds to gain expertise in the field of biomedical informatics and data science, to train as future biomedical researchers and industry leaders in biomedical informatics and data science core competencies, and to engage in scholarly activities under the guidance of experienced informatics faculty. Beyond an educational program, the mission of BIDS is to support biomedical informatics and data science practice through applied research in real-world settings.

The objectives of the BIDS program are to: 

• Expand students’ knowledge in data science theories and applications in biomedical informatics;
• Provide training and hands-on research and industry experience in biomedical informatics and data science;
• Assist students in enhancing and applying their skills in translational science, real-world problem solving, and dissemination of knowledge;
• Encourage students to pursue careers in academia and/or industry through exposure to professional role models or mentors; and
• Develop and improve students’ skills in interdisciplinary teamwork and communication. 

The BIDS program spans the fields of Data Science and Biomedical Informatics, which we define as follows:

• Biomedical Data Science  is the study of the generalizable extraction of knowledge from biomedical data; and
• Biomedical Informatics  is the science of information applied to or studied in the context of biomedicine. 

(Payne PR, Bernstam EV, Starren JB. Biomedical informatics meets data science: current state and future directions for interaction. Jamia Open. 2018 Oct;1(2):136-41.)

Throughout their training, learners will interact with patients, providers, and the healthcare data ecosystem in addition to working at the bench. The BIDS program promotes collaboration and partnerships in solving problems in clinical practice, individual and population health, and biomedical research, through the optimal use of information. Due to its interdisciplinary nature, the BIDS program offers learners great flexibility when creating individual education plans based on interests, experience, and future goals. 

Core research tracks and subfields  

Biostatistics and data science.

The goal of the Biostatistics and Data Science track is to train independent and innovative researchers who will contribute to the development and application of cutting-edge statistical and data science methodologies in health science disciplines. The track provides a balance of theory, methods, and applications of biostatistics and data science that are central to modern interdisciplinary research. Under the supervision of advisors, PhD students participate in the design of clinical studies and are involved in the analysis, inference, and interpretation of these studies. 

Biomedical Informatics

Through the Biomedical Informatics track, students will have training and research opportunities in the five subdisciplines of biomedical informatics, as defined by the American Medical Informatics Association (AMIA) :

• Applied Clinical Informatics (ACI)   focuses on supporting and elevating clinical informatics practice through research translation, which is essential to fill the gaps between practice and research. ACI involves the application of innovative measurement and informatics approaches to inform and improve clinical practice.
• Consumer Health Informatics (CHI)   involves views from consumers or patients and focuses on information structures and processes to empower individual’s management of their own health. CHI investigates consumers’ needs and integrates consumers’ preferences into health information systems.
• Clinical Research Informatics (CRI) involves the use of informatics in the discovery and management of new knowledge relating to health and disease. Specifically, CRI manages information related to clinical trials as well as secondary use of clinical data.
• Translational Bioinformatics (TBI)  is the development of storage, analytic, and interpretive methods to optimize the transformation of increasingly voluminous biomedical data into proactive, predictive, preventative, and participatory health.
• Population Health Informatics (PopHI) integrates aspects of public health, clinical informatics, and health care delivery with the target of improving health care system effectiveness and the well-being of communities and populations.

There is a wide range of research interests between faculty affiliated with the BIDS program, some of which include:

• Human genetic analysis
• Telemedicine & mobile health
• Cognitive computing & machine learning
• Systems biology
• Epidemiology & public health
• Neuroimaging
• Computational mass spectrometry & proteomics
• Survival Analysis
• Clinical decision support & workflow

Students in the BIDS program will typically take six (6) to seven (7) courses during their first year. Students will also participate in three laboratory rotations over the fall and spring semesters of Year 1 prior to selecting a thesis lab. Students are expected to complete the following coursework during their entire graduate education:

DBBS required courses

Graduate Research Fundamentals Ethics and Research Science – typically taken in Year 2

Program required courses

Introduction to Biomedical Informatics I Introduction to Biomedical Informatics II Introduction to Biomedical Data Science I Introduction to Biomedical Data Science II

Three (3) semesters of advanced electives

Four (4) semesters of bids journal club and seminar series, qualifying exam.

In the spring and/or summer semesters of Year 2, students must pass a Qualifying Exam (QE). Following a successful QE defense, students will identify and finalize their committee and complete their thesis proposal by December 31st of Year 3.

Thesis committee, proposal, and defense

In the summer and/or fall semesters of Year 2 after rotations are completed, students will select a thesis advisor and begin working in their thesis labs. Students will then select a thesis committee and complete their thesis proposal. Students will complete their thesis research, defense, and graduation over the rest of their graduate career. Most students graduate within five (5) to six (6) years of beginning their program.

Graduate Program Administrator:  Stacy Kiel​

Faculty Director:  Philip Payne, PhD, FACMI Adam Wilcox, PhD

• Biochemistry, Biophysics, & Structural Biology
• Cancer Biology
• Computational & Systems Biology
• Developmental, Regenerative, & Stem Cell Biology
• Ecology & Evolutionary Biology
• Molecular Cell Biology
• Molecular Genetics & Genomics
• Molecular Microbiology & Microbial Pathogenesis
• Neurosciences
• Plant & Microbial Biosciences

PhD in Biomedical Informatics

The PhD program in Biomedical Informatics is part of the   Coordinated Doctoral Programs in Biomedical Sciences . Students are trained to employ a scientific approach to information in health care and biomedicine. Students may only enroll full-time, as required by the Graduate School of Arts and Sciences (GSAS). The first two years are generally devoted to coursework and research. Subsequent years focus on independent research that culminates in a dissertation. 

Our PhD students come from top universities in the country and around the world. The group is dynamic and engaged, breaking new ground in informatics research as evidenced by their strong publication records. Our students are highly collaborative, frequently assisting on each other’s projects, sharing ideas, and supporting each other.

The program consists of core courses that are required of every student and provide a foundation in general biomedical informatics methods, techniques and theories, while electives enable students to apply these methods to one or more areas of specialization in bioinformatics, translational, clinical informatics, clinical research informatics, or public health informatics. In addition, students conduct research, assist in teaching (if PhD or postdoctoral trainees), and attend colloquia.

Degree Requirements

​ Courses : A minimum of 60 points of Columbia University graduate (4000 level or above) coursework, 6 residence units, consisting of:

• Research each term (BINF G6001, BINF G9001)
• 5 core classes
• 2 domain (specialization) courses
• 3 educational objectives courses
• 1 ethics course (spring term of first year)
• serving as a TA for 2 classes (or 1 class for MD-PhD students)
• 1 research seminar each term

Students must complete a minimum of 60 points of Columbia University instruction at the 4000 level or higher, address any admission deficiencies, and complete DBMI degree requirements. In years three and above, research is the primary focus of the student’s degree program, and the number of hours spent on research increases with each year in the program. Students enroll in BINF G6001 fall and spring terms as follows: a) 6 points each term year one, 9 points each term year two, 12 points each term years three and above. Students enroll in BINF G9001 in lieu of BINF G6001 the term following successful completion of the Oral II/Depth Exam. In their final term of enrollment, students will also register for BINF G9999 Doctoral Dissertation for 0 points. Students should pursue five goals when conducting research, and the grade earned in the required research classes (BINF G6001, BINF G9001) will reflect how well the student has achieved these goals: 1) understand the nature of informatics research 2) master intellectual and technical skills necessary for research 3) read and apply the scientific literature, 4) develop skill in scientific writing 5) demonstrate a responsible working attitude.

Ethics:  PhD students are required to enroll in CMBS G4010 Responsible Conduct of Research and Related Policy Issues in their second term in the program.

Teaching Assistantship: Students are required to serve as teaching assistants (TAs) for two courses in the department. In order to earn credit for TA responsibilities, students need to register for two points of BINF G8010 MPhil Teaching Experience each semester in which they serve as a TA. Students and faculty are solicited in spring term for their top 3 preferences. The Training Committee assigns TAs based on faculty and student preferences and departmental needs. The assignments will be communicated to students and faculty by the Graduate Program Manager. PhD students are required to TA two courses. Two-year postdoctoral research fellows TA one course; three-year postdoctoral research fellows TA two courses. MD-PhD students TA one course.

Seminar: PhD students are required to enroll in the weekly DBMI seminar. PhD students in the bio track are required to enroll in the DBMI seminar in their first year in the program, and may substitute the Systems Biology seminar in year 2 and beyond.

Residence Units: PhD students accrue 6 residence units for the degree. They are enrolled in the appropriate residence unit category by the GSAS Office of Graduate Affairs every fall and spring term.

Milestones: There are four milestones for PhD students:

• Breadth Exam
• Dissertation Proposal
• Dissertation Defense

Academic progress is tracked each semester by the students and their academic advisors (see Forms page for semester forms)

Research Rotations With the exception of MD-PhD students whose research rotation occurs between years 1 and 2 of medical school, all PhD students rotate in two different research labs their first year. Research rotations begin by the end of the change of program (add/drop) period of each term. The second research rotation begins the first day of classes of spring term. Projects should be completed prior to the start of the subsequent term. The permanent research advisor is chosen by May 15 of the first year. The Training Committee grants final approval of research rotations and permanent research advisor selections. Work with the permanent research advisor commences the next business day following the last day of final exams. A third summer rotation is possible with the Committee’s permission.

For first year students rotating with different research advisors, the Fall term dates for the first research rotation of BINF G6001 are the second week of September through the MLK, Jr. Holiday. For Spring term, the dates are the day following the MLK, Jr. holiday (the first day of classes) until the last date of final examinations ( see the online University academic calendar ) .  Work with the permanent research advisor commences the next business day following the last day of final exams.

Rotation Research Advisor Prior to the start of the Fall and Spring semesters, first-year PhD students should contact the faculty with whom they are considering doing a rotation to request an appointment. Selection of a research rotation advisor must be official by the end of the drop/add period of each semester. Students should discuss expectations for the rotation as well as a finite project to be completed by the end of the term of the rotation with the research advisor. This prevents projects continuing into the next semester which impacts the output of the new research rotation. The project should not depend on applying for a new IRB as this will delay the research into the subsequent semester, which is ill-advised. The Training Committee grants final approval of research advisors.

Register for research credit and a letter grade (6 points in fall and spring of first year, 9 points in fall and spring of second year, 12 points in fall and spring all subsequent years).

Publications PhD students and postdoctoral fellows are expected to make submissions to publications and conferences each year. The frequency and appropriateness of these submissions are decided by the research advisor. No student or fellow may submit work to any publication or conference without the expressed prior approval of their research advisor. Prior to submission, the research advisor must review final versions of all papers and abstracts submitted to journals, conferences, books or other publications. This policy applies to all publications, regardless of authorship, that deal with work that has been done at DBMI, Columbia University, or any affiliated institution(s).

Funding More information about funding sources and fellowships is available in the Student Funding page .​

We have 96 biomedical data science PhD Projects, Programmes & Scholarships

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biomedical data science PhD Projects, Programmes & Scholarships

Integration of transactional and ecological momentary assessment data for cancer risk factor prediction, phd research project.

PhD Research Projects are advertised opportunities to examine a pre-defined topic or answer a stated research question. Some projects may also provide scope for you to propose your own ideas and approaches.

Self-Funded PhD Students Only

This project does not have funding attached. You will need to have your own means of paying fees and living costs and / or seek separate funding from student finance, charities or trusts.

Sustainable Approaches to Biomedical Science: Responsible and Reproducible Research (Centre for Doctoral Training)

Funded phd programme (uk students only).

Some or all of the PhD opportunities in this programme have funding attached. It is only available to UK citizens or those who have been resident in the UK for a period of 3 years or more. Some projects, which are funded by charities or by the universities themselves may have more stringent restrictions.

4 Year PhD Programme

4 Year PhD Programmes are extended PhD opportunities that involve more training and preparation. You will usually complete taught courses in your first year (sometimes equivalent to a Masters in your subject) before choosing and proposing your research project. You will then research and submit your thesis in the normal way.

Using Big Data Approaches to Enhance Fluid Stewardship in Intensive Care Unit

Funded phd project (european/uk students only).

This project has funding attached for UK and EU students, though the amount may depend on your nationality. Non-EU students may still be able to apply for the project provided they can find separate funding. You should check the project and department details for more information.

Fully funded Master by Research in Biology studentships: Analysing datasets in disease models of obesity

Funded phd project (uk students only).

