theoretical cs phd

Theoretical Computer Science

This field comprises two sub-fields: the theory of algorithms, which involves the design and analysis of computational procedures; and complexity theory, which involves efforts to prove that no efficient algorithms exist in certain cases, and which investigates the classification system for computational tasks. Time, memory, randomness and parallelism are typical measures of computational effort.

Theoretical computer science is a natural bridge between mathematics and computer science, and both fields have benefited from the connection. The field is very active, with exciting breakthroughs and intriguing challenges. The P =? NP problem is one of the seven of the Clay Millennium Problems. The recent polynomial time primality algorithm received a Clay Math research award.

MIT has been the leading center for theoretical computer science for several decades. A strong group of EECS Department faculty also works in this field and runs joint activities with the Mathematics faculty through CSAIL. The RSA cryptosystem and Akamai Technologies are two important success stories that were developed by Mathematics and EECS Department faculty.

Our group investigates active areas such as quantum computation, approximation algorithms, algorithms in number theory, distributed computing and complexity theory.

Department Members in This Field

• Michel Goemans Algorithms, Combinatorics & Optimization
• Jonathan Kelner Theoretical Computer Science
• Tom Leighton Theoretical Computer Science, Combinatorics
• Ankur Moitra Theoretical Computer Science, Machine Learning
• Elchanan Mossel Probability, Algorithms and Inference
• Peter Shor Quantum Computation, Quantum Information
• Michael Sipser Algorithms, Complexity Theory

Instructors & Postdocs

• Mitali Bafna Theoretical Computer Science
• Manik Dhar Combinatorics, Theoretical Computer Science
• Jason Gaitonde Algorithms, Learning Theory, Probability Theory, Networks
• Shivam Nadimpalli Analysis of Boolean Functions, High-Dimensional Geometry, Property Testing
• Alexander Poremba Quantum computation, cryptography
• Yihui Quek Quantum Computing, Complexity Theory, Quantum Noise and Error Correction
• Michael Simkin Probabilistic combinatorics, random graphs, and random processes
• Anirudh Sridhar Statistical inference, network cascades, graph algorithms, graph matching

Graduate Students*

• Anna Brandenberger
• Andrey Khesin Quantum Computing
• Jonathan Lu
• Janko Ondras
• Yuchong Pan
• Jonathan Rodriguez Figueroa
• Anastasiia Struss
• Xinyu (Norah) Tan Quantum computing, quantum information, coding theory
• Neekon Vafa
• Shuo Wang Theoretical Computer Science, Combinatorics
• Lichen Zhang Theoretical computer science, machine learning and data science
• Kai Zhe Zheng

*Only a partial list of graduate students

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Harvard John A. Paulson School of Engineering and Applied Sciences

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Harvard offers an outstanding environment to pursue graduate studies in theoretical computer science. It provides a combination of world-class faculty, smaller program with individual attention, opportunity to collaborate with researchers across many areas in one of the world's top universities, as well as take advantage of the unique resources for TCS in the greater Cambridge/Boston area. If you are interested in graduate studies in the theory of computing, please apply to the  Ph.D program in SEAS  (see also their  FAQ ), and indicate your interest in ToC on your application. (You should apply to the "Engineering and Applied Science" program, choose "PhD Computer Science" for subject and "PhD" for the degree, and in the SEAS supplemental form page indicate your interest in "Theory of Computation" under Computer Science.)

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Theoretical Computer Science

Our efforts in Theoretical Computer Science span traditional algorithms and complexity, and often make contact with pure math (algebra, combinatorics, geometry, probability). Leonard Schulman works on aspects of coding and communication, combinatorics and probability, theoretical machine learning, and algorithmic game theory. Chris Umans works on algorithms and complexity with connections to algebra, and has an ongoing interest in algorithms for matrix multiplication that employ group theory and representation theory. Thomas Vidick is known for his work in quantum complexity and cryptography, particularly in studying the power of quantum interactive proofs. Urmila Mahadev has established landmark results regarding the classical verification of quantum computation, and is interested in problems at the intersection of quantum computation and cryptography.

Caltech is a place that is infused with theory, and many CMS and affiliated faculty make contributions in TCS as part of their work, including Babak Hassibi , Michelle Effros and Victoria Kostina (coding and information theory), Joel Tropp (matrix concentration and randomized algorithms), Erik Winfree and Lulu Qian (molecular programming), Fernando Brandao , Alexei Kitaev , and John Preskill (quantum information and computing), and Omer Tamuz (algorithmic economics, probability and statistics), and Adam Wierman (online algorithms and algorithmic economics).

Northwestern CS Theory Group

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Prospective Graduate Students

We are always looking for bright and motivated students interested in theoretical computer science! The theory group admit graduate students as a group. If you are interested in pursuing a PhD in theoretical CS, we encourage you to apply to the PhD program at Northwestern University . Please mention Theory as your primary interest in your application, and list the theory faculty that are closest to your interests.

