#### Research Group

## Algorithms Group

We devise new mathematical tools to tackle the increasing difficulty and importance of problems we pose to computers.

- Research Areas
- Impact Areas

31 Group Results

We devise new mathematical tools to tackle the increasing difficulty and importance of problems we pose to computers.

We design software for high performance computing, develop algorithms for numerical linear algebra, and research random matrix theory and its applications.

The MIT Center for Deployable Machine Learning (CDML) works towards creating AI systems that are robust, reliable and safe for real-world deployment.

We focus on finding novel approaches to improve the performance of modern computer systems without unduly increasing the complexity faced by application developers, compiler writers, or computer architects.

Our interests span quantum complexity theory, barriers to solving P versus NP, theoretical computer science with a focus on probabilistically checkable proofs (PCP), pseudo-randomness, coding theory, and algorithms.

Our lab focuses on designing algorithms to gain biological insights from advances in automated data collection and the subsequent large data sets drawn from them.

Our mission is fostering the creation and development of high-performance, reliable and secure computing systems that are easy to interact with.

Our group’s goal is to create, based on such microscopic connectivity and functional data, new mathematical models explaining how neural tissue computes.

We develop techniques and tools that exploit automated reasoning and large amounts of computing power to tackle challenging programming problems

This community is interested in understanding and affecting the interaction between computing systems and society through engineering, computer science and public policy research, education, and public engagement.

We seek to develop techniques for securing tomorrow's global information infrastructure by exploring theoretical foundations, near-term practical applications, and long-range speculative research.

We conduct research on all areas of database systems and information management.

We are investigating decentralized technologies that affect social change.

Our group studies geometric problems in computer graphics, computer vision, machine learning, optimization, and other disciplines.

We are an interdisciplinary group of researchers blending approaches from human-computer interaction, social computing, databases, information management, and databases.

We work at the intersection of human computer interaction and personal fabrication tools.

We investigate language in different contexts: from how it is learned, to how it is grounded in visual perception, all the way to how machines can readily interact with humans.

Our mission is to work with policy makers and cybersecurity technologists to increase the trustworthiness and effectiveness of interconnected digital systems.

We develop techniques for designing, implementing, and reasoning about multiprocessor algorithms, in particular concurrent data structures for multicore machines and the mathematical foundations of the computation models that govern their behavior.

We develop innovative approaches for building software and for solving problems in modern parallel and distributed software systems.

Our research interests center around the capabilities and limits of quantum computers, and computational complexity theory more generally.

Our goal is to find better ways to make software, and ways to make software better.

Our goal is to create technology that makes it possible for everyone in the world to interact with with computers via natural spoken language.

38 Project Results

We aim to develop a systematic framework for robots to build models of the world and to use these to make effective and safe choices of actions to take in complex scenarios.

The project concerns algorithmic solutions for writing fast codes.

Alloy is a language for describing structures and a tool for exploring them. It has been used in a wide range of applications from finding holes in security mechanisms to designing telephone switching networks. Hundreds of projects have used Alloy for design analysis, for verification, for simulation, and as a backend for many other kinds of analysis and synthesis tools, and Alloy is currently being taught in courses worldwide.

Self-driving cars are likely to be safer, on average, than human-driven cars. But they may fail in new and catastrophic ways that a human driver could prevent. This project is designing a new architecture for a highly dependable self-driving car.

Our goal is to develop a socially intelligent team coacher agent that helps humans communicate, strategize, and work together efficiently.

Automatic speech recognition (ASR) has been a grand challenge machine learning problem for decades. Our ongoing research in this area examines the use of deep learning models for distant and noisy recording conditions, multilingual, and low-resource scenarios.

We study the fundamentals of Bayesian optimization and develop efficient Bayesian optimization methods for global optimization of expensive black-box functions originated from a range of different applications.

Traffic is not just a nuisance for drivers: It’s also a public health hazard and bad news for the economy.

This project aims to design parallel algorithms for shared-memory machines that are efficient both in theory and also in practice.

Developing techniques to allow self-driving cars and other AI-driven systems to explain behaviors and failures.