This research project has funding attached. It is only available to UK citizens or those who have been resident in the UK for a period of 3 years or more. Some projects, which are funded by charities or by the universities themselves may have more stringent restrictions.

In silico modelling of endocannabinoid evolution

Generative ai in energy forecasting, research assistent in digital materials science (m/f/d) (tv‐l eg13, 100%, clausthal‐zellerfeld, germany), funded phd project (students worldwide).

This project has funding attached, subject to eligibility criteria. Applications for the project are welcome from all suitably qualified candidates, but its funding may be restricted to a limited set of nationalities. You should check the project and department details for more information.

Smart sensors and spectral techniques in human movement science

Remote sensing and advanced spectral analysis for coaching and rehabilitation, developing and applying semantic representations of scientific knowledge, computer vision with responsible artificial intelligence for human-related data, towards robotic 2d-ir spectroscopy for biomolecular fingerprinting of cancer, postgraduate research at the school of science, engineering and environment, university of salford, awaiting funding decision/possible external funding.

This programme is waiting to confirm funding from a university or external source. This may depend on attracting suitable students and applications are welcome. Please see the programme details for more information.

PhD Opportunities

PhD Opportunities highlight some of the specific PhD projects, programmes or other information currently available from a university.

Connected Field Lab – Integrating Data with Diagnostic Testing at the Front Line

Competition funded phd project (uk students only).

This research project is one of a number of projects at this institution. It is in competition for funding with one or more of these projects. Usually the project which receives the best applicant will be awarded the funding. The funding is only available to UK citizens or those who have been resident in the UK for a period of 3 years or more. Some projects, which are funded by charities or by the universities themselves may have more stringent restrictions.

Apply now for our fully funded PhD scholarships in Singapore at the Institute for Digital Molecular Analytics & Science (IDMxS)

Funded phd programme (students worldwide).

Some or all of the PhD opportunities in this programme have funding attached. Applications for this programme are welcome from suitably qualified candidates worldwide. Funding may only be available to a limited set of nationalities and you should read the full programme details for further information.

Singapore PhD Programme

A Singaporean PhD usually takes 3 years. Programmes are highly structured with taught courses and qualifying examinations in the first year, before students are confirmed as PhD candidates and allowed to produce a thesis. This is presented in a public seminar and then defended in a private oral examination. Programmes are often delivered in English.

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Ph.D. Biomedical Data Science Program

Accredited degree programs Offered by Meharry Medical College, one of the nation’s oldest and largest historically black academic health science centers.

Live classes Engage in group discussions with professors and peers.

100% online Hands-on learning from anywhere.

4 year minimum Courses taken during fall, spring and summer semesters.

$ 37,800 Per year. Tuition is reviewed each year. Additional fees will apply.

Lead with data and improve health care and public health through our Biomedical Data Science Ph.D. program

Through biomedical data science, you can learn to analyze biomedical data to better understand diseases and provide improved – and more affordable — health care. In Meharry’s Biomedical Data Science Ph.D. program you will learn to discover new knowledge from biomedical data sets. You will collaborate with faculty, and conduct independent research, to develop new technologies and novel data analysis methods.

Biomedical Data Science Ph.D. curriculum and resources

We know that pursuing a doctorate program is one of the biggest career decisions you will make. The Biomedical Data Science Ph.D. will prepare you to develop novel tools and leverage real-world, massive biomedical data to advance precision health, drug discovery and other areas of biomedical science.

• Biomedical data science courses include predictive modeling, computational structural biology, applied machine learning, mathematical and statistical theory, advanced biostatistics and epidemiology.
• Research topics of interest before beginning your dissertation.
• First-class resources: high-performance, supercomputer network and robust, diverse data resources.

Work with faculty who are impacting society

Our faculty are impacting society through meaningful research in areas such as:

• Computational and theoretical biology, quantum computing
• Atomic, molecular, and protein modeling, e.g. for drug efficacy
• Population health informatics
• Geospatial big data to support health care related application scenarios
• Close-range photogrammetry, computer vision and 3D printing for health care and epidemiology

Scholarships available!  

As an HBCU, Meharry is committed to increasing diversity in the data science profession.

Meharry is committed to increasing diversity in the   data science profession.

Students admitted to attend the M.S. Data Science, or the M.S. or Ph.D. Biomedical Data are eligible for scholarships of up to 50 percent of tuition.

Your path to a Biomedical Data Science Ph.D.

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Just complete the form and we can schedule a time to discuss your goals and our programs.

Start your application. We are still here to help you along the way.

Develop expertise in biomedical data science and pursue impactful research.

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Professional development services will help you take your next step after graduation.

Department of Biomedical Data Science

Ctbh and biomedical data science welcome dr. lynn fiellin.

The Center for Technology and Behavioral Health (CTBH) and the Department of Biomedical Data Science are pleased to welcome Lynn Fiellin, MD to our faculty November 1st!

Six Faculty Members Selected for the Ivy+ FAN Fellowship

The program supports emerging leaders who will foster inclusivity at their schools.

Saeed Hassanpour to lead new Dartmouth Center for Precision Health and Artificial Intelligence

Dartmouth has created a Center for Precision Health and Artificial Intelligence to spur interdisciplinary research that can better leverage—as well as more safely and ethically deploy—biomedical data in assessing and treating patients and improving their health care outcomes.

Open Positions

Tenure-track faculty position in biomedical data science, precision health & artificial intelligence.

Faculty Positions in Research Methods and Data Analytics in Digital Health as Applied to Behavioral Health

Postdoctoral Scholar in Biomedical Data Science at Dartmouth (Hoehn Lab)

Postdoctoral Scholar in Biomedical Data Science at Dartmouth (Zhao Lab)

Educational Programs

Qbs ms in health data science.

The QBS Masters of Science degree in Health Data Science provides training in data wrangling, exploratory data analysis, statistical modelling, machine learning, data visualization and communication. Graduates will have competencies in the management, analysis and interpretation of data from medicine, computational biology, pharma and genomics.

PhD Program

Students trained through the  PhD program  will be versed in bioinformatics, biostatistics and epidemiology. The program is interdepartmental with numerous collaborations between QBS members and those in other graduate programs at Dartmouth.

Welcome to Biomedical Data Science

Mission The mission of the department of Biomedical Data Science at Geisel School of Medicine is to advance scholarship and education for the quantitative and computational analysis of biomedical and health data and to unite and promote Biomedical Informatics and Biostatistics as essential disciplines for the mentoring and academic development of faculty and the training and education of the next generation of data scientists.

History The past decade in biomedical research has witnessed a tidal wave of “Big Data” generated from high throughput technologies, electronic medical records, and networked research resources. We are experiencing a revolution in our ability to measure and organize precise data on individuals relating to risk for disease development, its optimal management, and its outcomes.

As more quantitative evaluation of individual characteristics becomes possible, there is increased opportunity for larger studies that evaluate the combinations of factors that can predict disease and care. At the same time, the demands of handling volumes of increasingly detailed data the growth in sophistication in statistical and computational modeling, and the increased potential for drawing erroneous conclusions, if cause is not distinguished from association, create enormous challenges in study design, scientific data analysis and interpretation.

Data Science has emerged as a new interdisciplinary field to address these challenges. The automated management, retrieval and interpretation of extensive biological, medical and health information require that Data Science involves coordination and integration of the existing disciplines of Biomedical Informatics and Biostatistics. The Department of Biomedical Data Science brings together the breadth and depth of faculty expertise in computational and statistical methodologies.

Post-doctoral Scholar in Biomedical Data Science at Dartmouth (Hoehn Lab)

Faculty positions in research methods and data analytics in digital health as applied to behavioral health, post-doctoral scholar in biomedical data science at dartmouth (zhao lab), postdoctoral training program for quantitative population sciences in cancer at dartmouth, external fellowship opportunities.

The Data Incubator

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Stanford Online

Biomedical data science.

BIOMEDIN202

Stanford School of Medicine

This course introduces the data modalities and methods valuable to ask and answer probing and novel questions that advance biomedicine.

You will get exposure to a variety of current data types from imaging and omics to patient-centric and digital health generated data types. You will also be exposed to the core methodological concepts useful to analyze these data in isolation or in combination.

Specifically, in four separate modules taught by expert faculty in each area the basic principles of each module will be defined and explained.

• Module 1, Clinical Data and Systems, will explain the basics of Electronic Health Records, and how they operate in health care settings.
• Module 2, Image Data Health Science, will focus on an introduction to the main imaging modalities in medicine and how methodological analysis using machine vision can be used on large studies.
• Module 3 will focus on fusing different data streams such as clinical, imaging, molecular and other data modalities.
• Module 4 will focus on reproducibility, evaluation and ethical issues when deploying models based on biomedical data, with emphasis on translation to practice.

Emphasis will be placed questions, data and methods that advance health and medicine. Primary learning goals for this course include how to frame biomedical health questions, what data are needed to answer those questions, and what methodological constructs can be leveraged to probe and answer those questions.

This course is a newly designed course for the PhD program of the Department of Biomedical Data Science but open to all.

Prerequisites

• A conferred bachelor’s degree with an undergraduate GPA of 3.0 or better

What You Need To Get Started

Before enrolling in your first graduate course, you must complete an online application .

Don’t wait! While you can only enroll in courses during open enrollment periods, you can complete your online application at any time.

Once you have enrolled in a course, your application will be sent to the department for approval. You will receive an email notifying you of the department's decision after the enrollment period closes. You can also check your application status in your my stanford connection account at any time.

Learn more about the graduate application process .

How Much It Will Cost

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State of Affairs: Summer Updates for Biomedical Informatics & Data Science

Meet the department of biomedical informatics & data science.

To the BIDS community,

This is the first newsletter from the ‘Department’ of Biomedical Informatics & Data Science at Yale School of Medicine. This is a landmark not only for BIDS and Yale, but for our field in general. The first department of medical informatics in the USA was created in the 1960s (University of Utah in 1964), with a few launched in the following decades. With the transition of the section into a department, our specialty receives the needed recognition at Yale as we move ahead with many new initiatives, and celebrate Yale for bringing its world leading medical expertise and informatics into a center for innovation.

The process of developing a department is not always straightforward. While 16 months may look fast, we stood on the shoulders of those who have been practicing informatics at Yale for a long time: clinical informatics experts and many computational biologists were working in clinical and bioinformatics for many decades. We have recruited several new faculty to fill strategic gaps and we are starting a new phase where the boundaries of clinical and bioinformatics become fuzzier. We now have a department that can serve as a hub for physicians, informaticians and biomedical data scientists, a unit that make its own academic appointments and develop its own degree programs while connecting everyone together.

Please join me in congratulating everyone for this important milestone. Through this newsletter, take a moment to learn what we are doing at BIDS, and join us at the start of this incredible journey!

– Lucila Ohno-Machado, MD, MBA, PhD

Waldemar von Zedtwitz Professor of Medicine and Biomedical Informatics and Data Science

Deputy Dean for Biomedical Informatics

Chair, Department of Biomedical Informatics and Data Science

BIDS Summer Newsletter 2024

Edited by Akio Tamura-Ho and Shane Zhang

BIDS Bets on Global Talent

This summer, four students will travel 7,000 miles from Rwanda to Yale School of Medicine as part of a new international exchange program organized by the department of biomedical informatics and data science.

Hot off the Press: Natural Language Processing in Biomedicine

A new textbook on natural language processing, co-edited by Hua Xu, PhD, FACMI, assistant dean for biomedical informatics, offers students and researchers essential reading for biomedical machine learning techniques.

Blockchain Trainee Headed to Med School

After spending a year at the department of biomedical informatics and data science (BIDS), Roger Lacson is headed to medical school. He’ll do so with an unconventional background in computer science and software engineering—and a prestigious journal publication under his belt.

Welcome New Faculty, Fellows, Students and Staff (Summer 2024)

We are pleased to welcome new faculty, postdocs, staff, and students to our growing department.

Rodríguez Martínez Brings AI and Cancer Therapy Research to Yale

María Rodríguez Martínez, PhD, has been recruited as Associate Professor in Yale School of Medicine's new department of biomedical informatics and data science.

Cho's Lab Presents Privacy-Preserving Genomics at RECOMB

In this summer newsletter feature for the department of biomedical informatics and data science, we spotlight recent research from Hoon Cho, PhD, and his lab.