The deadline for PhD applicants is Thursday, December 1, 2023 (for Fall 2024). Most of the decisions are typically finalized by the end of March, but some decisions may be made as late as April 15.

The PhD Application Feedback Program aims to assist first-generation and underrepresented students with their applications to Computer Science PhD programs in the US. An advanced PhD student, postdoctoral fellow, or CS faculty volunteer will provide one round of feedback on an applicant’s resume and statement of purpose (capacity limited). Volunteers may also arrange for a short 15 to 30-minute Zoom meeting to offer additional advice. Participation in the PhD Application Feedback Program does not guarantee admission to any program.

International PhD applicants

If you are experiencing a hardship due to a case of widespread disruption such as political unrest, a natural disaster, war etc., please review our  2022 Hardship Consideration Form. 

The purpose of this form is to allow applicants to the Northwestern Computer Science PhD program to request flexibility in application requirements and/or request an application fee waiver due to a  widespread hardship , such as a natural disaster, political unrest, etc.    The deadline to submit the form is Wednesday, December 7th.

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About Stanford Theory

As theoretical computer scientists, we seek greater understanding of fundamental computational techniques and their inherent limitations. Research includes the development and analysis of algorithms for a variety of settings and applications. Major directions include Complexity Theory, the Design and Analysis of Algorithms, Cryptography , Algorithmic Game Theory , Geometric Algorithms , algorithmic techniques for internet applications , various aspects of network and computer security , databases, design and analysis of programs and programming languages, and supporting studies in combinatorial, logical, and algebraic mathematics. Please see the research page for an overview of research in a sampling of areas, and refer to the faculty pages for specific research interests.

• Please see the Motwani lecture series in memory of our late colleague Rajeev Motwani , who has been an inspiration to generations of theory researchers.

Recurring Events

• We have Theory Lunch talks (and lunch!) Thursdays at noon.
• The Stanford Theory Seminar happens occasionally as of 2022.
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• The RAIN Seminar is dedicated to algorithms on the internet.
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TCS @ Princeton

Theory Lunch on Fridays, 12:30-13:30, Computer Science 105

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November 18th, Andrew Goldberg at Theory Lunch Title: Hub Labeling Algorithms

Current Areas of Research

• Algebraic Complexity
• Algorithmic Game Theory
• Analysis of Algorithms
• Analytic Combinatorics
• Approximation Algorithms
• Big-Data and Machine Learning
• Coding Theory
• Computational Complexity
• Computational Geometry
• Data Structures/Network Algorithms
• Graph Theory
• Information Theory
• Natural Algorithms and Dynamical Systems
• Probabilistically Checkable Proofs/UGC
• Pseudorandomness
• Semidefinite Programs

Theory of Computation at Princeton

Theoretical computer science (TCS) studies efficient algorithms and protocols, which ultimately enable much of modern computing. But even more than that, the very concept of computation gives a fundamental new lens for examining the world around us. It underlies many 20th century inventions such as cryptography, computational biology, machine learning, quantum computing, etc. An abiding interest in the power of computation has been a regular feature of life at Princeton since the times of Turing, Church, Goedel and von Neumann (all Princeton residents). The TCS group continues today to pursue research in many areas of theory, including complexity theory, algorithms, data structures, computational geometry, cryptography, machine learning and computational economics. We have close connections with faculty in other groups, including computational biology, graphics, networks and systems. Princeton is a wonderful place for TCS research. The presence of active research groups at the CS and Math depts, as well as the nearby Institute of Advanced Study (not to mention Rutgers/Dimacs half an hour away) ensures that almost every day there is an exciting talk to go to, or an exciting visitor to meet.

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Theoretical Computer Science at Boston University

Welcome to the Theoretical Computer Science (TCS) research group. Research in TCS (and in our group) encompasses diverse areas such as theory of computation, complexity, sublinear algorithms, optimization, distributed and parallel computing, data privacy, machine learning and more. The common theme behind the TCS approach to these research directions is a focus on precise models and rigorous mathematical analysis.

We hold a weekly Theory Seminar . Currently, we meet Mondays from 1:30-2:30pm simultaneously in person and over Zoom. For other events and general announcements, you can subscribe to the TCS mailing list here .

If you are interested in joining the TCS research group, potential PhD students should apply either to the Department of Computer Science or the Faculty of Computing & Data Sciences PhD programs, depending on their interests and intended faculty advisors. Inquiries about postdoc positions should be directed to faculty directly.

