# Research

- Research Areas
- Impact Areas

20 Group Results matching all criteria

We are investigating decentralized technologies that affect social change.

#### Research Group

## Haystack Group

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

#### Research Group

## Multimodal Understanding Group

Our objective is to build techniques, software, and hardware that enable natural interaction with

computation.

computation.

#### Research Group

## Applied Computing Group

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

#### Research Group

## Cryptography and Information Security Group

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.

#### Research Group

## Geometric Data Processing Group

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

#### Research Group

## Supertech Research Group

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

#### Research Group

## Algorithms Group

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

#### Research Group

## Complexity Theory Group

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.

#### Research Group

## Computational Connectomics Group

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

#### Research Group

## Quantum Information Science Group

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

#### Research Group

## Multicore Algorithmics

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.

#### Research Group

## Theory of Distributed Systems Group

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.

#### Research Group

## Computation and Biology

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.

#### Research Center

## Center for Deployable Machine Learning (CDML)

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

#### Community of Research

## Computing & Society Community of Research

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.

#### Community of Research

## Vertical AI Community of Research

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.

#### Community of Research

## Theory of Computation Community of Research

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.

62 Project Results matching all criteria

#### Project

## Aspect-Augmented Adversarial Networks for Domain Adaptation

We propose a novel aspect-augmented adversarial network for cross-aspect and cross-domain adaptation tasks. The effectiveness of our approach suggests the potential application of adversarial networks to a broader range of NLP tasks for improved representation learning, such as machine translation and language generation.

#### Project

## Matrix Permanents and Linear Optics

We use tools from quantum physics to prove new results in classical complexity.

#### Project

## Deep Inverse Planning

Deep inverse planning for learning from high-dimensional demonstrations

#### Project

## Multi-Core Data Structures

We aim to develop data structures optimized for large-scale multi-core computers.

#### Project

## Distributed Computation in Ant Colonies

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.

#### Project

## Social Network Extraction from GPS Datasets with Coresets

We extract the underlying hidden relations from the given location-based datasets (e.g. GPS data) and we estimate (approximate) the hidden a social network in the data by using a particular data reduction algorithm (i.e., by using coresets).

#### Project

## Uhura: Personal Assistant that Manages Risk

Uhura is an autonomous system that collaborates with humans in planning and executing complex tasks, especially under over-subscribed and risky situations.

#### Project

## Bayesian Optimization for Global Optimization of Expensive Black-box Functions

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.

#### Project

## Active Learning of Models for Planning

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.

#### Project

## Reconstructing Neural Circuits from Mammalian Brain

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.

#### Project

## Performance Engineering of Cache Profilers

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.

#### Project

## Generating Good Adversarial Examples for Neural Networks

Our goal is to better understand adversarial examples by 1) bounding the minimum perturbation that needs to be added to a regular input example to cause a given neural network to misclassify it, and 2) generating some adversarial input example with minimum perturbation.

#### Project

## Deterministic Algorithms for Robotic Task and Motion Planning

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

#### Project

## Hidden Influencers, Risk and Causes of Infection

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.

#### Project

## Predicting Adverse Events Across Changing Electronic Health Record Systems

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

#### Project

## Safety Standards for Autonomous Vehicles

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.

#### Project

## Algorithmic Aspects of Performance Engineering

The project concerns algorithmic solutions for writing fast codes.

#### Project

## Data Garbling: Computing on Encrypted Data

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

#### Project

## Efficient Robust Estimation in High Dimensions

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.

#### Project

## OpenTuner: An Extensible Framework for Program Autotuning

OpenTuner is a new framework for building domain-specific multi-objective program autotuners.

#### Project

## Better Models for Ride-Sharing

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

#### Project

## Programming Abstractions for Dynamic Graph Analytics

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

#### Project

## Bridging Theory and Practice in Shared-Memory Parallel Algorithm Design

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

40 People Results matching all criteria

## Leandro Agudelo

Research Scientist

## Cenk Baykal

Graduate Student

## Martin Demaine

Robotics Engineer

## Noah Golowich

Graduate Student

## Joanne Hanley

Administrative Assistant II

## Dhiraj Holden

Graduate Student

## Siddhartha Jayanti

Graduate Student

## Kenji Kawaguchi

Graduate Student

## William Kuszmaul

Graduate Student

## Lucas Liebenwein

Graduate Student

## Andrea Lincoln

Graduate Student

## Slobodan Mitrovic

Postdoctoral Associate

8 News Results matching all criteria

## Giving soft robots senses

In a pair of papers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), two teams enable better sense and perception for soft robotic grippers.

## CSAIL device lets doctors monitor COVID-19 patients from a distance

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

## Better fetal health - by building a map of the placenta

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

## Higher-res models for creating structures with complex features

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

## Helping computers fill in the gaps between video frames

Machine learning system efficiently recognizes activities by observing how objects change in only a few key frames.

## Teaching chores to an artificial agent

Activity simulator could eventually teach robots tasks like making coffee or setting the table.

