theory of computation research papers

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Theory of Computation (TOC) studies the fundamental strengths and limits of computation, how these strengths and limits interact with computer science and mathematics, and how they manifest themselves in society, biology, and the physical world.

At its core, TOC investigates tradeoffs among basic computational resources. These resources include computation time, space, communication, parallelization, randomness, quantum entanglement, and more. As computational systems come in many forms and the goals of computation are diverse, TOC studies the limits of computation in its many manifestations. These are determined by what access we have to the computation’s input: do we have access to it as a whole, or does it come as a stream; as samples from a distribution; in encrypted form; or in fragments? Limits are also determined by the environment within which the computation takes place. Beyond the architecture and connectivity of the computational environment determining where the data is produced and stored and where the computation takes place, we are interested in the presence of other forces, such as adversaries who might want to eavesdrop on the computation, or strategic parties who want to influence the computation to their benefit.

Moreover, computation takes place both in systems that are explicitly computational but also systems that are not explicitly computational, such as biological systems, the human brain, social networks, and physical systems. As such, TOC provides a scientific lens with which to study such systems, and the study of these systems motivates new models of computation and computational tradeoffs, to be studied in turn by TOC. 

MIT’s TOC faculty research an unusually broad spectrum of both core TOC and interdisciplinary topics, including algorithms, optimization, complexity theory, parallel and distributed computing, cryptography, computational economics and game theory, computational algebra and number theory, computational geometry, quantum computation, computational biology, machine learning, statistics, and numerical computation.

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The dedicated teacher and academic leader transformed research in computer architectures, parallel computing, and digital design, enabling faster and more efficient computation.

QS ranks MIT the world’s No. 1 university for 2024-25

Ranking at the top for the 13th year in a row, the Institute also places first in 11 subject areas.

Department of EECS Announces 2024 Promotions

Adam Belay, Manya Ghobadi, Stefanie Mueller, and Julian Shun are all being promoted to associate professor with tenure.

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Theory of Computing Systems (TOCS) is devoted to publishing original research from all areas of theoretical computer science, ranging from foundational areas such as computational complexity, to fundamental areas such as algorithms and data structures, to focused areas such as parallel and distributed algorithms and architectures.

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Jumping automata over infinite words.

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String Attractors of Some Simple-Parry Automatic Sequences

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On the Solution Sets of Three-Variable Word Equations

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Near-Optimal Auctions on Independence Systems

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Max-plus Algebraic Description of Evolutions of Weighted Timed Event Graphs

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Theory and computation articles from across Nature Portfolio

Accelerating predictions of electronic transport and superconductivity

By developing a machine learning framework, a recent study substantially accelerates the calculation of electron–phonon coupling, making it computationally feasible to predict and understand a range of important physical phenomena, including electronic transport, hot-carrier relaxation, and superconductivity in complex materials.

Related Subjects

• Atomistic models
• Coarse-grained models
• Computational methods
• Electronic structure
• Scaling laws

Latest Research and Reviews

Learning grain boundary segregation behavior through fingerprinting complex atomic environments

Describing site-specific segregation in multi-species materials is a computationally complex task that typically requires model simplification, at the expense of atomic accuracy, or limitation to small samples. Here, the relationships between local atomic environments at grain boundaries and their segregation energies are investigated by developing suitable machine learning atomic descriptors.