• Ari Karchmer won "Best Student Paper" at ITCS '24 for their work " Distributional PAC-Learning from Nisan's Natural Proofs ".
• Rathin Desai , Mark Bun , and Aloni Cohen (former BU postdoc, now at UChicago) won an "Outstanding Paper" award at ALT 2024 for their work " Private PAC Learning May be Harder than Online Learning ".
• Gavin Brown , Adam Smith , and Sam Hopkins (MIT) won the COLT 2023 Mark Fulk Award for Best Student Paper for their paper " Fast, Sample-Efficient, Affine-Invariant Private Mean and Covariance Estimation for Subgaussian Distributions ."
• Connor Wagaman was awarded a 2023 Hariri Institute Graduate Student Fellowship for pursuing innovative computationally-driven research with the potential for high impact.
• Satchit Sivakumar was named a 2023 Apple Scholar in AI/ML for his work on the foundations of privacy-preserving ML.
• Mark Bun was awarded a 2022 Sloan Research Fellowship .
• Adam Smith and collaborators were awarded the 2021 ACM Paris Kanellakis Theory and Practice Award for their work on the fundamentals of differential privacy.

Related Groups & Events

• BU Algorithms & Theory
• BU Security
• BU Cyber Alliance
• BU Statistics and Probability Seminar
• Boston-area Data Privacy
• Charles River Crypto Day
• Northeastern Theory Seminar
• Harvard Theory of Computation Seminar
• MIT Theory of Computation Colloquium
• Harvard EconCS Seminar

Recent Graduate Courses

• CS 599: The Meta-Complexity Frontier (F23)
• CS 531: Advanced Optimization Algorithms (F23)
• CS 535: Complexity Theory (F23)
• DS 574: Algorithmic Mechanism Design (F23)
• DS/CS 563: Algorithmic Techniques for Taming Big Data (S23)
• DS 657: Law and Algorithms (S23)
• DS 682: Responsible AI, Law, Ethics & Society (S23)
• CS 537: Randomness in Computing (F22)
• DS 574: Algorithmic Mechanism Design (F22)
• DS 657: Law and Algorithms (S22)
• CS 599: Mathematical Methods for Theoretical CS (S22)

Recent Alumni (PhDs and Postdocs)

• Gavin Brown (PhD '23)
• Maryam Aliakbarpour (Postdoc '21–'23)
• Marcel Neunhoeffer (Postdoc '21–'23)
• Tomislav Petrovic (PhD '22)
• Rawane Issa (PhD '22)
• Talya Eden (Postdoc '21–'22)
• Marco Carmosino (Postdoc '19–'21)
• Nicholas Spooner (Postdoc '20–'21)
• Aloni Cohen (Postdoc '19–'21)
• Eylon Yogev (Postdoc '20–'21)
• Sarah Scheffler (PhD '21)
• Jiayu Zhang (PhD '21)
• Ramesh Krishnan Pallavoor (PhD '20)
• Adrian Vladu (Postdoc '17–'20)
• Audra McMillan (Postdoc '18–'20)
• Nithin Varma (PhD '19)
• Om Thakkar (PhD '19)
• Erasmo Tani (PhD student '17–'19, transferred to University of Chicago)
• Lorenzo Orecchia (Faculty '15–'19)
• Zhenyu Liao (PhD '18)
• Hannah Flynn (MS '18)
• Jelena Diakonikolas (Postdoc '16–'18)

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Theoretical Computer Science

Theoretical computer science is the mathematical study of the capabilities and limitations of computing. The major research projects in the theoretical computer science group include the development and analysis of algorithms in a variety of models, parallel computing, computational complexity, and learning.

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Theoretical computer science.

• Algorithmic Game Theory
• Approximation and Online Algorithms
• Coding and Information Theory
• Computational Complexity
• Geometric Computing
• Graph Algorithms
• Machine Learning
• Numerical Analysis
• Programming Languages

For more information about Theoretical Computer Science research at Duke Computer Science, visit the theory group wiki .

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THEORY of COMPUTATION

TOC has undergone a number of evolutions in a short span of time. From its beginning in the 1960s as an outgrowth of mathematical logic and information theory, it evolved into a branch of mathematics where one looks at classical problems with the aesthetics of computational complexity and asks new questions concerning non-determinism, randomness, approximation, interaction, and locality. It then took a foundational role in addressing challenges arising in computer systems and networks, such as error-free communication, cryptography, routing, and search, and is now a rising force in the sciences: exact, life, and social. The TOC group at MIT has played a leadership role in theoretical computer science since its very beginning. Today, research done at the TOC group covers an unusually broad spectrum of research topics.

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Theory of Computation (TOC) is the study of the inherent capabilities and limitations of computers: not just the computers of today, but any computers that could ever be built. By its nature, the subject is close to mathematics, with progress made by conjectures, theorems, and proofs. What sets TOC apart, however, is its goal of understanding computation -- not only as a tool but as a fundamental phenomenon in its own right.

At MIT, we are interested in a broad range of TOC topics, including algorithms, complexity theory, cryptography, distributed computing, computational geometry, computational biology, and quantum computing.

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Will it hurt my chances if I state I want to study Theoretical Computer Science instead of something like AI/ML for PhD?

I will be applying to CS PhD programs coming fall. I have a BA in Mathematics right now.

