# Research

- Research Areas
- Impact Areas

2 Group Results matching all criteria

#### 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.

2 Project Results matching all criteria

#### 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

## Understanding neural networks in the brain

We aim to develop fully automated algorithms for mapping networks within biological brains.

25 Group Results

#### 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

## Center for Brains, Minds and Machines

Our main goal is developing a computationally based understanding of human intelligence and establishing an engineering practice based on that understanding.

#### Research Group

## Clinical Decision-Making Group

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

#### 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 Biology Group

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

#### Research Group

## Computational Cognitive Science Group

We study the computational basis of human learning and inference.

#### Research Group

## Computational Connectomics Group

#### Research Group

## Computational Genomics Group

We develop new machine learning techniques and algorithms to model the transcriptional regulatory networks that control gene expression programs in living cells.

#### Research Group

## Computational Perception & Cognition

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

#### 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 techniques and 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

## Medical Vision Group

We develop new algorithms for medical image analysis and visualization of medical imagery.

#### 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.

#### Research Group

## Theory of Computation Group

Theory research at CSAIL covers a broad spectrum of topics, including algorithms, complexity theory, cryptography, distributed systems, parallel computing and quantum computing.

#### 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

## Tidor Lab

We focus on the analysis of complex biological systems at both the molecular level and the systems level.

68 Project Results

#### Project

## A new way of handling all-to-all broadcast

We design a new all-to-all broadcasts scheme with significantly less communication cost using aggregate signatures.

#### Project

## A Simplified and Extensible Cilk Runtime for Research

CilkS is a new runtime system for the Cilk multithreaded programming environment which makes it easy to experiment with new algorithms, data structures, and programming linguistics.

#### Project

## Algebraic Techniques for Algorithm Design

We work on improving the algorithms for algebraic problems like matrix multiplication, and using these to design algorithms for fundamental non-algebraic problems.

#### Project

## Algorithmic Aspects of Performance Engineering

The project concerns algorithmic solutions for writing fast codes.

#### Project

## Approximating the diameter of a directed graph

There is a family of approximation algorithms for computing the diameter of an undirected graph that give a time/accuracy trade-off and our goal is to extend these results to directed graphs.

#### Project

## Artificial tissue homeostasis

In order to be able to design synthetic organs that function autonomously, we will need to engineer artificial tissue homeostasis. To control the size of these artificial tissues, two major mechanisms will have to be engineered.

#### 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

## Basing Cryptography on Structured Hardness

We aim to base a variety of cryptographic primitives on complexity theoretic assumptions. We focus on the assumption that there exist highly structured problems --- admitting so called "zero-knowledge" protocols --- that are nevertheless hard to compute

#### 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

## Blood Pressure Imager

Development of affordable wearable continuous blood pressure monitor based on radial arterial pulse imaged from the skin surface of human wrist

## Mumin Jin

## Aya G. Halawi

#### 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.

#### Project

## Clinical Intervention Prediction with Neural Networks

Integrating multi-modal clinical data and using recurrent and convolution neural networks to predict when patients will need important interventions.

#### Project

## Coresets for Machine Learning Algorithms

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

#### Project

## Covering All K-mers Using Joker Characters

We developed a new algorithm to generate compact sequence sets covering all k-mers using joker characters.

#### 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

## Deep Inverse Planning

Deep inverse planning for learning from high-dimensional demonstrations

#### Project

## Deterministic Algorithms for Robotic Task and Motion Planning

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

#### Project

## Distributed Co-prime Sampling Algorithms

To further parallelize co-prime sampling based sparse sensing with generalized Chinese Remainder Theorem and explore the geometry properties in the remainder code space.

#### 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

## Distributed Robot Garden

The robot garden provides an aesthetically pleasing educational platform that can visualize computer science concepts and encourage young students to pursue programming and robotics.

#### Project

## Diversity-inducing Probability Measures

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.

#### Project

## Driver-Friendly Bilateral Control for Suppressing Traffic Instabilities

Self-driving cars themselves can solve traffic problems even without global control.

