#### 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

27 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.

We focus on finding novel approaches to improve the performance of modern computer systems without unduly increasing the complexity faced by application developers, compiler writers, or computer architects.

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 mission is fostering the creation and development of high-performance, reliable and secure computing systems that are easy to interact with.

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 conduct research on all areas of database systems and information management.

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.

The focus of the HCI CoR is inventing new systems and technology that lie at the interface between people and computation, and understanding their design, implementation, and societal impact.

MIT App Inventor is an intuitive, visual programming environment that allows everyone – even children – to build fully functional apps for smartphones and tablets.

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.

Mission: improve the software and hardware development process by replacing ugly development techniques with beautiful ones

Secret weapon: applied mathematical logic, including computer theorem proving (especially Coq) and type systems

Secret weapon: applied mathematical logic, including computer theorem proving (especially Coq) and type systems

We research programming languages, software engineering, and related work in human-computer interaction.

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

Our goal is to find better ways to make software, and ways to make software better.

We focus on understanding the problem-solving strategies used by scientists and engineers, with the goals of automating parts of the process and formalizing educational methods.

The Systems CoR is focused on building and investigating large-scale software systems that power modern computers, phones, data centers, and networks, including operating systems, the Internet, wireless networks, databases, and other software infrastructure.

20 Project Results

The project concerns algorithmic solutions for writing fast codes.

Alloy is a language for describing structures and a tool for exploring them. It has been used in a wide range of applications from finding holes in security mechanisms to designing telephone switching networks. Hundreds of projects have used Alloy for design analysis, for verification, for simulation, and as a backend for many other kinds of analysis and synthesis tools, and Alloy is currently being taught in courses worldwide.

Data scientists universally report that they spend at least 80% of their time finding data sets of interest, accessing them, cleaning them and assembling them into a unified whole.

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.

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.

Help robots learn faster by providing demonstrations when they need help

Tools to build cryptographic software that is correct and secure by construction

Tools for building digital hardware with machine-checked proofs of correctness

Tools for building software with machine-checked proofs of correctness

A framework to support implementing, specifying, verifying, and compiling hardware designs, modularly

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.

An approach to reducing risks of attack on cyberphysical infrastructure (such as water purification plants and electric grids) with new software design and analysis techniques.

Solid aims to radically change the way Web applications work today, resulting in true data ownership as well as improved privacy.

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.

A polyhedral compiler for expressing image processing, DNN, and linear/tensor algebra applications

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

19 People Results

Research Affiliate

Graduate Student

Graduate Student

Graduate Student

23 News Results

MIT scientists show how fast algorithms are improving across a broad range of examples, demonstrating their critical importance in advancing computing.

A tool for navigating complex computer instructions

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.

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.

When designing actuators involves too many variables for humans to test by hand, this system can step in.

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

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.

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.

Breakthrough CSAIL system suggests robots could one day be able to see well enough to be useful in people’s homes and offices.

Users can quickly visualize designs that optimize multiple parameters at once.

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

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

CSAIL’s robotic system minimizes dangerous sawing, helps users customize furniture.

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.

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).

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.”