#### 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

22 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 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.

32 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.

Robot scheduling via learning from demonstration for tasks in medicine and more

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.

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 aim to study the causes and transmission modes of infectious diseases among members of a community in the presence of hidden, asymptomatic spreaders of the pathogen.

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

Models and algorithms to predict in real time the next actions of a human working together with a robot

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.

Transitioning machine learning models across electronic health record (EHR) versions can be improved by mapping different EHR encodings to a common vocabulary.

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 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.

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

34 People Results

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36 News Results

Machine learning model predicts probability that a patient’s UTI can be treated by various antibiotics

Book co-authored by Associate Professor Julie Shah and Laura Major SM ’05 explores a future populated with robot helpers.

Wireless device captures sleep data without using cameras or body sensors; could aid patients with Parkinson’s disease, epilepsy, or bedsores.

CSAIL system uses muscle signals to cue a drone’s movement, enabling more natural human-robot communication

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

A new MIT study finds “health knowledge graphs,” which show relationships between symptoms and diseases and are intended to help with clinical diagnosis, can fall short for certain conditions and patient populations. The results also suggest ways to boost their performance.

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

Model could recreate video from motion-blurred images and “corner cameras,” may someday retrieve 3D data from 2D medical images.

New technique stretches out MRI scans of placentas so they can be more accurately analyzed, and shows the potential of MRI for pregnancy monitoring.

Dutch delegation visits the Institute for a tour focused on computing, robotics, and health care innovation.

System lets nonspecialists use machine-learning models to make predictions for medical research, sales, and more.

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

System helps machine-learning models glean training information for diagnosing and treating brain conditions Home Node From one brain scan, more information for medical artificial intelligence

CSAIL’s "RoCycle" system uses in-hand sensors to detect if an object is paper, metal or plastic.

Technique could improve machine-learning tasks in protein design, drug testing, and other applications.

Gripper inspired by “origami magic ball” can grasp wide array of delicate and heavy objects

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

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.

Computer scientists often develop mathematical models to understand how animals move, enabling breakthroughs in designing things like microrobotic wings and artificial bone structures.

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

Model learns to pick out objects within an image, using spoken descriptions.

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