The goal of the Theory of Computation CoR is to study the fundamental strengths and limits of computation as well as how these interact with mathematics, computer science, and other disciplines.
Also of interest are how these strengths and limitations manifest themselves in society, biology, and the physical world.
Our goal in this project is to understand how one can test if a particular dealer's shuffles follow a certain pattern. We have developed a theoretical framework for the same and wish to understand its performance in practice.
For many problems the best known algorithms take polynomial time but super linear time, we want to understand why problems like diameter, longest common sub-sequence and co-linear point detection can't be solved in linear time.
Mavo is a language that lets anyone turn a static HTML document into a fully functioning reactive web application with data presentation, editing, storage and lightweight computation, all without writing a single line of Javascript or other programming code.
We work on improving the algorithms for algebraic problems like matrix multiplication, and using these to design algorithms for fundamental non-algebraic problems.
There is a family of approximation algorithms for computing the diameter of an undirected graph that give a time/accuracy trade-off and our goal is to extend these results to directed graphs.
We are creating a concurrent data structure for 2D/3D points that supports efficient storage, range queries, and merging of disjoint datasets, motivated by highly-parallel algorithms for reconstructing neuron connections in the brain.
We aim to base a variety of cryptographic primitives on complexity theoretic assumptions. We focus on the assumption that there exist highly structured problems --- admitting so called "zero-knowledge" protocols --- that are nevertheless hard to compute
We develop algorithms, systems and software architectures for automating reconstruction of accurate representations of neural tissue structures, such as nanometer-scale neurons' morphology and synaptic connections in the mammalian cortex.
Our goal is to develop lightweight tools that allow programmers to better understand the cache performance of their applications. Tasks include designing profilers, performance engineering existing ones, and exploring different metrics for cache interactions.
We are developing robust estimators for multivariate distributions which are both computationally efficient and near-optimal in terms of their accuracy. Our focus is on methods which are both theoretically sound and practically effective.
We build tools to allow a community of people to collectively summarize large discussions online and manage knowledge embedded within these discussions.
We are interested in applying insights from distributed computing theory to understand how ants and other social insects work together to perform complex tasks such as foraging for food, allocating tasks to workers, and choosing high quality nest sites.
We aim to develop a context-aware data-driven functional genomics framework that can characterize tissue-specific gene representations, provide context-aware genotype to phenotype mapping, and enable network-based exploration of disease genetics.
JuliaHub, MIT and WashU developers unveil major update to Julia programming language: a more nimble experience that may tilt the balance towards Julia for more users.
The American Academy of Arts & Sciences recently elected MIT EECS professor, CSAIL member, and co-director of Foundations of Data Science Institute (FODSI) Piotr Indyk as one of their members.
The Association for Computing Machinery (ACM) recently awarded Yael Tauman Kalai, MIT Department of Electrical Engineering and Computer Science (EECS) adjunct professor, CSAIL member, and Senior Principal Researcher at Microsoft Research with the 2022 ACM Prize in Computing for her cryptography research.
The John Simon Guggenheim Memorial Foundation recently awarded MIT EECS Professor and CSAIL principal investigator Ronitt Rubinfeld with a 2023 fellowship for her exceptional computer science research.
MIT professor and CSAIL principal investigator Constantinos Daskalakis is one of six researchers with ties to MIT recognized by ACM for significant contributions to computing systems.
New research enables users to search for information without revealing their queries, based on a method that is 30 times faster than comparable prior techniques.
New technique significantly reduces training and inference time on extensive datasets to keep pace with fast-moving data in finance, social networks, and fraud detection in cryptocurrency.
MIT CSAIL PhD students recently received the award as part of a team that includes Argonne National Lab and Technical University of Munich researchers.
MIT professor and CSAIL principal investigator Peter Shor recently received the 2023 Breakthrough Prize in Fundamental Physics for his pioneering work in the field of quantum information.
Studying a powerful type of cyberattack, researchers identified a flaw in how it’s been analyzed before, then developed new techniques that stop it in its tracks.
This week it was announced that MIT professors and CSAIL principal investigators Shafi Goldwasser, Silvio Micali, Ronald Rivest, and former MIT professor Adi Shamir won this year’s BBVA Foundation Frontiers of Knowledge Awards in the Information and Communication Technologies category for their work in cryptography.
Last week CSAIL principal investigator Shafi Goldwasser spoke about cryptography and privacy as part of the annual congressional briefing of the American Mathematical Society (AMS) and the Mathematical Sciences Research Institute (MSRI).