I am hearing that it is harder to get into a CS PhD program if I state I want to study Theoretical Computer Science because there are less funding opportunities for the area. So if I state I want to study TCS in my SOP, I might have a hard time getting in to a program as opposed to something like Systems or AI that have lots of funding and faculty who are willing to take students. Is this true at all? I am talking about getting into a CS PhD program in the first place by indicating my interest in TCS. I am kind of worried because I do want to pursue TCS in grad school and have done undergrad research in coding theory and also number theory.

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• 3 What country is this? –  Buffy Commented Jul 10, 2022 at 22:06
• 16 It is probably true that you will have a better chance of getting in if you are willing to study AI/ML. However, if you want to study TCS, what will be the point of saying that you want to study AI/ML? –  Agnishom Chattopadhyay Commented Jul 11, 2022 at 8:06
• 2 Can only agree with @AgnishomChattopadhyay - if you are accepted into the program saying you want to do AI, would you actually do AI when accepted, or are you hoping you will be allowed to switch to TCS? –  Sabine Commented Jul 11, 2022 at 8:56
• 4 Think of graduate admissions as not only a process to be gamed strategically by the applicant, but also as a process of fitting students to the programs that suit them best and vice-versa. –  Bryan Krause ♦ Commented Jul 11, 2022 at 14:47

5 Answers 5

This is a US-based answer.

Theoretical computer science is indeed less well-funded, but it is also less popular, and the effect of this on graduate admissions is complicated. You should also consider the effect on your eventual chances for jobs after you finish your PhD.

The main effect of TCS being less well-funded is that many lower-ranked, smaller departments do not have anyone in TCS at all and have no plans of hiring anyone, because the people they can hire in TCS would not be able to get funding. If your record can only get you admitted to, say, University of Georgia, then you are out of luck; they don't have any faculty with primary interests in TCS as far as I can tell.

On the other hand, if you are competitive for admission at University of Illinois at Urbana-Champaign, then you probably need a slightly less impressive record to get admitted in TCS, because they have a lot more applicants interested in AI/ML than their faculty can advise, whereas they only have somewhat more applicants interested in TCS than faculty availability.

You should make sure you are only applying to departments with several faculty primarily interested in TCS, but if you are doing that (and competitive for admission in those departments), then mentioning an interest in TCS probably helps your application.

You should also consider the other end. Once you get a PhD, only the best researchers in the TCS will eventually get faculty jobs at research universities (or research jobs in industry), whereas merely good researchers in other areas frequently manage to get faculty jobs.

• Thanks for that answer! I am looking for faculty whose research areas are around "design and analysis of algorithms". I am aiming for mostly top 30-40 ranked CS PhD programs. Should I be fine with TCS at these schools? I see about at least 3-4 profs doing TCS (some kind of coding theory, algorithms, complexity etc) at most of these programs. –  prestonphilly Commented Jul 11, 2022 at 1:49
• You have to be a little careful here. Say a department has a professor who primarily works on network protocols, and somewhere in their work they needed to develop an algorithm to solve some network optimization problem which they couldn't find in the literature, so they write an algorithms paper that gets in some second-or-third-tier algorithms conference (or just for a journal). Now their department advertises them as working in algorithms as well as networking, but that's a bit misleading, and they're not going to be a good advisor for someone who just wants to do algorithms. –  Alexander Woo Commented Jul 11, 2022 at 2:10
• So how do I find someone who works just in algorithms? Will they be in related fields? Like graph algorithms, complexity etc. If I see a prof who does "algorithms, optimization, complexity, coding theory" etc, then can I count them as a prof who does TCS? How do I actually know who would be the right advisor? This is to, of course, mention their name in the SOP. –  prestonphilly Commented Jul 11, 2022 at 2:42
• 4 Look at their actual CV/publication list. –  Alexander Woo Commented Jul 11, 2022 at 3:00
• Thank you! Do you mind taking a look at my SOP after I am done writing it? Do you have experience reading SOPs written by CS (or TCS in particular) applicants? –  prestonphilly Commented Jul 11, 2022 at 13:37

Graduate studies are no cake-walk. You will likely be totally on your own, motivation-wise. Your advisor will advise you, but very likely it will be up to you to develop the motivation to actually get through - do not expect your advisor to pull you along.

Doing things that interest and motivate you in themselves makes a huge difference when the going gets tough. In the best case, the time will fly by and you will be sad when it's over. If instead you study something that does not really interest you, chances are you'll not get through the darker phases.

Another aspect: If I were on the committee that interviews graduate applicants, I would strongly look for people that show great interest (and somehow give me the feeling that they are able to do it, of course). Especially because that field has less funding, they will try to pick the cherries amongst the applicants. And vice versa - if you apply to a ML/AI course, and the board notices that you don't really feel like it's the most interesting thing for you, your chances of getting taken might diminish significantly.

So, do what your heart tells you. Of course, don't be blind to realities, but if you burn for TCS, then study TCS.