MarketScan Data Now Available for Yale Researchers

The Yale Biomedical Informatics and Computing Office (YBIC) is excited to announce that members of the Yale community can now access the Merative™ MarketScan® Research Databases.

Recognition

Taylor and Scottish Partners Receive £1 Million for Palliative Care Research

Andrew Taylor, MD, MHS joins collaborators at the University of St Andrews and other Scottish institutions to conduct research to improve unscheduled care for people in the last year of life.

Yale New Haven Health and Yale University celebrate Innovation Awards

BIDS faculty led two out of the five winning teams at the second YNHHS Innovation Awards Ceremony, winning $100,000 each. Mark Iscoe, MD, MHS, Andrew Taylor, MD, MHS, and Melissa Davis MD, MBA, led one winning project, while Rohan Khera, MD, MS and Evangelos Oikonomou, MD, DPhil led another.

Leeds Receives VA Research Award for AI, Housing Study

Ira Leeds, MD, FACS, FASCRS, recently received an Association of VA Surgeons (AVAS) Faculty Research Award to study the relationship between housing instability and surgical outcomes using AI methods.

Cheung Receives NIH Grant to Research Water Contaminants and Human Health

Kei-Hoi Cheung, PhD, has been awarded a grant by the National Institute of Environmental Health Sciences (NIEHS) to research environmental health data and drinking water contamination using AI methods.

Meeker Elected to Cosmos Governing Council

Daniella Meeker, PhD, is one of three researchers newly elected to the governing council of Cosmos, Epic's platform for electronic health records.

New Department of Biomedical Informatics & Data Science Established at Yale

Attaining department status will enable Biomedical Informatics & Data Science to build on the momentum it already has achieved, increasing its national profile and positioning YSM to lead AI in medicine.

Yale-EPFL AI Model for Medicine Featured in 2024 AI Index Report

Meditron, the medical LLM developed by Annie Hartley, MD, PhD, MPH, and her LiGHT lab, was featured in the 2024 AI Index Report.

Yale Research Team Awarded $4 Million Grant to Evaluate New Immunizations for Infant RSV

A multidisciplinary team led by principal investigator Carlos Oliveira, MD, has received a $4 million federal grant to study the effectiveness of a new vaccine and monoclonal antibody shot designed to prevent respiratory syncytial virus (RSV) in infants.

• Yale is the lead institution in the ‘All of Us’ Research Consortium , receiving a large grant from NIH.
• Rohan Khera, MD, MS , and his CarDS lab, presented groundbreaking clinical research at the 2024 American College of Cardiology Scientific Sessions.
• Hua Xu, PhD, FACMI , launches the Yale University LLM and NLP Interest Group . They meet every second Wednesday.
• Deborah Levy, MD, MPH , is first author of a new VA cohort study on gabapentin .
• Terika McCall, PhD, MPH, MBA , appeared as a guest on the For Your Informatics podcast , discussing breakthroughs and insights at the intersection of digital health and mental health.
• Ethel Nalule 's photography is on display in Sterling Hall of Medicine .
• Lee Schwamm, MD , was an expert witness to the House Energy & Commerce Subcommittee on Health, discussing the role and future of telehealth .
• Jeffrey Cohen, MD , co-authored a study finding a significant association between eczema (atopic dermatitis) and eating disorders. The researchers analyzed data from more than 250,000 electronic medical records of patients participating in the All of Us Research Program.
• Mark Gerstein, PhD , features in Yale News, for a new study of nearly 400 human brains that could help enable precision-medicine for neuropsychiatric disease.
• Yuan Lu, ScD, Yuntian Liu, MPH, Wade Schulz, MD , and others use real-world data to identify the most effective hypertension drugs for patients
• On Connecticut Public Radio, Melissa Davis, MD, MBA , discusses her research finding racial bias in ChatGPT’s radiology reports
• A team of BIDS faculty and students, including Andrew Taylor, MD, MHS, David Chartash, PhD, Qingyu Chen, PhD, Vimig Socrates, MS, Thomas Huang, Xuguang Ai, MS, and Soraya Fereydooni , earned 2nd place out 89 participants in the BioNLP ACL'24 Shared Task on Streamlining Discharge Documentation.
• Hang Zhou, PhD, and others use VA health data to complete the first well-powered, genome-wide association study of epiretinal membrane, a common retinal disorder.

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Prerequisites and Requirements

Before applying to the Ph.D. Program at Mayo Clinic Graduate School of Biomedical Sciences, review our full list of prerequisite information and complete admission requirements. The admissions committee reviews all completed applications through a holistic review process to select candidates for interviews.

Prerequisites

Candidates for the Ph.D. Program must meet the following eligibility requirements:

• Completion of a bachelor's degree, preferably in the biological or physical sciences, from an accredited institution. 
• A minimum cumulative undergraduate GPA of 3.0 on a 4.0 scale. 
• Degree conferral before the program begins (program begins in July).

Suggested undergraduate coursework:

• Applicants to our Ph.D. program are encouraged to have completed coursework with demonstrated proficiency (B average or above) in their math and science courses. Additionally, advanced courses in biology, chemistry, and physiology are encouraged.
• Applicants interested in applying to the Biomedical Engineering and Physiology Track are advised to take courses in quantitative science and engineering, such as signal processing, computer science, and instrumentation.

Holistic review

Our Ph.D. program prepares students to translate scientific discoveries into applications that improve patient care. This requires a wide range of skills, aptitudes, and characteristics. Along with the basic set of prerequisites, the track admissions committees take a holistic approach to admissions; meaning, they take into consideration the many factors that make up an applicant. These acceptance factors include:

• Academic performance
• Letters of recommendation
• Personal statement
• Research experience

Transfer student policy

The only pathway to matriculation at Mayo Clinic Graduate School of Biomedical Sciences is through application during the annual application window, September 1 - December 4.

The Ph.D. program does not accept transfer students; however, transfer credits for graduate courses taken at another institution may be considered if appointed to our Ph.D. program.

Application window

Apply between Sept. 1 and Dec. 4 for the following academic year.

To get in touch with the Ph.D. Program, fill out the form on the Contact Us page .