We plan to develop a suite of graph compression and reordering techniques as part of the Ligra parallel graph processing framework to reduce space usage and improve performance of graph algorithms.

Our goal is to design novel data compression techniques to accelerate popular machine learning algorithms in Big Data and streaming settings.

Our goal is to develop new applications and algorithms that leverage the skills of distributed crowdworkers, notably for image and video processing applications.

Data scientists universally report that they spend at least 80% of their time finding data sets of interest, accessing them, cleaning them and assembling them into a unified whole.

Historically, DBMSs in the warehouse space partitioned their data across a shared nothing

cluster.

cluster.

Wikipedia is one of the most widely accessed encyclopedia sites in the world, including by scientists. Our project aims to investigate just how far Wikipedia’s influence goes in shaping science.

Our goal is to investigate deterministic algorithms for robotic task and motion planning.

To further parallelize co-prime sampling based sparse sensing, we introduce Diophantine Equation in different algebraic structures to build generalized lattice arrays.

With strong relationship to generalized Chinese Remainder Theorem, the geometry properties in the remainder code space, a special lattice space, are explored.

With strong relationship to generalized Chinese Remainder Theorem, the geometry properties in the remainder code space, a special lattice space, are explored.

We aim to understand theory and applications of diversity-inducing probabilities (and, more generally, "negative dependence") in machine learning, and develop fast algorithms based on their mathematical properties.

Eyebrowse aims to create a social outdoors for your web browsing.

Developing state-of-the-art tools that process 3D surfaces and volumes

We are designing new parallel algorithms, optimizations, and frameworks for clustering large-scale graph and geometric data.

The creation of low-power circuits capable of speech recognition and speaker verification will enable spoken interaction on a wide variety of devices in the era of Internet of Things.

42 People Results

Graduate Student

Director, Project on Technology, Economy & National Security

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31 News Results

Mueller named 2020 Microsoft Research Faculty Fellow

Wireless system helps Boston retirement home care for COVID patients while reducing risk of contagion

Research aims to make it easier for self-driving cars, robotics, and other applications to understand the 3D world.

Computer Science and Artificial Intelligence Laboratory team creates new reprogrammable ink that lets objects change colors using light.

New capabilities allow “roboats” to change configurations to form pop-up bridges, stages, and other structures.

MIT system “learns” how to optimally allocate workloads across thousands of servers to cut costs, save energy.

Professor Adam Chlipala builds tools to help programmers more quickly generate optimized, secure code.

Fleet of “roboats” could collect garbage or self-assemble into floating structures in Amsterdam’s many canals.

Speakers — all women — discuss everything from gravitational waves to robot nurses

New architecture promises to cut in half the energy and physical space required to store and manage user data.

Workshop explores technical directions for making AI safe, fair, and understandable

Algorithm could help autonomous underwater vehicles explore risky but scientifically-rewarding environments.

Last week MIT’s Institute for Foundations of Data Science (MIFODS) held an interdisciplinary workshop aimed at tackling the underlying theory behind deep learning. Led by MIT professor Aleksander Madry, the event focused on a number of research discussions at the intersection of math, statistics, and theoretical computer science.

Model identifies instances when autonomous systems have learned from examples that may cause dangerous errors in the real world.

Cambridge Mobile Telematics Raises $500M from SoftBank Vision Fund

CSAIL’s approach uses algorithms and “2.5-D” sketches to let computers visualize images from any perspective

In simulations, robots move through new environments by exploring, observing, and drawing from learned experiences.

MIT professor discusses using paper-folding for applications in manufacturing, medicine, and robotics

Algorithm computes “buffer zones” around autonomous vehicles and reassess them on the fly.

Harini Suresh, a PhD student at MIT CSAIL, studies how to make machine learning algorithms more understandable and less biased.

CSAIL's NanoMap system enables drones to avoid obstacles while flying at 20 miles per hour, by more deeply integrating sensing and control.

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.

New CSAIL work shows that traffic would flow faster if drivers kept an equal distance between cars

Neural networks, which learn to perform computational tasks by analyzing huge sets of training data, have been responsible for the most impressive recent advances in artificial intelligence, including speech-recognition and automatic-translation systems.