I am hearing that it is harder to get into a CS PhD program if I state I want to study Theoretical Computer Science because there are less funding opportunities for the area. So if I state I want to study TCS in my SOP, I might have a hard time getting in to a program as opposed to something like Systems or AI that have lots of funding and faculty who are willing to take students.

The statement about AI is not correct. In fact, AI/ML widely considered the most competitive field to apply in right now, and much more competitive than theory and systems, because there is an explosion of applicants interested in pursuing a PhD in AI/ML, with generally far fewer faculty available than can advise all those interested.

Zooming out a bit: your ability to get in to a PhD program in an area X is roughly a function of (1) your application strength in X (rec letters, profile, and interests), (2) funding in area X, (3) the number of candidates interested in X. You are very focused on (2), but (1) and (3) are probably more important, especially if you are open to being funded through TA-ships rather than RA, which typically doesn't need the faculty to have as much external funding.

Comparing between theory and systems, it's less obvious; as far as I have seen, neither theory nor systems is especially hot at the moment, so they may be roughly equivalent (for metrics (2) and (3)).

This is all besides the point, though: if you are interested in theory research, why not give yourself a chance and apply? By far the most important factor to do a successful PhD is that you are working in a field you believe in. Without a doubt, your interest and excitement at this stage will take you further than just following the money. Good luck!

Actually, in the US, you have a lot of time after entering a doctoral program with a bachelors to choose a specialty. The first years are advanced coursework leading to comprehensive exams. You get to look at both areas of specialization and faculty members who might advise you.

Whether a university is more interested in theoretical CS or hot topics like AI depends on the faculty and also on which of the faculty are looking for doctoral advisees.

I wouldn't be too concerned, but it would probably be good to keep your options open for the moment and project some flexibility in a statement of purpose. Saying that your current interests are in more theoretical aspects is almost certainly fine.

Note that acceptance in the US is broad based. A single thing, especially a statement of interest in theory, is likely to have very little weight compared to things like GPA, letters of recommendation, specific courses, and such.

Also note that funding is less for many theoretical fields is less because less is required. If you don't need expensive labs/equipment then there are few funding requirements. Most students in such fields are supported as teaching assistants, which provide a service to the university and are adequately funded. TCS and math are similar in this regard. Even in physics, some theoretical aspects require little if any funding beyond salary. Einstein didn't require an experimental setup to come up with special relativity. (IIRC he was terrible at experimentation.)

Don't overthink it.

• So overall, it wouldn't be a problem if I wanna study Theoretical CS (algorithms, graphs, complexity etc) as opposed to ML/AI? It wouldn't hurt my chances of getting in, right? –  prestonphilly Commented Jul 11, 2022 at 14:18
• Generally, not a problem. But specific programs might have specific needs, so not a universal. The US is pretty flexible about such things at your stage. –  Buffy Commented Jul 11, 2022 at 14:20

I am a TCS grad student. Yes, it is noticeable that there is much more funding for ML/AI right now. My advice is to do (or at least try) what interests you. However, I will point you to another field that recently seems to have a hype going on and might be an option for you -- Quantum Computing.

This is not my area of research and so I am not that much into it, and it is definitely a broad field. But in certain areas of QC there is much of TCS involved -- BQP; QMA; the recent result MIP* = RE; Quantum fine-grained complexity, ...

As said, I do not know much about this field, but from what I do know your math background might be beneficial here -- Hilbert spaces, Unitary Matrices, the Spectral Theorem, all stuff that pops up there (to be honest in a finite-dimensional toy variant most of the time, but anyway, depends on how much you want to delve in) and you probably already have learnt (some of) this stuff as a mathematician.

As far as I heard, it is not expected that this QC hype ends soon. Companies are already establishing use cases, consulting companies evaluating applications and in general it is expected to have an impact on industry in the near future (some say next 10 years, some say next 30 years). Furthermore, there is some kind of a national competition between the US and China on quantum supremacy (and also Europe is investiging a lot recently, but as far as I know they buy their hardware in the US).

• So if you are interested in studying algorithms, complexity theory, is it harder for you to be admitted into the program in the first place? (going straight from an undergrad) –  prestonphilly Commented Jul 11, 2022 at 14:04
• To be honest I do not know anything about specific QC PhD programs, I just wanted to help you broaden your perspective and show you another option. Some universities that are into QC (where you might find further information): Texas Austin, University of Waterloo, QuTech (at Delft University), MCQST (Munich) -- just a few (big?) names that came to my mind. There are probably much more, but that should give you a start. I am really not into this community, I am just an outsider who tries to keep up. –  StefanH Commented Jul 11, 2022 at 14:13
• Thank you! But I wanted to ask you about TCS in general, not QC. Lets say I wanna work with professors specializing in designing algorithms, studying computational complexity, coding theory etc. Will I have a harder time getting in if I state I am interested in these fields, since they are typically less funded? –  prestonphilly Commented Jul 11, 2022 at 14:17
• 1 I do not think so, it might get harder after the postdoc phase maybe. But there are a lot of professors and institutions doing research related to what you have mentioned. –  StefanH Commented Jul 11, 2022 at 14:25

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How do I get started in theoretical CS ?

