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.
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.
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 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 goal is to understand the nature of cyber security arms races between malicious and bonafide parties. Our vision is autonomous cyber defenses that anticipate and take measures against counter attacks.
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.
Using AI methods, we are developing an attack tree generator that automatically enumerates cyberattack vectors for industrial control systems in critical infrastructure (electric grids, water networks and transportation systems). The generator can quickly assess cyber risk for a system at scale.
Automatic speech recognition (ASR) has been a grand challenge machine learning problem for decades. Our ongoing research in this area examines the use of deep learning models for distant and noisy recording conditions, multilingual, and low-resource scenarios.
We aim to better understand the features of network protocols that facilitate denial of service attacks, in order to design more robust protocols and architectures in the future and evaluate existing designs more accurately.
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.
Many optimization problems in machine learning rely on noisy, estimated parameters. Neglecting this uncertainty can lead to great fluctuations in performance. We are developing algorithms for these already nonconvex problems that are robust to such errors.
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.
Every spring, engineering students from MIT and law students from Georgetown University overcome the distance between their institutions and disciplines in a semester-long flurry of virtual classroom meetings and late-night Google hangout sessions, culminating in presentations to policy experts in DC.