	University of Houston
	Jun 30, 2024   2024-2025 Graduate Catalog (Catalog goes into effect at the start of the Fall 2024 semester)     2024-2025 Graduate Catalog (Catalog goes into effect at the start of the Fall 2024 semester) | Cullen College of Engineering    > Department of Biomedical Engineering    > Biomedical Engineering, PhD In addition to continued study of a broad range of engineering fundamentals, candidates for the doctoral degree enjoy intensive exposure to a specific field of engineering research. Individual research is the major focal point for these students, who are expected to expand the frontiers of knowledge in their area of endeavor. Moreover, candidates learn and experience the general philosophy, methods, and concepts of research and scholarly inquiry, so that they may contribute after graduation to substantive issues completely unrelated to their doctoral research. Please visit the Biomedical Engineering website for more information. Admission Requirements The graduate programs are open to all qualified individuals with a Bachelor of Science (B.S.) or Masters of Science (M.S.) in Biomedical Engineering or related field. Selection of an advisor is critical to completing the degree and therefore should be done as soon as possible. If a student is admitted to the Ph.D. program without an advisor, an advisor will not be assigned to them. Students must meet or exceed these requirements in order for their application to be reviewed. B.S. Degree: Biomedical Engineering or related field GPA: 3.00/4.00 on last 60 hours or Graduate hours if hold MS degree Recommended GRE*: (Current scale) Q-159, V-150 (Prior scale) Q-750, V-450 (International Applicants) TOEFL: PBT- 580, CBT- 236, IBT- 92 (International Applicants) IELTS: 7.0 (International Applicants) DuoLingo: 105 *These scores reflect those of a competitive applicant but admission into our program is based on a holistic review of your application. Course Requirements Upon admission, students with degrees in related fields will be evaluated on a case-by-case basis and may be required to take additional leveling courses. These leveling courses do not count towards the graduate degree. Generally, every graduate student should have taken: 2 years of Calculus (through differential equations) 1 year of Engineering Physics (calculus based physics) 1 year of Biology 1 year of Chemistry Acceptance into the program is based on a competitive combination of academic background, GRE scores, recommendation letters, resume, and the statement of purpose. The Checklists below list all requirements for the Application Submission: Applicant Checklist UH Graduate School Application Application Fee Official Transcripts from all colleges and universities you have attended (Scanned copies of official transcripts can be uploaded as PDF files and may be used to make admission decisions. If admitted, however, you will not be able to enroll without the official transcript(s) showing undergraduate degree conferral on file.) GRE scores (University code is 6870) Statement of Purpose (Upload into Application) Resume/CV (Upload into Application) 3 Letters of Recommendation (Submit emails within the Application and forms will be sent to Recommenders) International applications have additional documentation requirements, including fulfilling English language proficiency requirements with either degree completion or submitted test scores. For more information, visit the International Graduate Students website. Note: When preparing your Resume/CV and Personal Statement for submission, please be sure to highlight your past research, current research interests, and UH Biomedical Engineering faculty that you are interested in working with. There is no prompt or length requirement for the statement of purpose. For more information about the Graduate School Admissions, please visit How to Apply to the UH Graduate School . Doctor of Philosophy in Biomedical Engineering (with prior M.S. Degree) Credit hours required for this degree: 54.0 The program requires a minimum of 54 credit hours of approved graduate work distributed as follows: One (1) math course (beyond M.S. level):   BIOE 6300 - Mathematical Methods in Biomedical Engineering Credit Hours: 3.0 One (1) core course:   BIOE 6350 - Genomic and Proteomic Engineering Credit Hours: 3.0 Six (2) elective courses Eighteen (30) research credits Twelve (12) dissertation credits BIOE 6111 - Graduate Bioengineering Seminar Credit Hours: 1.0 (required with research enrollment) The elective courses must be relevant to the student’s research and approved by their advisor. Five of the eight elective courses must be taken within the BIOE department (effective Fall 2016). Courses taken outside of the department for elective credit must have previously been approved by the department. Doctor of Philosophy in Biomedical Engineering (directly from Undergraduate) Credit hours required for this degree: 72.0 The program requires a minimum of 72 credit hours of approved graduate work distributed as follows: Two (2) math courses:   BIOE 6300 - Mathematical Methods in Biomedical Engineering Credit Hours: 3.0 and approved MATH elective One (1) statistics course   BIOE 6301 - Statistical Methods in Biomedical Engineering Credit Hours: 3.0 Four (4) elective courses Thirty six (36) research credits BIOE 6111 - Graduate Bioengineering Seminar Credit Hours: 1.0 One of the four elective courses must be taken within the BIOE department (effective Fall 2020). Courses taken outside of the department for elective credit must have previously been approved by the department. Degree Requirements The Seminar Course ( BIOE 6111   ) is not a traditional lecture/lab course. BIOE 6111 is a professional development opportunity aimed at engaging students outside of the classroom by bringing in professionals within the field as well as an opportunity for students to present their research endeavors. Students are required to enroll in ONE Seminar course per TERM as they are enrolled in research hours.  BIOE 6111 is a one credit course, but the credit does not count towards the overall credit hours. For example, if a student is completing their Masters and doing a Thesis, their credit hour total is 30. In adding BIOE 6111, at least once a term during their academic program, they will roughly have taken 32 credit hours. The additional 2 are from the Seminar courses and do not count towards the 30 credits needed to complete the degree but do count towards the overall semester credit count. Adding this One Credit Course to the Term Course Schedule can cause the student to enroll in 10 credits instead of the traditional 9. In this case, students can reduce their research credits by 1, so the total credit hours equal 9 or simply take an extra credit. Qualifying Exam: Doctoral students are eligible to sit for the Qualifying Exam after the second term of graduate studies. Doctoral students MUST complete the Qualifying Exam by the end of their fourth term, but traditionally complete it by the end of their third term. Students must confirm with the Graduate Advisor that they plan to complete their Qualifying Exam in a given term. The Qualifying Exam is administered orally and students must submit two abstracts (1) current research and (2) future research, one week prior to the exam. Notes, PowerPoint slides or electronic displays are prohibited . The Graduate Advisor will create the Qualifying Exam committee based on faculty availability and the student’s schedule. The committee will consist of at least four (4) members: candidate’s Research Advisor, Department Chair, and two (2) additional faculty members from the department. Additional faculty should represent the candidate’s research focus area and are primarily responsible for the examination of the candidate. The Research Advisor may ask questions but is expected to fulfill the advocate role for the candidate as he/she prepares for the examination. The Chair’s primary function is to ensure that there is consistency across all candidate qualifying examinations. Qualifying Exam Committees are coordinated by the Graduate Advisor. Students will be notified of the date and time of their Exam via email. Examinations are expected to span about 1 hour but may vary between 1 to 1.5 hours. The oral component will start with a general overview provided by the candidate on their research thrust area and prospective research project. Committee members will be given hard copies of the two abstracts (supplied by the Doctoral student). Determine student’s depth of understanding of the Biomedical Engineering graduate core. Assess student’s capacity to think critically and apply engineering tools to solve problems. Assess student’s capacity to integrate skills in an area of research in biology and/or biomedical engineering. A successful student will be knowledgeable, able to think critically, and demonstrate the ability to integrate and/or apply course information to topics pertinent to their research area. Pass : the candidate may continue in the PhD program, complete course work, and prepare to defend a prospectus. Fail : the candidate will be removed from the PhD program. A contingent plan may be developed to enter the Masters program, either thesis or non-thesis. The candidate may petition to retake the qualifying exam during which time he/she may be retained in the PhD program until the petition is resolved. If the petition is not accepted, he/she will be removed from the PhD program. If the petition is accepted, a continuation in the PhD program will be contingent upon results of a re- examination. The Qualifying Exam Score Sheet will be filled out and turned into the Graduate Advisor, so the results can be put into the students file. Formation of Dissertation Committee: the advisor as chair, at least two additional faculty members from the Biomedical Engineering Department, and at least one additional University of Houston tenure-track faculty (not from the Biomedical Engineering Department); at least one additional tenure-track faculty (not from the University of Houston); In total, you need a minimum of four tenure-track faculty members from the University of Houston and one tenure-track faculty member from outside the University of Houston. The Committee members must fill out the Committee Appointment Form with their acknowledgement that they will participate. The form must be submitted well before the proposal defense is scheduled since the committee must be approved by the Department and Dean’s Office prior to the defense. A student need not be enrolled while requesting to form a committee but must be enrolled when the defense takes place. If a Committee member is outside of the University of Houston, that member’s CV must be sent to the Graduate Advisor.  The Committee must be formed at least two weeks prior to the Prospectus. Prospectus: Doctoral students must complete their Prospectus at least one term before Graduation. A rough draft of a research proposal should be shown to the student’s research advisor for approval of content prior to scheduling the oral presentation. The oral presentation of the dissertation prospectus is made to the student’s Dissertation committee. Other interested members of the faculty are invited to attend the presentation but are encouraged to leave prior to the questioning by the dissertation committee. The student’s presentation should take advantage of appropriate audio and visual aids and should be limited to no more than 50 minutes. Copies of the written dissertation prospectus must be distributed to all members of the student’s dissertation committee no later than one week prior to the oral presentation. In the oral examination, the student is expected to defend their prospectus and justify that the proposed research is of the acceptable quality and magnitude consistent with quality doctoral education. Following the oral presentation of the research proposition, questions are welcomed from members of the departmental faculty. Following general questions, departmental faculty members other than those on the student’s dissertation committee are excused and the student’s dissertation committee and interested faculty from the student’s major will remain to ask questions of the candidate regarding his proposed research. Generally, the oral discussion of the dissertation prospectus is limited to three hours. After questioning, the candidate is excused from the room while the dissertation committee conducts its deliberations. The Prospectus Committee is comprised of the Dissertation Committee members that were listed on the approved Committee form. The decision regarding whether or not the dissertation prospectus is acceptable is the decision of the dissertation committee alone. The student’s dissertation committee conveys its evaluation of the acceptability of the dissertation prospectus to the chair of the departmental graduate committee by signing the Prospectus Approval Form . If the student’s dissertation prospectus is considered acceptable, the chair of the departmental graduate committee will recommend to the Graduate College that the student be advanced to PhD candidacy status. A re-examination may be scheduled and the entire process repeated, or The student may be removed from the doctoral program. The results of the dissertation prospectus presentation are conveyed to the student by the chair of the departmental graduate committee. Dissertation Defense: The student will coordinate their Defense date with their committee and Advisor. If a room needs to be reserved, the student can contact the Graduate Advisor. Results should be reported to the Graduate Advisor, either via email or in person. For example, in Fall 2014, all students planning to defend, had to have their defense completed by Friday, December 05. All information necessary for submission can be found on the Guide for Preparation of Theses/Dissertations page. Academic Policies University of Houston Academic Policies   Graduate Academic Policies: Cullen College of Engineering   Department Academic Policies: BIO Graduate Handbook   BIOE Graduate Policies BIOE 6300 - Math Methods in BME     BIOE 6301 - Stats Methods in BME     BIOE 6350 - Genomic and Proteomic Engineering     The Qualifying Exam must be completed at the end of the 3rd term, unless an exception has been approved by the Department Chair and Graduate Director. BIOE 6111    - Seminar is required every term for all PhD students enrolled in research hours, unless the student has received an exception from their PI, due to interference with their confirmed graduation date. Math Methods ( BIOE 6300   ) is the first required BIOE math course, and Stats Methods for BME ( BIOE 6301   ) is the required BIOE statistics course. Stats is generally offered in the fall, and Math Methods will be offered in the spring. Once you enroll in research and dissertation, respectively, you have to remain continuously enrolled in research and dissertation . All first term BIOE students may only take BIOE courses. Students who started in and after Fall 2016: Only 25% of your courses may be taken outside of the department. If the course has not previously been approved by the department as an elective, a petition for the course must be submitted and approved prior to the start of the term of intended enrollment. The petition must be approved by your PI and should include an explanation of why the course is relevant to your research. Petitions can be turned in to the Graduate Advisor. Students who started prior to Fall 2016: Please check with the Graduate Advisor regarding elective courses outside of the department. If the course has not previously been approved by the department as an elective, a petition for the course must be submitted and approved prior to the start of the term of intended enrollment. The petition must be approved by your PI and should include an explanation of why the course is relevant to your research. Petitions can be turned in to the Graduate Advisor. Transfer of Credits A student may transfer up to 6 hours of graduate-level work completed elsewhere or at the University of Houston upon the approval of the Director of Graduate Studies. The student will need to file a general petition within one term after admission to graduate program. Cumulative Grade Point Average (GPA) This average is on all courses attempted at the university during the graduate program.  