#### Research Group

## Algorithms Group

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

- Impact Areas
- Research Areas

23 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.

This CoR brings together researchers at CSAIL working across a broad swath of application domains. Within these lie novel and challenging machine learning problems serving science, social science and computer science.

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 furthering the application of technology and artificial intelligence in medicine and health-care.

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.

We seek to understand the mechanistic basis of human disease, using a combination of computational and experimental techniques.

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

We combine methods from computer science, neuroscience and cognitive science to explain and model how perception and cognition are realized in human and machine.

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.

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 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 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.

We work on a wide range of problems in distributed computing theory. We study algorithms and lower bounds for typical problems that arise in distributed systems---like resource allocation, implementing shared memory abstractions, and reliable communication.

This CoR takes a unified approach to cover the full range of research areas required for success in artificial intelligence, including hardware, foundations, software systems, and applications.

23 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.

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.

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.

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

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

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.

We plan to develop a programming abstraction to enable programmers to write efficient parallel programs to process dynamic graphs.

To explore how randomness in connectivity can improve the performance of secure multi-party computation (MPC) and the properties of communication graph to support MPC.

We work towards a principled understanding of the current machine learning toolkit and making this toolkit be robust and reliable.

In this project, we aim to develop a framework that can ensure and certify the safety of an autonomous vehicle. By leveraging research from the area of formal verification, this framework aims to assess the safety, i.e., free of collisions, of a broad class of autonomous car controllers/planners for a given traffic model.

Starling is a scalable query execution engine built on cloud function services that computes at a fine granularity, helping people more easily match workload demand.

We are developing machine-learning technology to help users efficiently run data queries over large archives of raw video.

26 People Results

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32 News Results

Five years in the making, MIT’s autonomous floating vessels get a size upgrade and learn a new way to communicate aboard the waters.

Through innovation in software and hardware, researchers move to reduce the financial and environmental costs of modern artificial intelligence.

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.

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.

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

Neural network that securely finds potential drugs could encourage large-scale pooling of sensitive data.

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.

Genome-wide association studies, which look for links between particular genetic variants and incidence of disease, are the basis of much modern biomedical research.

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