#### 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

14 Group Results

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

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

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.

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.

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.

14 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.

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

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

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

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

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

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.

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.

The Robot Compiler allows non-engineering users to rapidly fabricate customized robots, facilitating the proliferation of robots in everyday life. It thereby marks an important step towards the realization of personal robots that have captured imaginations for decades.

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.

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

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

13 People Results

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26 News Results

A team from MIT helped create an image retrieval system to find the closest matches of paintings from different artists and cultures.

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.

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

CSAIL’s approach uses algorithms and “2.5-D” sketches to let computers visualize images from any perspective

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

MIT-developed tool improves automated image vectorization, saving digital artists time and effort.

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.

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.

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

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

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).