Students must maintain an overall GPA of 3.0 or better in order to remain in good academic standing for the graduate program. Students who drop below a 3.0 cumulative GPA will be placed on Academic Warning. Failure to bring up the cumulative GPA to 3.0 in the following term may result in dismissal of the program. The cumulative GPA must be 3.0 or better at all times in order to maintain eligibility for assistantships or in-state tuition waivers when applicable. The cumulative GPA must be 3.0 or better at all times in order to receive the in-state tuition waiver.  If you do not meet this requirement, you will lose the scholarship and no longer be eligible for in-state tuition.  If you drop below the 3.0 GPA in the first term, you may not receive the 2nd installment of the scholarship. For Current Students Biomedical data science program information. Nearly all of the DBDS program policies and procedures are described in the Student Handbook, updated 12/13/23. Current DBDS Courses Student handbook, cs/stats/math/eng electives, coterminal ms advice, hcp ms advice, presentations, dbds student wiki. Find our current DBDS courses here.  Biomedical Data Science Student Handbook The DBDS Student Handbook is a reference for DBDS program policies. DBDS Student Handbook, updated 12/13/2023 ( pdf ). Note : the DBDS Curriculum has changed. Students starting after July 1, 2018 should use the new curriculum, which is described on the ExploreDegrees pages . Others should follow what is on this page. Take a look at our current courses.  Curriculum (admitted before July 1, 2018) Computer science, statistics, mathematics, and engineering electives. STATS 315A is recommended, not required. Curriculum (admitted before Aug 1, 2016) Dbds core courses (12 units). Required: BIOMEDIN 212 and the three courses from the list below. BIOMEDIN 202 Biomedical Data Science BIOMEDIN 212 Introduction to Biomedical Data Science Research Methodology BIOMEDIN 214 Representations and Algorithms for Computational Molecular Biology BIOMEDIN 215  Data Driven Medicine For PhD students who have received waivers (see below) for any of the core courses, then you should replace them with an equivalent number of units from another BIOMEDIN course, or CS/Stats/Math/Eng  elective . You may, with permission, also replace waivered courses with DBDS 299 units. HCP students: Note that BIOMEDIN 212 is a project class that has to-date been only available on campus, so you are currently exempt (replace with other course from DBDS, or from the CS/Stats/Math/Eng electives, as below). We have contemplated offering a remote version of 212, so please contact us if you are interested. Core courses should be taken for grade, not pass/fail (all students). Computer Science, Statistics, Mathematics, and Engineering Electives (24 units) See our  electives page  for the full list. If the course does appear on the list, then it is in principle acceptable, but realize that your entire elective list must be approved. This is to ensure some degree of coherence and to avoid excessive overlap of course material. It is therefore possible to submit an elective list, all of whose courses are listed, but which considered together would not be approved. Note that CS 107 and CS 108 can count towards the CS/Stats/Math/Eng category. CS 106A, B cannot count for this category, but can be counted as Unrestricted Electives. BIOMEDIN 224 and BIOMEDIN 258 are basically courses in biology and medicine, so don’t count as this category. All other BIOMEDIN courses can count in this category, if needed. Up to 9 units can be 100-199 level; the rest must be 200 and above. Up to 8 units can be taken pass/fail. Social and Ethical Issues Electives (4 units) To find all the approved courses in this category, type “dbds::ethics” into the search box in explorecourses, or click here . Note that MED 255 is required for all MS and PhD students engaged in NIH-funded research at Stanford. It is not required but strongly recommended for coterm MS students not doing research. There is no distance education version of this class for HCP students. HCP students: currently, only two classes satisfy this requirement and are offered through SCPD. These are: MS&E 256: Technology Assessment and Regulation of Medical Devices (Spring) and ME 208: Patent Law and Strategy for Innovators and Entrepreneurs, (Autumn). For HCP students who hold an MD degree or equivalent: this set of electives is waived on the basis of your medical school training, so replace with 4 more unrestricted units. Unrestricted Electives (6 units) Any graduate level class (200 and above) can be used for this category. Classes 100 and above can also count, subject to the restrictions listed below. Biology/Medicine Electives (for PhD students only) (9 units) In order to reach a total of 52 units of core curriculum, PhD students should take an additional 9 units; this should include 6 units of biology or medicine classes relevant to their research interests, 2 units of BIOMEDIN 290 Biomedical Data Science Teaching Methods and one additional unit of unrestricted elective. All 6 units can be taken as Credit/No Credit. All courses taken towards a graduate degree must be 100 level or above. At least 50% must be 200 level or above. BIOMEDIN 201 can be taken up to three times for credit. MS: total of 45 units required at Stanford. PhD: total of 135 units, of which at least 27 units should come from DBDS, CS/Stats/Math/Engr electives, Biology/Medicine electives, or courses satisfying the Ethics requirement (including MED 255). They may not include research, teaching, journal club, or other classes can that only be taken pass/fail such as some seminars. Coterms: see our  Coterm page . HCP MS students: see the  SCPD Student Handbook . Many more details on  ExploreDegrees . The core curriculum generally entails a minimum of 45 units of course work for MS students and 54 units of course work for PhD students, but can require substantially more or less depending upon the courses chosen and the previous training of the student. BIOMEDIN 299, 801, and 802 may be taken for satisfactory/no credit (S/NC). Waivers: The varying backgrounds of students are well recognized and no one is required to take courses in an area in which he or she has already been adequately trained; under such circumstances, students are permitted to skip courses or substitute more advanced work using a formal annual process administered by the DBDS executive committee, in which students demonstrate satisfaction of core curriculum prerequisites, and request permission to receive core curriculum credit for classes taken previously in areas of the core curriculum. Students design appropriate programs for their interests with the assistance and approval of their Biomedical Data Science academic adviser. Also, see the curriculum requirements for the MS and PhD degrees listed on the DBDS  page in exploredegrees . Advice on choosing courses and useful links Fill in a  course plan , paying attention to listed prerequisites and when course will be offered. Ask other students. See the  student wiki . Discuss with your academic advisor. Read the  Student Handbook . Stanford Explorecourses (enter “BIOMEDIN” to find DBDS courses) Stanford Center for Professional Development (SCPD)  (for HCP students) There are two categories of electives in our curriculum: Computer science, mathematics, statistics, and engineering electives. This page is a list of courses which can used for this category. Unrestricted electives. These can be any graduate-level courses at Stanford at or above the 100 level (subject to degree-specific limits). Note that a course not on this list is not an allowable elective. If you want to add a course to this list, send an email to DBDS Student Services ( [email protected] ) for consideration. Your particular course plan still needs to be approved by your academic advisor. Not all subsets of the following list are acceptable, for example, in the case of significant overlap between courses. Also, to find a course on this list, you may need to look under its cross-listed course ID. APPPHYS 215: Numerical Methods for Physicists and Engineers APPPHYS 217: Estimation and Control Methods for Applied Physics APPPHYS 223: Stochastic and Nonlinear Dynamics (BIO 223) APPPHYS 223B: Nonlinear Dynamics: This Side of Chaos APPPHYS 293: Theoretical Neuroscience APPPHYS 315: Methods in Computational Biology APPPHYS 345: Advanced Numerical Methods for Data Analysis and Simulation BIO 223: Stochastic and Nonlinear Dynamics (APPPHYS 223) BIOC 223: Open Problems in Biology BIODS 205: Bioinformatics for Stem Cell and Cancer Biology BIODS 215: Topics in Biomedical Data Science: Large-scale inference BIODS220: Artificial Intelligence in Healthcare (CS271, BIOMEDIN220) BIODS 237: Deep Learning in Genomics and Biomedicine (BIOMEDIN 273B, CS 273B, GENE 236) BIODS 239: Introduction to Analysis of RNA Sequence Data (BIOC 239) BIODS 253: Software Engineering for Scientists BIODS 260A,B,C: Workshop in Biostatistics (STATS 260A,B,C) BIODS 271 (CS 277): Foundation Models for Healthcare BIOE 115: Computational Modeling of Microbial Communities (MI 245) BIOE 210: Systems Biology (BIOE 101) BIOE 285: Computational Modeling in the Cardiovascular System (CME 285, ME 285) BIOE 279: Computational Biology: Structure and Organization of Biomolecules and Cells (BIOMEDIN 279, BIOPHYS 279, CME 279, CS 279) BIOE 291: Principles and Practices of Optogenetics BIOE 300B: Engineering Concepts Applied to Physiology BIOE301E: Computational Protein Modeling Laboratory BIOE331: Protein Engineering BIOE 332: Large-Scale Neural Modeling BIOE 334: Engineering Principles in Molecular Biology BIOMEDIN 219: Mathematical Models and Medical Decisions BIOMEDIN 220: Artificial Intelligence in Healthcare (BIODS 220, CS 271) BIOMEDIN 221: Machine Learning Approaches for Data Fusion in Biomedicine BIOMEDIN 222: Cloud Computing for Biology and Healthcare (CS 273C, GENE 222) BIOMEDIN 224 does NOT count towards this category BIOMEDIN 226: Digital Health Practicum in a Health Care Delivery System BIOMEDIN 233: Intermediate Biostatistics: Analysis of Discrete Data (EPI 261, STATS 261) BIOMEDIN 245: Statistical and Machine Learning Methods for Genomics (BIO 268, CS 373, GENE 245, STATS 345) BIOMEDIN 248: Clinical Trial Design in the Age of Precision Medicine and Health (BIODS 248, BIODS 248P, STATS 248) BIOMEDIN 248B: Causal Inference in Clinical Trials and Observational Study (II) BIOMEDIN 251: Outcomes Analysis (HRP 252, MED 252) BIOMEDIN 262: Computational Genomics (CS 262) BIOMEDIN 273A: The Human Genome Source Code (CS 273A, DBIO 273A) BIOMEDIN 273B: Deep Learning in Genomics and Biomedicine (BIODS 237, CS 273B, GENE 236) BIOMEDIN 279: Computational Biology: Structure and Organization of Biomolecules and Cells (CS 279, BIOPHYS 279, BIOE 279, CME 279) BIOMEDIN 371: Computational Biology in Four Dimensions (CS 371, BIOPHYS 371, CME 371) BIOMEDIN 374: Algorithms in Biology (CS 374) BIOMEDIN 472: Data Sciencee and AI for COVID-19 (BIODS 472, CS 472) BIOPHYS 279: Computational Biology: Structure and Organization of Biomolecules and Cells (BIOMEDIN 279, BIOE 279, CME 279, CS 279) BIOPHYS 371: Computational Biology in Four Dimensions (BIOMEDIN 371, CME 371, CS 371) BIOS 221: Modern Statistics for Modern Biology (STATS 366) BIOS 234: Personalized Genomic Medicine CBIO 243: Principles of Cancer Systems Biology CHEM 263: Machine Learning for Chemical and Dynamical Data CME 100: Vector Calculus for Engineers (ENGR 154) CME 102: Ordinary Differential Equations for Engineers (ENGR 155A) CME 103: Introduction to Matrix Methods (EE 103) CME 104: Linear Algebra and Partial Differential Equations for Engineers (ENGR 155B) CME 106: Introduction to Probability and Statistics for Engineers (ENGR 155C) CME 108: Introdution to Scientific Computing (MATH 114) CME 151A: Introduction to Data Visualization in D3 CME 200: Linear Algebra with Application to Engineering Computations (ME 300A) CME 204: Partial Differential Equations in Engineering (ME 300B) CME 207: Numerical Methods in Engineering and Applied Sciences CME 211: Software Development for Scientists and Engineers CME 212: Advanced Software Development for Scientists and Engineers CME 213: Introduction to Parallel Computing using MPI, openMP, and CUDA CME 214: Software Design in Modern Fortran for Scientists and Engineers (EARTHSCI 214) CME 250: Introduction to Machine Learning CME 250A: Machine Learning on Big Data CME 251: Geometric and Topological Data Analysis (CS 233) CME 285: Computational Modeling in the Cardiovascular System (BIOE 285, ME 285) CME 263: Introduction to Linear Dynamical Systems (EE 263) CME 279: Computational Biology: Structure and Organization of Biomolecules and Cells (BIOMEDIN 279, BIOE 279, BIOPHYS 279, CS 279) CME 292: Advanced MATLAB for Scientific Computing CME 302: Numerical Linear Algebra CME 303: Partial Differential Equations of Applied Mathematics (MATH 220) CME 309: Randomized Algorithms and Probabilistic Analysis (CS 265) CME 323: Distributed Algorithms and Optimization CME 330: Applied Mathematics in the Chemical and Biological Sciences (CHEMENG 300) CME 334: Advanced Methods in Numerical Optimization (MS&E 312) CME 362: An Introduction to Compressed Sensing (STATS 330) CME 371: Computational Biology in Four Dimensions (BIOMEDIN 371, BIOPHYS 371, CS 371) CME 500: Numerical Analysis and Computational and Mathematical Engineering Seminar CME 510: Linear Algebra and Optimization Seminar CS 103: Mathematical