I'm a freshmen studying computer science and I already know that I want to go into academia with focus of theoretical comp sci. I already read some of papers referenced in this question and this question convinced me further.

What should I be doing now , as an undergrad, to get involved in the field? What can I do to prepare for research in the field?

• soft-question
• advice-request
• 8 $\begingroup$ This isn't really significant enough to post as an answer, but consider picking up a copy of Michael Sipser's "Introduction to the Theory of Computation," too. Even if you don't have time for self-study with that book, it's a great reference if there's anything you need to look up that isn't clearly explained on Wikipedia. $\endgroup$ –  user1338 Commented Dec 20, 2010 at 3:23
• 2 $\begingroup$ I don't know how the procedure works, but I'd love to see this as community wiki. $\endgroup$ –  chazisop Commented Dec 20, 2010 at 4:17
• 3 $\begingroup$ flag the question for moderator attention and we'll deal with it. $\endgroup$ –  Suresh Venkat Commented Dec 20, 2010 at 13:37
• 2 $\begingroup$ @Good Person: OP can not make a question CW, only moderators can. $\endgroup$ –  Kaveh Commented Dec 21, 2010 at 5:29
• 2 $\begingroup$ @Good Person: as Kaveh mentions, original posters can't make posts CW anymore (and neither can anyone with edit privileges). Only mods can do this. no offense was intended. $\endgroup$ –  Suresh Venkat Commented Dec 21, 2010 at 6:04

7 Answers 7

Let me provide an answer from the other side. I've had a few undergraduate student researchers work with me. The experience has been mixed: with some, I have published papers and have work in progress, and with others, we never really got off to any kind of start.

It's great that you know what you want to do. As an undergraduate, here's what you should be focusing on:

• Building the mathematical "muscles" that will help you when you start working on problems in earnest
• Exploring different aspects of theoretical CS to get a sense of the area and figure out what kinds of problems/areas you find interesting
• (depending on the area) working out some puzzles, maybe solving some exercises, and working your way up to a research question.

Find a professor to guide you, and PUT IN THE TIME ! The hardest thing you'll face is creating the open time to think about problems in the midst of classwork, assignments and exams. But you need to reserve blocks of time for your independent study and research otherwise it will be very hard to make any kind of progress. How you do this is upto you: maybe you can find a professor to meet with you once a week and set intermediate goals for you, or maybe you can set a long term goal (working through X exercises from a text) and work steadily on that.

• 5 $\begingroup$ Nice answer! "you need to reserve blocks of time for your independent study": this remembers me of my own experience. I used to be a lazy math student, then in master I discovered a nice TCS problem (centered around the busy beaver function). I started working on my free time on it with guidance by one of my prof. I never found something interesting on the problem, but 12 years later, I wrote more than 15 papers with him (but he never was my phd/master advisor, only an "external" mentor). $\endgroup$ –  Sylvain Peyronnet Commented Dec 20, 2010 at 18:06
• 1 $\begingroup$ Suresh's answer is great! To the OP: Building mathematical "muscles" means getting used to the language and tools of math (combinatorics, graph theory, analysis etc) and also getting used to prove theorems! (And this is the really exciting part! :-) $\endgroup$ –  Jay Commented Dec 25, 2010 at 9:12

I'm currently a PhD student and not a prof, so my suggestion comes from my (limited) personal experience as a graduate student.

When I was an undergraduate student, I always worked as a research assistant in summer with different profs in my department. I personal believe that the only way to figure out if TCS is truly for you or not is to work on concrete problems and see what you can enjoy the most. It did take me quite a while to find a prof and a topic that I liked. There's also a "social" aspect in research, and different profs have different working and supervision habits, and thus these summer research jobs will give you a better idea what quality you want the most from a supervisor in the future.

There are many interesting fields in Computer Science, and TCS is just one of them. So it's always best to keep your options open and talk to different profs. It's very important to specialize when you're doing PhD, but as as an undergrad I think Mark Braverman 's advice is extremely relevant:

" Try to learn as much as you can. [...] It is more difficult later on!"

[Mark tried to enroll in many courses (well above the limit), and explore different areas of Mathematics and Computer Science when he was an undergrad.] Try to attend lectures and seminars on different topics in your department. When you're in your upper years, you should also ask for permission to audit graduate courses related to your interest.

Also depending if you're majoring in Maths or CS, you also have to plan courses you should take to prepare you a solid basic foundation. If you're a Maths undergrad, then you should take more CS courses in algorithms and complexity which give you a more "algorithmic" mind. If you're a CS or Engineering undergrad, then it's always a good idea to learn some basic Maths courses in:

• Combinatorics
• Probability and Statistics
• Advanced Linear Algebra
• Abstract Algebra

It's true that you can never learn enough Maths, and that you should learn to pick up new Maths/methods/techniques fast whenever needed. But a solid background will definitely give you an easier start into TCS.

