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.
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.
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.
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.
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.
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.
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.
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.”
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.
Anonymity networks, which sit on top of the public Internet, are designed to conceal people’s Web-browsing habits from prying eyes. The most popular of these, Tor, has been around for more than a decade and is used by millions of people every day.
In a computer operating system, the file system is the part that writes data to disk and tracks where the data is stored. If the computer crashes while it’s writing data, the file system’s records can become corrupt. Hours of work could be lost, or programs could stop working properly.At a symposium this fall, MIT researchers will present the first file system that is mathematically guaranteed not to lose track of data during crashes. Although the file system is slow by today’s standards, the techniques the researchers used to verify its performance can be extended to more sophisticated designs. Ultimately, formal verification could make it much easier to develop reliable, efficient file systems.
By crunching 130 million mouse-clicks, two CSAIL researchers have developed a machine-learning model that can predict with surprising accuracy whether or not a MOOC student will drop out of a given course.