Foundations of Computing CS 106A and 106B do NOT count towards this category CS 107: Computer Organization and Systems CS 108: Object-Oriented Systems Design CS 109: Introduction to Probability for Computer Scientists CS 109L: Statistical Computing with R Laboratory CS 110: Principles of Computer Systems CS 111: Operating Systems Principles CS 124: From Languages to Information (LINGUIST 180, LINGUIST 280) CS 129: Applied Machine Learning CS 131: Computer Vision: Foundations and Applications CS 140: Operating Systems and Systems Programming CS 142: Web Applications CS 143: Compilers CS 144: Introduction to Computer Networking CS 145: Introduction to Databases CS 147: Introduction to Human-Computer Interaction Design CS 148: Introduction to Computer Graphics and Imaging CS 149: Parallel Computing CS 154: Introduction to Automata and Complexity Theory CS 155: Computer and Network Security CS 157: Logic and Automated Reasoning CS 161: Design and Analysis of Algorithms CS 164: Computing with Physical Objects: Algorithms for Shape and Motion CS 166: Data Structures CS 167: Readings in Algorithms CS 193C: Client-Side Internet Technologies CS 193P: iPhone and iPad Application Programming CS 205L: Continuous Mathematical Methods with an Emphasis on Machine Learning CS 221: Artificial Intelligence: Principles and Techniques CS 223A: Introduction to Robotics (ME 320) CS 224D: Deep Learning for Natural Language Processing CS 224M: Multi-Agent Systems CS 224N: Natural Language Processing (LINGUIST 284) CS 224S: Spoken Language Processing (LINGUIST 285) CS 224U: Natural Language Understanding (LINGUIST 188, LINGUIST 288) CS 224W: Social and Information Network Analysis CS 225A: Experimental Robotics CS 225B: Robot Programming Laboratory CS 226: Statistical Techniques in Robotics CS 227B: General Game Playing CS 228: Probabilistic Graphical Models: Principles and Techniques CS 229: Machine Learning CS 229A: Applied Machine Learning CS 229T: Statistical Learning Theory (STATS 231) CS 230: Deep Learning CS 231A: Introduction to Computer Vision CS 231B: The Cutting Edge of Computer Vision CS 231N: Convolutional Neural Networks for Visual Recognition CS 232: Digital Image Processing (EE 368) CS 236: Deep Generative Models CS 236G: Generative Adversarial Networks CS 238: Decision Making under Uncertainty (AA 228) CS 240: Advanced Topics in Operating Systems CS 240E: Embedded Wireless Systems CS 240H: Functional Systems in Haskell CS 242: Programming Languages CS 243: Program Analysis and Optimizations CS 244: Advanced Topics in Networking CS 244B: Distributed Systems CS 244C: Readings and Projects in Distributed Systems CS 244E: Networked Wireless Systems (EE 384E) CS 245: Database Systems Principles CS 246: Mining Massive Data Sets CS 246H: Mining Massive Data Sets Hadoop Lab CS 248: Interactive Computer Graphics CS 248A: Computer Graphics: Rendering, Geometry, and Image ManipulationCS 249A: Object-Oriented Programming from a Modeling and Simulation Perspective CS 249B: Large-scale Software Development CS 254: Computational Complexity CS 255: Introduction to Cryptography CS 259: Security Analysis of Network Protocols CS 261: Optimization and Algorithmic Paradigms CS 262: Computational Genomics (BIOMEDIN 262) CS 263: Algorithms for Modern Data Models (MS&E 317) CS 265: Randomized Algorithms and Probabilistic Analysis (CME 309) CS 266: Parameterized Algorithms and Complexity CS 267: Graph Algorithms CS 268: Geometric Algorithms CS 270: Modeling Biomedical Systems: Ontology, Terminology, Problem Solving (BIOMEDIN 210) CS 272: Introduction to Biomedical Informatics Research Methodology (BIOE 212, BIOMEDIN 212, GENE 212) CS 273A: A Computational Tour of the Human Genome (BIOMEDIN 273A, DBIO 273A) CS 273B: Deep Learning in Genomics and Biomedicine (BIODS 237, BIOMEDIN 273B, GENE 236) CS 274: Representations and Algorithms for Computational Molecular Biology (BIOE 214, BIOMEDIN 214, GENE 214) CS 275: Translational Bioinformatics (BIOMEDIN 217) CS 276: Information Retrieval and Web Search (LINGUIST 286) CS 279: Computational Biology: Structure and Organization of Biomolecules and Cells (BIOMEDIN 279, BIOPHYS 279, BIOE 279, CME 279) CS 295: Software Engineering CS 309A: Cloud Computing CS 315A: Parallel Computer Architecture and Programming CS 315B: Parallel Computing Research Project CS 316: Advanced Multi-Core Systems (EE 382E) CS 319: Topics in Digital Systems CS 328: Topics in Computer Vision CS 329: Topics in Artificial Intelligence CS 331A: Advanced Reading in Computer Vision CS 331B: 3D Representation and Recognition CS 334A: Convex Optimization I (CME 364A, EE 364A) CS 340: Topics in Computer Systems CS 341: Project in Mining Massive Data Sets CS 343: Advanced Topics in Compilers CS 344: Topics in Computer Networks CS 344E: Advanced Wireless Networks CS 345: Advanced Topics in Database Systems CS 347: Parallel and Distributed Data Management CS 348A: Computer Graphics: Geometric Modeling CS 348B: Computer Graphics: Image Synthesis Techniques CS 349: Topics in Programming Systems CS 349C: Topics in Programming Systems: Readings in Distributed Systems CS 354: Topics in Circuit Complexity CS 355: Advanced Topics in Cryptography CS 357: Advanced Topics in Formal Methods CS 358: Topics in Programming Language Theory CS 359: Topics in the Theory of Computation CS 361A: Advanced Algorithms CS 361B: Advanced Algorithms CS 362: Algorithmic Frontiers: Effective Algorithms for Large Data CS 364A: Algorithmic Game Theory CS 364B: Topics in Algorithmic Game Theory CS 366: Graph Partitioning and Expanders CS 367: Algebraic Graph Algorithms CS 369: Topics in Analysis of Algorithms CS 369N: Beyond Worst-Case Analysis CS 371: Computational Biology in Four Dimensions (BIOMEDIN 371, BIOPHYS 371, CME 371) CS 373: Statistical and Machine Learning Methods for Genomics (BIO 268, BIOMEDIN 245, GENE 245, STATS 345) CS 374: Algorithms in Biology (BIOMEDIN 374) CS 375: Large-Scale Neural Network Modeling for Neuroscience (PSYCH 249) CS 379C: Computational Models of the Neocortex CS 427: Hero’s Journey: AI and Game Theory in 3D Real-time Storytelling CS 431: High-Level Vision: Object Representation (PSYCH 250) CS 442: High Productivity and Performance with Domain-specific Languages in Scala CS 447: Software Design Experiences CS 448: Topics in Computer Graphics CS 448B: Data Visualization CS 468: Topics in Geometric Algorithms: Differential Geometry for Computer Science CS 520: Knowledge Graphs CS 522: Seminar in Artificial Intelligence in Healthcare CS 545: Database and Information Management Seminar CS 547: Human-Computer Interaction Seminar CS 528: Machine Learning Systems Seminar DBIO 273A: A Computational Tour of the Human Genome (BIOMEDIN 273A, CS 273A) EE 101A: Circuits I EE 101B: Circuits II EE 102A: Signal Processing and Linear Systems I EE 102B: Signal Processing and Linear Systems II EE 103: Introduction to Matrix Methods (CME 103) EE 108A: Digital Systems I EE 108B: Digital Systems II EE 168: Introduction to Digital Image Processing EE 169: Introduction to Bioimaging EE 179: Analog and Digital Communication Systems EE 248: Fundamentals of Noise Processes EE 256: Numerical Electromagnetics EE 257: Applied Optimization Laboratory (Geophys 258) (GEOPHYS 258) EE 261: The Fourier Transform and Its Applications EE 262: Two-Dimensional Imaging EE 263: Introduction to Linear Dynamical Systems (CME 263) EE 264: Digital Signal Processing EE 276:  Information Theory EE 277: Reinforcement Learning: Behaviors and Applications (MS&E 237) EE 278A: Probabilistic Systems Analysis (EE 178) EE 278B: Introduction to Statistical Signal Processing EE 279: Introduction to Digital Communication EE 282: Computer Systems Architecture EE 284: Introduction to Computer Networks EE 292M: Parallel Processors Beyond Multi-Core Processing EE 361: Principles of Cooperation in Wireless Networks EE 364A: Convex Optimization I (CME 364A, CS 334A) EE 364B: Convex Optimization II (CME 364B) EE 365: Stochastic Control EE 368: Digital Image Processing (CS 232) EE 369A: Medical Imaging Systems I EE 369B: Medical Imaging Systems II EE 369C: Medical Image Reconstruction EE 373A: Adaptive Signal Processing EE 373B: Adaptive Neural Networks EE 376A: Information Theory (STATS 376A) EE 376B: Network Information Theory (STATS 376B) EE 376C: Universal Schemes in Information Theory EE 378A: Statistical Signal Processing EE 378B: Inference, Estimation, and Information Processing EE 379: Digital Communication EE 382C: Interconnection Networks EE 382E: Advanced Multi-Core Systems (CS 316) EE 384A: Internet Routing Protocols and Standards EE 384C: Wireless Local and Wide Area Networks EE 384E: Networked Wireless Systems (CS 244E) EE 384M: Network Science EE 384S: Performance Engineering of Computer Systems & Networks EE 386: Robust System Design EE 387: Algebraic Error Control Codes EE 388: Modern Coding Theory EE 398A: Image and Video Compression EE 464: Semidefinite Optimization and Algebraic Techniques EPI 206: Meta-research: Appraising Research Findings, Bias, and Meta-analysis EPI 224: Genetic Epidemiology (GENE 230) EPI 225: Introduction to Epidemiologic and Clinical Research Methods EPI 226: Intermediate Epidemiologic and Clinical Research Methods EPI 239: Applications of Causal Inference Methods (EDUC 260 A, STATS 209B) EPI 251: Design and Conduct of Clinical Trials EPI 258: Introduction to Probability and Statistics for Clinical Research EPI 259 : Introduction to Probability and Statistics for Epidemiology (HUMBIO 89X) EPI  262: Intermediate Biostatistics: Regression, Prediction, Survival Analysis (STATS 262) ENGR 150: Data Challenge Lab ENGR 154: Vector Calculus for Engineers (CME 100) ENGR 155A: Ordinary Differential Equations for Engineers (CME 102) ENGR 155B: Linear Algebra and Partial Differential Equations for Engineers (CME 104) ENGR 155C: Introduction to Probability and Statistics for Engineers (CME 106) ENGR 205: Introduction to Control Design Techniques ENGR 206: Control System Design ENGR 207A: Linear Control Systems I ENGR 207B: Linear Control Systems II ENGR 209A: Analysis and Control of Nonlinear Systems GENE 236: Deep Learning in Genomics and Biomedicine (BIODS 237, BIOMEDIN 273B, CS 273B) GENE 244: Introduction to Statistical Genetics (STATS 345) HRP 252: Outcomes Analysis (BIOMEDIN 251, MED 252) HRP 255: Decoding Academia: Power, Hierarchies, and Transforming Institutions IMMUNOL 206A: Systems and Computational Immunology IMMUNOL 206B: Directed Projects in Systems and Computational Immunology IMMUNOL 207: Essential Methods in Computational and Systems Immunology IMMUNOL 208: Advanced Computational and Systems Immunology MATH 104: Applied Matrix Theory MATH 106: Functions of a Complex Variable MATH 107: Graph Theory MATH 108: Introduction to Combinatorics and Its Applications MATH 109: Applied Group Theory MATH 110: Applied Number Theory and Field Theory MATH 111: Computational Commutative Algebra MATH 113: Linear Algebra and Matrix Theory MATH 114: Introdution to Scientific Computing (CME 108) MATH 115: Functions of a Real Variable MATH 116: Complex Analysis MATH 118: Mathematics of Computation MATH 120: Groups and Rings MATH 121: Galois Theory MATH 122: Modules and Group Representations MATH 131P: Partial Differential Equations I MATH 132: Partial Differential Equations II MATH 136: Stochastic Processes (STATS 219) MATH 137: Mathematical Methods of Classical Mechanics MATH 143: Differential Geometry MATH 144: Riemannian Geometry MATH 145: Algebraic Geometry MATH 146: Analysis on Manifolds MATH 147: Differential Topology MATH 148: Algebraic Topology MATH 149: Applied Algebraic Topology MATH 151: Introduction to Probability Theory MATH 152: Elementary Theory of Numbers MATH 154: Algebraic Number Theory MATH 155: Analytic Number Theory MATH 159: Discrete Probabilistic Methods MATH 161: Set Theory MATH 171: Fundamental Concepts of Analysis MATH 172: Lebesgue Integration and Fourier Analysis MATH 173: Theory of Partial Differential Equations MATH 174: Calculus of Variations MATH 175: Elementary Functional Analysis MATH 177:  Geometric Methods in the Theory of Ordinary Differential Equations MATH 193: Polya Problem Solving Seminar MATH 205A: Real Analysis MATH 205B: Real Analysis MATH 210A: Modern Algebra I MATH 210B: Modern Algebra II MATH 210C: Lie Theory MATH 215A: Complex Analysis, Geometry, and Topology MATH 215B: Complex Analysis, Geometry, and Topology MATH 215C: Complex Analysis, Geometry, and Topology MATH 216A: Introduction to Algebraic Geometry MATH 216B: Introduction to Algebraic Geometry MATH 216C: Introduction to Algebraic Geometry MATH 217A: Differential Geometry MATH 220: Partial Differential Equations of Applied Mathematics (CME 303) MATH 221A: Mathematical Methods of Imaging (CME 321A) MATH 221B: Mathematical Methods of Imaging (CME 321B) MATH 222: Computational Methods for Fronts, Interfaces, and Waves MATH 224: Topics in Mathematical Biology MATH 226: Numerical Solution of Partial Differential Equations (CME 306) MATH 227: Partial Differential Equations and Diffusion Processes MATH 228: Stochastic Methods in Engineering (CME 308) MATH 230A: Theory of Probability (STATS 310A) MATH 230B: Theory of Probability (STATS 310B) MATH 230C: Theory of Probability (STATS 310C) MATH 231A: An Introduction to Random Matrix Theory (STATS 351A) MATH 231C: Free Probability MATH 232: Topics in Probability: Percolation Theory MATH 233: Probabilistic Methods in Analysis MATH 234: Large Deviations Theory (STATS 374) MATH 236: Introduction to Stochastic Differential Equations MATH 239: Computation and Simulation in Finance MATH 243: Functions of Several Complex Variables MATH 244: Riemann Surfaces MATH 245A: Topics in Algebraic Geometry: Moduli Theory MATH 245B: Topics in Algebraic Geometry: Intersection Theory MATH 245C: Topics in Algebraic Geometry: Alterations MATH 247: Topics in Group Theory MATH 248: Ergodic Theory and Szemeredi’s Theorem MATH 248A: Algebraic Number Theory MATH 249A: Topics in number theory MATH 249B: Topics in Number Theory MATH 249C: Topics in Number Theory MATH 252: Algebraic Groups MATH 254: Geometric Methods in the Theory of Ordinary Differential Equations MATH 256A: Partial Differential Equations MATH 256B: Partial Differential Equations MATH 257A: Symplectic Geometry and Topology MATH 257B: Symplectic Geometry and Topology MATH 257C: Symplectic Geometry and Topology MATH 258: Topics in Geometric Analysis MATH 259: mirror symmetry MATH 261A: Functional Analysis MATH 263A: Lie Groups and Lie Algebras MATH 264: Infinite Dimensional Lie Algebra MATH 266: Computational Signal Processing and Wavelets MATH 269: Topics in symplectic geometry MATH 270: Geometry and Topology of Complex Manifolds MATH 271: The H-Principle MATH 272: Topics in Partial Differential Equations MATH 280: Evolution Equations in Differential Geometry MATH 282A: Low Dimensional Topology MATH 282B: Homotopy Theory MATH 282C: Fiber Bundles and Cobordism MATH 283: Topics in Algebraic and Geometric Topology MATH 283A: Topics in