• Jacob P. Tavenner
• Ankit Gupta
• Garritt J. Tucker

Machine learning driven performance for hole transport layer free carbon-based perovskite solar cells

• Sreeram Valsalakumar
• Shubhranshu Bhandari
• Senthilarasu Sundaram

Hydrogen adsorption on fcc metal surfaces towards the rational design of electrode materials

• Cláudio M. Lousada
• Atharva M. Kotasthane

Deep learning for symmetry classification using sparse 3D electron density data for inorganic compounds

• Seonghwan Kim
• Byung Do Lee
• Kee-Sun Sohn

A new method for identifying elastic parameters of isotropic materials based on square specimens

• Longxin Zhang
• Wenbin Zhang

Bayesian optimization acquisition functions for accelerated search of cluster expansion convex hull of multi-component alloys

• Dongsheng Wen
• Victoria Tucker
• Michael S. Titus

News and Comment

Metamaterials design via and for computation

This issue of Nature Computational Science features a Focus that highlights recent advancements, challenges, and opportunities in computational models for metamaterials design and manufacturing, as well as explores their potential promises in emerging information processors and computing technologies.

Don’t flock to faulty AI fashion

• Mark Buchanan

A machine learning tool to efficiently calculate electron–phonon coupling

A machine learning framework that uses atomic orbital-based Hamiltonian matrices and gradients predicted by an equivariant graph neural network is established to calculate electron–phonon coupling (EPC). This approach accelerates the calculations by several orders of magnitude, enabling EPC-related properties to be predicted for complex systems using highly accurate functionals.

Boosting graph neural networks with virtual nodes to predict phonon properties

A graph neural network using virtual nodes is proposed to predict the properties of complex materials with variable dimensions or dimensions that depend on the input. The method is used to accurately and quickly predict phonon dispersion relations in complex solids and alloys.

Underground quantum criticality is hot right now

• Richard Brierley

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The theory of computing is the study of efficient computation, models of computational processes, and their limits. Research at Cornell spans all areas of the theory of computing and is responsible for the development of modern computational complexity theory, the foundations of efficient graph algorithms, and the use of applied logic and formal verification for building reliable systems. In keeping with our tradition of opening new frontiers in theory research, we have emerged in recent years as a leader in exploring the interface between computation and the social sciences.

In addition to its depth in the central areas of theory, Cornell is unique among top research departments in the fluency with which students can interact with faculty in both theoretical and applied areas, and work on problems at the critical juncture of theory and applications.

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Also of interest are how these strengths and limitations manifest themselves in society, biology, and the physical world.

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Eyebrowse: social and public web browsing, is the casino using a riffle shuffle, nick gravin, understanding neural networks in the brain, leader election in the sinr model with arbitrary power control, magnus halldorsson, stephan holzer, using reductions to understand polynomial time algorithms, mavo: creating interactive data-driven web applications by authoring html, algebraic techniques for algorithm design, approximating the diameter of a directed graph, sketching distances in graphs, spatial data structure parallel merging, michael coulombe, sublinear/streaming algorithms for covering problem, sublinear algorithms for massive data problems, basing cryptography on structured hardness, splinter: practical private queries on public data, reconstructing neural circuits from mammalian brain, performance engineering of cache profilers, algorithmic aspects of performance engineering, data garbling: computing on encrypted data, efficient robust estimation in high dimensions, covering all k-mers using joker characters, matrix permanents and linear optics, wikum: bridging discussion systems and wikis with collective summarization, squadbox: combating online harassment using friendsourced moderation, multi-core data structures, distributed computation in ant colonies, towards context-aware functional genomics,   24 more.

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Theory of Computation

The Theory Group at the University of Michigan conducts research, using the emphasis on mathematical technique and rigor typical of theoretical computer science, across many areas such as combinatorial optimization, data structures, cryptography, quantum computation, parallel and distributed computation, algorithmic game theory, graph theory, geometry, combinatorics, and energy efficiency. We investigate the value of tradeoffs among fundamental resources such as running time, storage space, randomness, communication, and energy, in both the classical and quantum senses. 

Theory faculty and students work with others from the division, as well as faculty from Mathematics, Electrical and Computer Engineering, Industrial and Operations Engineering, Atmospheric, Oceanic, and Space Science, and elsewhere in the University.

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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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Theory of computation is the branch of theoretical computer science and mathematics that deals with how efficiently problems can be solved on a model of computation, using an algorithm.

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