I wish you the best of luck and success!

• 6 $\begingroup$ +1: nice answer. I will add that taking/auditing non-theoretical CS or non-CS courses is also a good thing, it open ups your mind for possible problems to work on, you never know where a nice theory question shows up, so keep your eyes open. :) $\endgroup$ –  Kaveh Commented Dec 20, 2010 at 6:33
• $\begingroup$ Would you recommend "Advanced Linear Algebra" by Steve Roman for graduate self-study? $\endgroup$ –  Jacob Commented Mar 6, 2011 at 7:20
• 2 $\begingroup$ @Jacob: Roman's book is certainly recommended by many people. It is really advanced and covers nice topics like modules, convexity, affine geometry, tensor products, etc. But the bottom line is you should always choose something that is suitable for your taste and background! So scanning through a bunch of books in the library before deciding is always a good idea (at least for me). $\endgroup$ –  Dai Le Commented Mar 6, 2011 at 10:21

As a freshman undergraduate, your best bet is to express this interest to your professors in the CS department, who can help you out (providing this help a big part of their job!). Most of them would, I would expect, be delighted to help out an undergraduate who was interested in the same things they are. At the very least, they can give you good advice about what classes to take at your institution, and advice that is customized to your situation.

Lots of other folks seem to be giving various pieces of good advice. To add to the wisdom, let me say this: There is no such thing as knowing too much math (unless you decide you want to do pure math instead). Seriously, know your analysis, combinatorics, algebra, maybe some rep theory, alg top, and so forth. It makes being widely read across areas of CS theory much much much easier :)

• 2 $\begingroup$ excellent advice. I tell this to all undergrads who come talk to me as well. $\endgroup$ –  Suresh Venkat Commented Dec 23, 2010 at 6:14

You've made a great first step! I'll second pboothe about talking with a professor in your department. If you are interested in theory, find whoever is teaching the theory courses and talk with them. They may have some problems that don't require too much background to get started on. In my personal experience, there are more problems in graph theory and combinatorics that are more approachable than theory, but still build the same research skills. Don't be afraid of your math department!

It may also help to start getting involved in the community, especially by asking and answering questions here. It would help to have your username be your own name so we know who you are.

I'm an almost graduate student. So the answers to your question are also interesting for me, but perhaps my little personal experience can help.

Here is my list (in random order) of suggestions what you can do:

• First of all read as many papers/books/lecture notes as you can find. But it is also important to apply your knowledge to exercises and later to open problems! Some of my tutor always says: Not only your muscles need continous training :)
• Read/Ask/Answer questions on this site (or some other related SE site) is a good way to learn how to work with research topics in TCS or math.
• I try to read a lot of TCS blogs from well-known researcher. The topics cover both current and historical results.
• $\begingroup$ Would you have any links to those blogs? $\endgroup$ –  SH7890 Commented Mar 30, 2018 at 3:02

I think the most important thing is to explore as widely as possible to learn what aspects of TCS really excite you. In the course of this exploration you might find that the problems that intrigue you the most are the intersections of TCS with other fields (e.g.economics) or applications of TCS (e.g. to computer networks) or TCS topics which are also part of other areas in CS like computational or statistical learning theory. Heck, it is even possible you might change your major to math or physics or something related if your interests take you in that direction.

My point is that as a freshman you really have an opportunity to explore widely with less pressure than you would as a grad student or professor. And to not be alarmed if you don't end up in the "usual suspect" TCS topics.

Of course it would be ideal if you go "deep" in some area, learn solid techniques and publish great results.

And if you end up becoming an expert in a hot new field before it becomes mainstream that will probably be great for your career.

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TCS Graduate Programme

Please click here for more information on  2022 interview details, please click here for more information on  2024 interview details, graduate programme in theoretical computer science.

The normal route to the IMSc graduate research program is through an interview, and JEST ( Joint Entrance Screening Test )  is one of the means by which we screen students and select them for interview. The graduate program consists of two streams: the PhD and the Integrated PhD programme for Theoretical Computer Science in IMSc.

What is JEST ?

The Joint Entrance Screening Test in Theoretical Computer Science is a test conducted across the country each year, usually on a Sunday in the month of February. However, in 2021 it was held in April and 2022 in March.

What is the eligibility for appearing for JEST?

Anyone with a Bachelor's degree in CS or related disciplines, or an M.Sc./ M.E./ M.Tech./M.C.A. in CS or related disciplines, interested in the mathematical aspects of computer science is eligible to appear for JEST. Students in the final year of these courses are also eligible to appear for JEST. Examples of related disciplines are mathematics, statistics, information technology, etc.

What are the eligibility criteria for admission to Ph.D. and Integrated Ph.D. programme?