Topology MATH 284: Topics in Geometric Topology MATH 284A: Geometry and Topology in Dimension 3 MATH 284B: Geometry and Topology in Dimension 3 MATH 286: Topics in Differential Geometry MATH 287: Introduction to optimal transportation MATH 295: Computation and Algorithms in Mathematics MATH 301: Advanced Topics in Convex Optimization MATH 310: Algorithms MATH 384: Seminar in Geometry MATH 385: Seminar in Topology MATH 388: Seminar in Probability and Stochastic Processes MATH 389: Seminar in Mathematical Biology MATH 394: Classics in Analysis MATH 395: Classics in Geometry and Topology MGTECON 634: Machine Learning and Causal Inference ME 285: Computational Modeling in the Cardiovascular System (BIOE 285, CME 285) ME 261: Dynamic Systems, Vibrations and Control (ME 161) ME 300B: Partial Differential Equations in Engineering (CME 204) MED 206: Meta-research: Appraising Research Findings, Bias, and Meta-analysis (HRP 206, STATS 211) MED 263: Advanced Decision Science Methods and Modeling in Health (HRP 263) MED 252: Outcomes Analysis (BIOMEDIN 251, HRP 252) MI 245: Computational Modeling of Microbial Communities (BIOE 115) MS&E 120: Probabilistic Analysis MS&E 211: Linear and Nonlinear Optimization MS&E 220: Probabilistic Analysis MS&E 223: Simulation MS&E 226: “Small” Data MS&E 228: Applied Causal Inference with Machine Learning and AI MS&E 252: Decision Analysis I: Foundations of Decision Analysis MS&E 263: Healthcare Operations Management MS&E 310: Linear Programming MS&E 312: Advanced Methods in Numerical Optimization (CME 334) MS&E 328: Foundations of Causal Machine Learning MS&E 335: Queueing and Scheduling in Processing Networks MS&E 352: Decision Analysis II: Professional Decision Analysis MS&E 355: Influence Diagrams and Probabilistics Networks MS&E 454: Decision Analysis Seminar MS&E 463: Healthcare Systems Design NBIO 228: Mathematical Tools for Neuroscience NENS 230: Analysis Techniques for the Biosciences Using MATLAB OIT 673: Data-driven Decision Making and Applications in Healthcare PSYCH 248: Advanced fMRI modeling and analysis PSYCH 248A: fMRI Analysis Bootcamp PSYCH 253: Advanced Statistical Modeling STATS 110: Statistical Methods in Engineering and the Physical Sciences STATS 116: Theory of Probability STATS 166: Computational Algorithms for Statistical Genetics (GENE 245, STATS 345) STATS 191: Introduction to Applied Statistics STATS 200: Introduction to Statistical Inference STATS 201: Design and Analysis of Experiments STATS 202: Data Mining and Analysis STATS 203: Introduction to Regression Models and Analysis of Variance STATS 205: Introduction to Nonparametric Statistics STATS 206: Applied Multivariate Analysis STATS 207: Introduction to Time Series Analysis STATS 208: Introduction to the Bootstrap STATS 209: STATS 209: Introduction to Causal Inference STATS 209B: Applications of Causal Inference Methods STATS 211: Meta-research: Appraising Research Findings, Bias, and Meta-analysis (HRP 206, MED 206) STATS 212: Applied Statistics with SAS STATS 213: Introduction to Graphical Models STATS 214: Machine Learning Theory STATS 215: Statistical Models in Biology STATS 216: Introduction to Statistical Learning STATS 216V: Introduction to Statistical Learning STATS 217: Introduction to Stochastic Processes STATS 218: Introduction to Stochastic Processes STATS 219: Stochastic Processes (MATH 136) STATS 222: Statistical Methods for Longitudinal Data (EDUC 351A) STATS 231: Statistical Learning Theory (CS 229T) STATS 245: Data, Models, and Decision Analytics STATS 253: Spatial Statistics (STATS 352) STATS 260A: Workshop in Biostatistics (BIODS 260A) STATS 260B: Workshop in Biostatistics (BIODS 260B) STATS 260C: Workshop in Biostatistics (BIODS 260C) STATS 261: Intermediate Biostatistics: Analysis of Discrete Data (BIOMEDIN 233, HRP 261) STATS 262: Intermediate Biostatistics: Regression, Prediction, Survival Analysis (HRP 262) STATS 270: A Course in Bayesian Statistics (STATS 370) STATS 285: Massive Computational Experiments, Painlessly STATS 290: Paradigms for Computing with Data STATS 300: Advanced Topics in Statistics STATS 300A: Theory of Statistics STATS 300B: Theory of Statistics STATS 300C: Theory of Statistics STATS 305A: Applied Statistics I STATS 305B: Applied Statistics II STATS 305C: Applied Statistics III STATS 306A: Methods for Applied Statistics STATS 306B: Methods for Applied Statistics: Unsupervised Learning STATS 310A: Theory of Probability (MATH 230A) STATS 310B: Theory of Probability (MATH 230B) STATS 310C: Theory of Probability (MATH 230C) STATS 314: Advanced Statistical Methods STATS 315A: Modern Applied Statistics: Learning STATS 315B: Modern Applied Statistics: Data Mining STATS 316: Stochastic Processes on Graphs STATS 317: Stochastic Processes STATS 318: Modern Markov Chains STATS 319: Literature of Statistics STATS 320: Heterogeneous Data with Kernels STATS 321: Modern Applied Statistics: Transposable Data STATS 322: Function Estimation in White Noise STATS 324: Multivariate Analysis STATS 325: Multivariate Analysis and Random Matrices in Statistics STATS 329: Large-Scale Simultaneous Inference STATS 330: An Introduction to Compressed Sensing (CME 362) STATS 338: Topics in Biostatistics STATS 341: Applied Multivariate Statistics STATS 345: Introduction to Statistical Genetics (GENE 244) STATS 345: Computational Algorithms for Statistical Genetics (GENE 245, STATS 166) STATS 351A: An Introduction to Random Matrix Theory (MATH 231A) STATS 352: Spatial Statistics (STATS 253) STATS 355: Observational Studies (HRP 255) STATS 362: Monte Carlo STATS 366: Modern Statistics for Modern Biology (BIOS 221) STATS 367: Statistical Models in Genetics STATS 370: A Course in Bayesian Statistics (STATS 270) STATS 374: Large Deviations Theory (MATH 234) STATS 375: Inference in Graphical Models STATS 376A: Information Theory (EE 376A) STATS 396: Research Workshop in Computational Biology Funding Sources for DBDS PhD Students All DBDS PhD students are admitted with a funding plan in place. However, we require that enrolled students apply to internal and external funding for which they are eligible . This is typically done in one of the first three years of graduate study. Below are listed the funding sources that would be available to most students. You are encouraged to seek out others as well. Note that for some of these sources having an MS will make you ineligible, or the MS time will count as years of graduate study. NSF Graduate Research Fellowship Program (GRFP) . Open to first  or  second year graduate students. In general, we recommend that you apply in your second year. Contact your research or academic advisor if you want to apply in your first year. Eligibility: No previous graduate training (e.g., masters degree), must be US citizen or permanent resident. Due date: late October. Ruth L. Kirschstein National Research Service Award (NRSA) Individual Predoctoral Fellowship (F31) . Eligibility: US citizen or permanent resident. You should apply in the fall of your 3rd year, immediately after passing your qualifying examination. There are more details in the DBDS Student Handbook. You should also coordinate with DBDS Admin. Apply for Cycle III, due date Dec 8 . Note that you need to take a  Stanford course  beforehand. DOE Computational Science Graduate Fellowship . You apply in your first year of graduate school. Eligibility: US citizen or permanent resident. Due date:  January . National Defense Science & Engineering Graduate Fellowship (NDSEG) . You apply in your first or second year of graduate study. Eligibility: US citizen (not permanent resident). Stanford Interdisciplinary Graduate Fellowship (SIGF) . You can apply in your first three years of graduate study. There are no citizenship requirements. Due date: March. Stanford BIO-X . A comprehensive list of predoctoral funding opportunities is available on the  Stanford Research Management Group website . Checklist for coterminal students The rules are a bit complicated. Here’s a summary: See the  Coterminal Masters Degree  page,  VPGE Coterminal Degrees  page,  Stanford University Procedures for Coterminal Students , and DBDS  curriculum description  in exploredegrees. When asking questions about your particular circumstances, please make sure that you have listed all the courses towards your MS on a  flowsheet , and make that available to us, as nearly all questions involve some aspect of that. You are required to have at least 180 units (undergraduate) and 45 units (graduate). No units may be counted towards both undergraduate and DBDS MS degree. 45 units are required for the DBDS MS. All courses counted towards the MS must be DBDS-related courses at or above the 100 level (as approved by advisor and DBDS program). At least 23 units must be courses at or above the 200 level. At least 27 units must be taken for a grade (unless, through exceptional circumstances such as the 2020 crisis, this is imposible, in that case, consult DBDS Student Services). DBDS core courses may not be taken pass/fail. Exceptions: BIOMEDIN 200 (no longer offered), 201, 205, 206, 207, 290, 299, 390, 801, 802, and MED 255, or DBDS courses that are offered only on a S/NC basis (including during the 2020 crisis). A complete list of elective courses that can count for CS/Stats/Math/Eng is listed  here . Up to 6 units of CS/Stats/Math/Eng can be taken pass/fail. You can apply to DBDS upon completion of 120 units, but no later than the quarter prior to the expected completion of the undergraduate degree. Coterminal students are permitted to count coursework taken in the three quarters immediately prior to their first graduate quarter toward their graduate degree (Summer quarter is not included in the count). The  Coterminal Course Transfer eForm  is used for transferring courses from the undergraduate career to the graduate career, and vice versa. This must be done before the undergraduate degree is conferred. You may take any course you want at Stanford while in the program, including electives not at all related to DBDS, as long as you also have an approved DBDS-related 45-unit program of study that satisfies the requirements for the DBDS degree. For a “Core Biomedical Data Science” course, you can take another course in the same category, any course listed under BIOMEDIN or BIODS, or any course in the CS/Stats/Math/Eng electives list. For a “CS/Math/Stats/Eng” course, you can take another course in the same category. For a “Social/Ethics” course, you can take another course in the same category, or any other course appropriate for graduate degree credit. Example: You took CS181W as an undergraduate. This class satisfies the DBDS MS Social and Ethical requirement; instead of taking graduate courses towards this distributional requirement, you now take an additional 4 units of DBDS-related electives. Enter CS181W on your flowsheet, and put “Y” in the “UG?” column. Note that it then contributes no units to the DBDS MS. Then list a course (or courses) for four units under DBDS-related electives. Example: You took BIOMEDIN 217 as an undergraduate. This class is part of the DBDS core. List the course in the DBDS core part of the flowsheet, and enter “Y” in the “UG?” column. Then list another approved course (or courses) for four units under DBDS-related electives. See the  example spreadsheet . In general, for choosing the courses that go under your (now expanded) set of units under DBDS-related electives, pick graduate courses with some thematic relationship to Biomedical Data Science. Feel free to discuss with the DBDS Program staff if you are unsure whether a course will be approved as DBDS-related. Courses such as BIOMEDIN 299 are fine. Students must finish the master’s degree within three years of their first co-terminal quarter. Note that taking a leave of absence does NOT extend your time to degree. If you want to take more than three years, you will need to petition the registrar. Checklist for HCP MS students This document is some advice about how to navigate our curriculum and how to choose courses. There are differences between the HCP MS requirements and those listed for the other MS and PhD degrees; those differences are highlighted here. Start by reviewing the curriculum at  DBDS curriculum description  in ExploreDegrees,  the SCPD website , and the  SCPD HCP Program Handbook . You should also review the  DBDS Student Handbook . Core courses: Although many classes necessary for the degree are available online, some requirements may be fulfilled through implementation of an alternative plan to be approved by the program. DBDS 212 requires special arrangements with the instructor, so we can include you in the class remotely if possible. Note that core courses should be taken for a grade, not pass/no credit (unless not offered for a grade). If Stats 200 is not offered remotely, then in that case you can consider taking: MS&E 226 or EPI 259 and EPI 261 (both). Computer science, statistics, mathematics, and engineering electives (18 units). Courses that can count for CS/Stats/Math/Eng is listed on the  Electives page . Up to 6 units of CS/Stats/Math/Eng can be taken pass/fail. If you are following the new curriculum (admitted after Aug 1, 2016), note that CS 161 and STATS 200 are required. These two courses, or some of their prerequisites, may not be available through SCPD. You should submit a course plan with next best alternatives: these may include similar courses at Stanford, or courses at other institutions (note that outside courses would not count towards the required 45 units). Social and Ethical issues (4 units): Type “dbds::ethics” into the search box in explorecourses . Note that although MED 255 is listed as required, it is required only for MS and PhD students engaged in NIH-funded research at Stanford. This typically does not apply to HCP students. Unrestricted electives (14 units): They need to be at or above the 100 level. How to put the courses in order. Use one of the  course flowsheets . First, check ExploreCourses and the SCPD website for which quarter the courses are offered. Also, check the listed prerequisites for each class, and also the course websites for additional information about what’s expected so you can estimate how difficult each class will be for you. Typically, SCPD students take one course per quarter. Note that SCPD imposes an upper limit of three courses per quarter. Also, use the DBDS  Student Wiki  for advice on courses. The following courses are only available P/F: BIOMEDIN 201, 205, 206, 207, 290, 299, 801, 802, and MED 255. All courses counted towards the MS must be at or above the 100 level. You can include up to 18 units that you took Non-Degree Option before entering the MS program. At least 27 units must taken for a grade. Requests to transfer from part-time (HCP) to full-time (Academic MS) are reviewed by the DBDS Exec on a case-by-case basis. Final decisions are at the DBDS Exec’s discretion. Please note the following limitations (for students enrolling