#### Project

## Drug Target Identification Via Computational Modeling

We use computational models of biological pathways in disease to estimate the range of possible responses to various hypothetical drugs, given patient variability and limited pathway-relevant data.

39 People Results

## Leandro Agudelo

Postdoctoral Associate

## Cenk Baykal

Graduate Student

## Martin Demaine

Robotics Engineer

## Debayan Gupta

Lecturer

## Joanne Hanley

Administrative Assistant II

## Gautam Kamath

Graduate Student

## Konstantin Krismer

Graduate Student

## Lucas Liebenwein

Graduate Student

## Andrea Lincoln

Graduate Student

## Zelda Mariet

Graduate Student

## Sumaiya Nazeen

Graduate Student

20 News Results

## Holding law-enforcement accountable for electronic surveillance

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

## MIT professors win awards for research in theoretical computer science

MIT professors win awards for research in theoretical computer science

## Automating molecule design to speed up drug development

Machine-learning model could help chemists make molecules with higher potencies, much more quickly.

## Teaching chores to an artificial agent

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

## Demaine gives Congressional briefing on intersection of origami and computer science

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

## Making driverless cars change lanes more like human drivers do

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

## Gauging language proficiency through eye movement

Study tracks eye movement to determine how well people understand English as a foreign language.

## Removing health-care barriers and boundaries

MIT’s Amar Gupta and his wife Poonam were on a trip to Los Angeles in 2016 when she fell and broke both wrists. She was whisked by ambulance to a reputable hospital. But staff informed the couple that they couldn’t treat her there, nor could they find another local hospital that would do so. In the end, the couple was forced to take the hospital’s stunning advice: return to Boston for treatment.

## Protecting confidentiality in genomic studies

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

## Building AI systems that make fair decisions

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

## CSAIL’s Tom Leighton receives Marconi Prize, top prize in communications technology

CSAIL’s Tom Leighton receives Marconi Prize, top prize in communications technology

## 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.

## Institute launches the MIT Intelligence Quest

New Institute-wide initiative will advance human and machine intelligence research.

## Goldwasser, Micali, and Rivest win BBVA Foundation Frontiers of Knowledge Awards

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.

## Improving traffic - by tailgating less

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

## Four from MIT named 2017 Association for Computer Machinery Fellows

Today four MIT faculty were named among the Association for Computer Machinery's 2017 Fellows for making “landmark contributions to computing.”

## Goldwasser gives briefing on cryptography to Congress

Last week CSAIL principal investigator Shafi Goldwasser spoke about cryptography and privacy as part of the annual congressional briefing of the American Mathematical Society (AMS) and the Mathematical Sciences Research Institute (MSRI).

## Computer system predicts products of chemical reactions

When organic chemists identify a useful chemical compound — a new drug, for instance — it’s up to chemical engineers to determine how to mass-produce it. There could be 100 different sequences of reactions that yield the same end product. But some of them use cheaper reagents and lower temperatures than others, and perhaps most importantly, some are much easier to run continuously, with technicians occasionally topping up reagents in different reaction chambers.

## CSAIL's Daniel Jackson receives two ACM awards

This week the Association for Computer Machinery presented CSAIL principal investigator Daniel Jackson with the 2017 ACM SIGSOFT Outstanding Research Award for his pioneering work in software engineering. (This fall he also received the ACM SIGSOFT Impact Paper Award for his research method for finding bugs in code.)An EECS professor and associate director of CSAIL, Jackson was given the Outstanding Research Award for his “foundational contributions to software modeling, the creation of the modeling language Alloy, and the development of a widely used tool supporting model verification.”

## Cinematography on the fly

In recent years, a host of Hollywood blockbusters — including “The Fast and the Furious 7,” “Jurassic World,” and “The Wolf of Wall Street” — have included aerial tracking shots provided by drone helicopters outfitted with cameras. Those shots required separate operators for the drones and the cameras, and careful planning to avoid collisions. But a team of researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and ETH Zurich hope to make drone cinematography more accessible, simple, and reliable.