## Programming drones to fly in the face of uncertainty

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

20 Group Results

#### Research Group

## Algorithms Group

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

#### Research Group

## Applied Computing Group

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

#### Research Center

## Center for Deployable Machine Learning (CDML)

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

#### Research Group

## Complexity Theory Group

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.

#### Research Group

## Computation and Biology

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.

#### Research Group

## Computational Connectomics Group

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

#### Community of Research

## Computing & Society Community of Research

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.

#### Research Group

## Cryptography and Information Security Group

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.

#### Research Group

## Decentralized Information Group

We are investigating decentralized technologies that affect social change.

#### Research Group

## Geometric Data Processing Group

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

#### Research Group

## Haystack Group

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

#### Research Group

## Multicore Algorithmics

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.

#### Research Group

## Multimodal Understanding Group

Our objective is to build techniques, software, and hardware that enable natural interaction with

computation.

computation.

#### Research Group

## Quantum Information Science Group

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

#### Research Group

## Supertech Research Group

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

#### Community of Research

## Theory of Computation Community of Research

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.

#### Research Group

## Theory of Distributed Systems Group

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.

#### Community of Research

## Vertical AI Community of Research

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.

62 Project Results

#### Project

## Efficient Robust Estimation in High Dimensions

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.

#### Project

## Fast Lightweight Autonomy

The goal of the FLA program is to explore non-traditional perception and autonomy methods that could enable a new class of algorithms for minimalistic high-speed navigation in cluttered environments.

## Nick Villanueva

## Jake Ware

#### Project

## Generating Good Adversarial Examples for Neural Networks

Our goal is to better understand adversarial examples by 1) bounding the minimum perturbation that needs to be added to a regular input example to cause a given neural network to misclassify it, and 2) generating some adversarial input example with minimum perturbation.

#### Project

## Geometry and topology for scientific computing and shape analysis

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

#### Project

## Helping robots learn using demonstrations

Help robots learn faster by providing demonstrations when they need help

#### Project

## Hidden Influencers, Risk and Causes of Infection

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.

#### Project

## High-Performance Parallel Clustering

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

#### Project

## Is the Casino using a Riffle Shuffle?

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.

#### Project

## Leader Election in the SINR Model with Arbitrary Power Control

We study the leader election problem in a theoretical wireless network setting. We show that it can be solved in two communication rounds.

## Magnus Halldorsson

## Stephan Holzer

#### Project

## Matrix Permanents and Linear Optics

We use tools from quantum physics to prove new results in classical complexity.

#### Project

## Multi-Core Data Structures

We aim to develop data structures optimized for large-scale multi-core computers.

#### Project

## OpenTuner: An Extensible Framework for Program Autotuning

OpenTuner is a new framework for building domain-specific multi-objective program autotuners.

#### Project

## Optimal transport for statistics and machine learning

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

#### Project

## Performance Engineering of Cache Profilers

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.

#### Project

## Planning under uncertainty with complex dynamics

We focus on learning to compute near-optimal plans which leverage environmental contact to mitigate action uncertainty, in hopes of enabling inexpensive robotic manipulators to perform precise assembly tasks.

#### Project

## Political Geometry: Establishing Fair Mathematical Standards for Political Redistricting

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

#### Project

## Practical Secure Computation

Our goal is to bridge theory and practice to create infrastructure that allow computation and multi-party computation without compromising privacy.

## Danny Feldman

#### Project

## Predicting Adverse Events Across Changing Electronic Health Record Systems

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

#### Project

## Privacy-Preserving Decentralized Optimization

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.

#### Project

## Programming Abstractions for Dynamic Graph Analytics

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

#### Project

## Random Graph with Applications in MPC

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.

#### Project

## Reconstructing Neural Circuits from Mammalian Brain

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.

#### Project

## Reliable and Robust Machine Learning

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

#### Project

## Return of the Byzantine Generals

This project studies new solutions to the Byzantine General Problem and its applications in distributed systems and cryptography.

40 People Results

## Leandro Agudelo

Research Scientist

## Cenk Baykal

Graduate Student

## Martin Demaine

Robotics Engineer

## Noah Golowich

Graduate Student

## Joanne Hanley

Administrative Assistant II

## Dhiraj Holden

Graduate Student

## Siddhartha Jayanti

Graduate Student

## Kenji Kawaguchi

Graduate Student

## William Kuszmaul

Graduate Student

## Lucas Liebenwein

Graduate Student

## Andrea Lincoln

Graduate Student

## Slobodan Mitrovic

Postdoctoral Associate

8 News Results

## Giving soft robots senses

In a pair of papers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), two teams enable better sense and perception for soft robotic grippers.

## CSAIL device lets doctors monitor COVID-19 patients from a distance

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

## Better fetal health - by building a map of the placenta

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

## Higher-res models for creating structures with complex features

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

## Helping computers fill in the gaps between video frames

Machine learning system efficiently recognizes activities by observing how objects change in only a few key frames.

## Teaching chores to an artificial agent

Activity simulator could eventually teach robots tasks like making coffee or setting the table.

## Programming drones to fly in the face of uncertainty

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