in the HCP program starting Fall 2020) : Students must  complete a minimum of two (2) quarters in the part-time program excluding summer quarter or enrollment as a non-degree option student, before requesting to transfer to full-time. Therefore, the soonest the transfer can be discussed and approved is during the first DBDS Exec meeting of the third quarter of the student in the HCP program Students must complete a minimum of 10 units of letter-graded courses that meet requirements for the DBDS MS degree before commencing their first full-time (Academic MS) quarter GPA will be considered as part of the request. Students can make a maximum of two (2) transfers during the program (e.g. transfer from part-time to full-time and back to part-time). Students should consider the availability of courses online before requesting to switch from full-time to part-time, especially if this may interfere with their ability to satisfy the requirements of the degree. Advice on Talks and Presentations 10 Suggestions for Improving your Scientific Talks  Lawrence Fagan Informatics Journal Club and Research Talk Template . PowerPoint presentation for Tuesday Talks (Journal Club and Student Research) Journal Club Template  (Altman/Bagley). Revised version of the above. Pre-Proposal Talk Guidelines Suggestions for DBDS Pre-Proposal Talks by Dr. Lawrence Fagan Russ Altman’s Translational Bioinformatics Year in Review slides:  2008 ,  2009 ,  2010 ,  2011 ,  2012 ,  2013 ,  2014 ,  2015 ,  2016 ,  2017 ,  2018 Russ Altman, “ How to Write a Grant Proposal “ Russ Altman, “ How to Review a Grant Proposal “ Russ Altman, “ How to Give a Talk “ Bagley, DBDS Journal Club Template Video Forms for dbds students, course flowsheets by degree program. Coterminal MS Obtain signatures and submit all Milestone forms to the DBDS Student Services Officer, unless otherwise stated. MS Program Proposal Form  – All MS students must complete and submit by end of first quarter of enrollment. Application for Doctoral Candidacy  – To be completed and submitted after passing Qualifying Exam. Doctoral Dissertation Reading Committee form  – To be completed and submitted before scheduling Defense, also required to qualify for TGR. University Doctoral Orals/Defense Exam form  – Date, time, and location of the Exam should be confirmed with committee at least 2 months in advance. The Defense Exam form should be completed and submitted to the Student Services Officer at least 3 weeks before the Defense. Please make sure that you, the Chair, and the other committee members are familiar with the  University policy  in advance of the Exam. TGR (Terminal Graduate Residency) TGR form  – TGR allows students to register at a reduced tuition rate while working on a dissertation, thesis, or department project. Must have completed 135 total units to qualify. Submit to the Office of the University Registrar. Graduate Quarter Petition  – To be submitted to the Office of the University Registrar prior to the quarter of intended graduation. Commencement Walk Through Petition – To be submitted to DBDS Student Services Officer, if student has not completed degree requirements, but would like to participate in June commencement. Walkthrough candidates who submit their forms by May 1 will be in listed in the June Commencement Program. For travel policy, please refer to the DBDS  Student Handbook Register with  Stanford Travel Egencia  before arranging any business travel. Student Request for Travel Required by DBDS before booking your travel or paying registration fees. The GSA per diem rates  can be used to estimate your lodging & meals expenses on the travel request form. Student Certification Form  Required for all travel beforehand. Required by Stanford University before booking your travel or paying registration fees. Travel Reimbursement Expense Summary  Required by DBDS after the trip in you are submitting receipts for personal reimbursement. International travel requirements can be found under  Fly America  rules. Other Forms Non-travel ( Petty Cash )  Reimbursement Form   Petty Cash Form student_request_for_travel_hand-written (word doc)[23] Use to form to transfer into HCP MS or from HCP MS to Academic MS program. Must be submitted at least 3 weeks before the quarter in which the program change will start. See form for more details. Other Registrar Forms Biomedical Data Science Student Wiki The DBDS Student Wiki 314 IMAGES Biomedical Informatics and Data Science Skills (BIDSS) Learning Platform Meharry announces Biomedical Data Science Ph.D. Program Session 1 PhD-Biomedical-Data-Science-blog Ph.D. Biomedical Data Science Program Ph.D. Biomedical Data Science Program VIDEO Biomedical Data Tools Live Development: Brain Activity Classification Master's Degree Programme in Epidemiology and Biomedical Data Science Biomedical Data Tools Live Development: Brain Activity Classification How is Meharry Medical College using AI in Medical Science- Analytics Summit 2023 Interview Series Reading research papers Biomedizinische Datenwissenschaft: Alles rund ums Masterstudium an der MHH COMMENTS Biomedical Data Science, PhD Biomedical Data Science, PhD. The current explosion of biomedical data provides an awesome opportunity to improve understanding of the mechanisms of disease and ultimately to improve human health care. However, fully harnessing the power of high-dimensional, heterogeneous data requires a new blend of skills including programming, data ... BMDS-PHD Program The Biomedical Data Science Program is interdisciplinary and offers instruction and research opportunities leading to MS and PhD degrees in Biomedical Data Science. The program emphasizes research to develop novel computational methods that can advance biomedicine. Students receive training in investigating new approaches to conceptual modeling ... Health Data Science The PhD Program in Health Data Science trains the next generation of data science leaders for applications in public health and medicine. The program advances future leaders in health and biomedical data science by: (i) providing rigorous training in the fundamentals of health and biomedical data science, (ii) fostering innovative thinking for the design, conduct, analysis, and reporting of ... Biomedical Data Science Graduate Program Overview The Biomedical Data Science Graduate Program has a long history both at Stanford and internationally, as the first program of its kind. The degree program was initiated in October 1982 as Medical Information Sciences (MIS) and continues to emphasize interdisciplinary education between medicine, computer science, and statistics, offering pre ... Biomedical Data Science and Informatics, M.S. / Ph.D. Clemson University and the Medical University of South Carolina offer a joint Master of Science and Ph.D. degree in Biomedical Data Science and Informatics. This unique collaboration combines Clemson's strengths in computing, engineering, and public health with MUSC's expertise in biomedical sciences to produce the next generation of data scientists, prepared to manage and analyze big data ... Biomedical Data Science Kavli Neuroscience Discovery Institute. Mathematical Institute for Data Science (MINDS) Translational Tissue Engineering Center. See More. Johns Hopkins Biomedical Engineering. Contact BME. Homewood Campus. 3400 N. Charles StreetWyman Park BuildingSuite 400 WestBaltimore, MD 21218. (410) 516-8120. Stanford The Department of Biomedical Data Science merges the disciplines of biomedical informatics, biostatistics, computer science and advances in AI. The intersection of these disciplines is applied to precision health, leveraging data across the entire medical spectrum, including molecular, tissue, medical imaging, EHR, biosensory and population data. For Prospective Students The PhD minor in Biomedical Data Science is designed for graduate students in allied departments to acquire specialization in biomedical informatics during their graduate studies. The PhD minor is open to Stanford graduate students only, and is not a formal degree. The minor may be of particular interest to those in Bioengineering, Computer ... BIOM-PHD Program The Biomedical Data Science Program is interdisciplinary and offers instruction and research opportunities leading to MS and PhD degrees in Biomedical Data Science. All students must complete the core curriculum requirements and additional coursework to fulfill degree requirements and pursue their technical interests and goals as specified for ... Biomedical Data Science Chair: Michael Whitfield, PhD. The mission of the department of Biomedical Data Science at Geisel School of Medicine is to advance scholarship and education for the quantitative and computational analysis of biomedical and health data and to unite and promote Biomedical Informatics and Biostatistics as essential disciplines for the mentoring ... PhD in Biomedical Data Science Students will carryout three semester- or summer-long research rotations in the first year (fall-spring-summer terms) concerning a substantive problem in biomedical data science, advised by a Program Faculty member, in collaboration with an additional UW faculty member from the biological, biomedical, or population health sciences. Biomedical Data Science Department of Medicine. Led by Robert L. Grossman, PhD ,the Section of Biomedical Data Science provides an intellectual home for faculty whose research interests encompass computational biomedicine; biomedical data science (data science and its applications to biology, medicine and healthcare), and biomedical informatics (bioinformatics ... Biomedical Informatics & Data Science The Biomedical Informatics and Data Science (BIDS) PhD program will provide opportunities for graduate students with diverse backgrounds to gain expertise in the field of biomedical informatics and data science, to train as future biomedical researchers and industry leaders in biomedical informatics and data science core competencies, and to engage in scholarly activities under the […] PhD in Biomedical Informatics PhD in Biomedical Informatics The PhD program in Biomedical Informatics is part of the Coordinated Doctoral Programs in Biomedical Sciences. Students are trained to employ a scientific approach to information in health care and biomedicine. Students may only enroll full-time, as required by the Graduate School of Arts and Sciences (GSAS). The first two years are biomedical data science PhD Projects, Programmes & Scholarships Fully funded Master by Research in Biology studentships: Analysing datasets in disease models of obesity. FUNDING (October 2024 start - September 2025 finish). 1 year full-time Master by Research studentship (this covers all university tuition fees and research project costs, plus £19,237/year tax free 'stipend' for living expenses) in the ... Ph.D. Biomedical Data Science Program The Biomedical Data Science Ph.D. will prepare you to develop novel tools and leverage real-world, massive biomedical data to advance precision health, drug discovery and other areas of biomedical science. Biomedical data science courses include predictive modeling, computational structural biology, applied machine learning, mathematical and ... Biomedical Data Science MS Degree The Biomedical Informatics Program is a graduate and postdoctoral program, now part of the Department of Biomedical Data Science. Our mission is to train future research leaders to design and implement novel quantitative and computational methods that solve challenging problems across the entire spectrum of biology and medicine. Department of Biomedical Data Science Data Science has emerged as a new interdisciplinary field to address these challenges. The automated management, retrieval and interpretation of extensive biological, medical and health information require that Data Science involves coordination and integration of the existing disciplines of Biomedical Informatics and Biostatistics. Biomedical Data Science and Informatics DOCTOR OF PHILOSOPHY. A joint PhD degree program offered by Clemson University and the Medical University of South Carolina. The program is a unique collaboration that brings together Clemson's strengths in computing, engineering, and public health and MUSC's expertise in biomedical sciences to produce the next generation of data scientists, prepared to manage and analyze big data sources ... Frequently Asked Questions DBDS is the Biomedical Data Science Training Program, the program affiliated with the Department of Biomedical Data Science in Stanford's School of Medicine granting MS and PhD degrees. BMIR is the Stanford Center for Biomedical Data Science Research, a division in the Department of Medicine devoted to research in this area. Several faculty ... Biomedical Data Science Graduate Certificate The Biomedical Data Science Graduate Certificate, previously named "Biomedical Informatics: Data, Modeling and Analysis", explores the design and implementation of novel quantitative and computational methods that solve challenging problems across the entire spectrum of biology and medicine. You will acquire knowledge and skills in bio- and ... Biomedical Data Science Course Biomedical Data Science Graduate Certificate. This course introduces the data modalities and methods valuable to ask and answer probing and novel questions that advance biomedicine. You will get exposure to a variety of current data types from imaging and omics to patient-centric and digital health generated data types. State of Affairs: Summer Updates for Biomedical Informatics & Data Science A new textbook on natural language processing, co-edited by Hua Xu, PhD, FACMI, assistant dean for biomedical informatics, offers students and researchers essential reading for biomedical machine learning techniques. ... Attaining department status will enable Biomedical Informatics & Data Science to build on the momentum it already has ... Admissions The only pathway to matriculation at Mayo Clinic Graduate School of Biomedical Sciences is through application during the annual application window, September 1 - December 4. The Ph.D. program does not accept transfer students; however, transfer credits for graduate courses taken at another institution may be considered if appointed to our Ph.D ... Program: Biomedical Engineering, PhD The graduate programs are open to all qualified individuals with a Bachelor of Science (B.S.) or Masters of Science (M.S.) in Biomedical Engineering or related field. Selection of an advisor is critical to completing the degree and therefore should be done as soon as possible. For Current Students In order to reach a total of 52 units of core curriculum, PhD students should take an additional 9 units; this should include 6 units of biology or medicine classes relevant to their research interests, 2 units of BIOMEDIN 290 Biomedical Data Science Teaching Methods and one additional unit of unrestricted elective. 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