Students with a Master's degree (such as, M.Sc./M.E./M.Tech.) in CS or related disciplines are usually admitted to the PhD program. However, using background information, performance in the admission process, and other relevant information, the student may be advised to credit more courses (the courses can be at the Integrated Ph.D. level). The student should also meet the admission requirements for the PhD program given by HBNI.

Students having a B.Sc./B.E./B.Tech./M.C.A. in CS or related disciplines are admitted to the Integrated Ph.D. program. This means that they pick up a Masters' degree after two years in the graduate school, and a PhD degree on completion of the graduate school. The student should also meet the  admission requirements  for the Integrated PhD program given by HBNI.

Where can I write the JEST exam?

Please see the list of centers .

Can I do a PhD part-time with my job?

No, we only admit full-time students at IMSc. However, if you register for PhD with some university, there are possibilities of research collaboration with IMSc. Please see our page on the Graduate Visitor Programme .

Can I use a GATE score instead of writing JEST in Theoretical Computer Science?

No. We use the GATE score as additional information.

Are past JEST question papers for TCS available?

See the Downloads section on https://www.jest.org.in/

Are sample JEST questions for TCS available?

See the Downloads section on https://www.jest.org.in/ .

What material is covered by the JEST questions for TCS?

What is the structure of the JEST examination for TCS?

It is a three-hour written test. The question paper consists of two parts:

• Part A is multiple-choice to be marked on the answer card provided. The answer card is collected by the invigilator at the end of 75 minutes of the exam. There are 25 questions of 3 marks each. Each correct answer carries 3 marks, each incorrect answer carries -1 mark. Unattempted questions fetch 0 marks.
• Part B requires short answers to be written on the space provided in the paper. There are around 9 questions, some are 8 marks and some are 12 marks.

How is the evaluation done?

• Part A is evaluated mechanically. Part A scores will be available at the JEST website . IMSc decides a first cutoff. Only for students who meet this first cutoff, Part B of the answer-book will be evaluated.
• The Part B scores are not declared. IMSc decides a second cutoff based on the performance in both parts.
• Students meeting this second cutoff are called for an interview to Chennai.
• Interview dates are usually during the last week of April and the first week of May.

What should I do to increase my chances of getting selected?

Here are some criteria which were followed by the TCS JEST examiners in recent years.

The first cutoff selects the top students among the Part A scores. IMSc chooses the cutoff depending on the number of students taking the examination and the number of answer-books for which Part B is to be evaluated (manually). For example,   in 2020 we chose the cutoff score to get the top 196 students in Part A .

• Among the top students with Part A scores, those who get low Part B scores are rejected.
• The top few among the Part B scores are selected. IMSc chooses this second cutoff depending on the number of students to be called to the interviews (to be done in a limited amount of time).
• Apart from this, we look at answers of candidates who top-score on individual questions in Part B (if very few candidates overall do well on a question they have an advantage), or if the answers are ingenious or meticulous enough to deserve extra credit. In 2020 we were able to call around 25 students for TCS interviews.

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New Horizons in Theoretical Computer Science

Important dates:

For full consideration, apply by April 21, 2023

School will take place (online) on June 12 to June 16, 2023

See application form below.

New horizons in theoretical computer science is a week-long online summer school which will expose undergraduates to exciting research areas in the area of theoretical computer science and its applications. The school will contain several mini-courses from top researchers in the field. The course is free of charge,and we welcome applications from undergraduates majoring in computer science or related fields. We particularly encourage applications from students that are members of groups that are currently under-represented in theoretical computer science .

Please contact [email protected] for more information. Please also see this (regularly updated) FAQ page .

Organizers:

The summer course is organized by the Committee for the Advancement of Theoretical Computer Science (CATCS) of the ACM Special Interest Group on Algorithms and Computation Theory (SIGACT) . We are grateful for support from SIGACT and the Toyota Technical Institute at Chicago .

We are looking for teaching assistants! Please fill out this form if you are interested.

Applying to the school

The course is intended for currently enrolled undergraduate students that are majoring in computer science or related fields. Students will be expected to be familiar with the material typically taught in an introductory algorithms and discrete mathematics / mathematics for computer science courses. If you are unsure if you are prepared for the course, please write to us at [email protected] . We particularly encourage applications from students that are members of groups that are currently under-represented in theoretical computer science.

Application form:

To apply to the summer school, please fill out this Google form (URL: https://forms.gle/ssVoomzTFaXDwRnf7 ).

To ensure full consideration, please fill out the form no later than April 21, 2023 . Please mention one reference in your form. References do not need to write a full letter, but rather a short (one paragraph) email to [email protected] will suffice. The letter should have the subject “Reference for [full name]”.

Decisions will be communicated to applicants who applied by the deadline no later than May 08, 2023 .

Instructors:

We will update the list of instructors as we get more confirmations:

The course will take place June 12 to June 16, 2023 . A detailed program is available here .

You can also look at the past year’s webpage and program to get an idea of what to expect during the summer school.