#### 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

26 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.

This CoR aims to develop AI technology that synthesizes symbolic reasoning, probabilistic reasoning for dealing with uncertainty in the world, and statistical methods for extracting and exploiting regularities in the world, into an integrated picture of intelligence that is informed by computational insights and by cognitive science.

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

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 are investigating decentralized technologies that affect social change.

We aim to develop the science of autonomy toward a future with robots and AI systems integrated into everyday life, supporting people with cognitive and physical tasks.

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.

The Interactive Robotics Group aims to imagine the future of work by designing collaborative robot teammates that enhance human capability.

We conduct interdisciplinary research aimed at discovering the principles underlying the design of artificially intelligent robots.

Our projects are centered around the problems of navigation and mapping for autonomous mobile robots operating in underwater and terrestrial environments.

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.

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

We research building machines which exploit their natural dynamics to achieve extraordinary agility and efficiency.

We build unmanned vehicles that can fly and drive without maps or GPS.

We investigate the technologies that support scalable high-performance computing, including hardware, software, and theory.

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.

39 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.

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.

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.

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

We are investigating the limits of computing on encrypted data, with a focus on the private outsourcing of computation over sensitive data.

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.

We are studying how best to implement bilateral control and make it acceptable to drivers

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

Our goal is to create an online risk-aware planner for vehicle maneuvers that can make driving safer and less stressful through a “parallel” autonomous system that assists the driver by watching for risky situations, and by helping the driver take proactive, compensating actions before they become crises.

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

Linking probability with geometry to improve the theory and practice of machine learning

Our goal is to develop an autonomous vehicle control strategy that adapts on-line to varying levels of congestion in the environment.

Gerrymandering is a direct threat to our democracy, undermining founding principles like equal protection under the law and eroding public confidence in elections.

Printable Hydraulics allows fluid-actuated robots to be automatically fabricated using 3D printers.

To enable privacy preservation in decentralized optimization, differential privacy is the most commonly used approach. However, under such scenario, the trade-off between accuracy (even efficiency) and privacy is inevitable. In this project, distributed numerical optimization scheme incorporated with lightweight cryptographic information sharing are explored. The affect on the convergence rate from network topology is considered.

35 People Results

Graduate Student

Graduate Student

Graduate Student

Graduate Student

Graduate Student

Senior Postdoctoral Associate

Graduate Student

Graduate Student

Graduate Student

Postdoctoral Associate

Research Scientist

31 News Results

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

System ensures hackers eavesdropping on large networks can’t find out who’s communicating and when they’re doing so.

In a Washington Post op-ed, CSAIL's R. David Edelman outlines how to regulate AI properly

Developed at MIT’s Computer Science and Artificial Intelligence Laboratory, a team of robots can self-assemble to form different structures with applications in inspection, disaster response, and manufacturing

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

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

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

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

System allows drones to cooperatively explore terrain under thick forest canopies where GPS signals are unreliable.

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

CSAIL system encourages government transparency using cryptography on a public log of wiretap requests.

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.

For all the progress made in self-driving technologies, there still aren’t many places where they can actually drive. Companies like Google only test their fleets in major cities where they’ve spent countless hours meticulously labeling the exact 3-D positions of lanes, curbs, off-ramps, and stop signs.

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

Made of silicone rubber, CSAIL’s “SoFi” could enable a closer study of aquatic life.

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.