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.
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.
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.
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.
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.
We use visualization as a petri dish to study intelligence augmentation, or how can computational representations and software systems help amplify our cognition and creativity, while respecting our agency?
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.
We plan to develop a suite of graph compression and reordering techniques as part of the Ligra parallel graph processing framework to reduce space usage and improve performance of graph algorithms.
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.
Wikipedia is one of the most widely accessed encyclopedia sites in the world, including by scientists. Our project aims to investigate just how far Wikipedia’s influence goes in shaping science.
The robot garden provides an aesthetically pleasing educational platform that can visualize computer science concepts and encourage young students to pursue programming and robotics.
Predicting the number of clock cycles a processor takes to execute a block of assembly instructions in steady-state (the throughput) is important for both compiler designers and performance engineers.
However, building an analytical model to do so is especially complicated in modern x86-64 Complex Instruction Set Computer (CISC) machines with sophisticated processor microarchitectures in that it is tedious, error-prone, and must be performed from scratch for each processor generation.
Ithemal is the first tool that learns to predict the throughput of a set of instructions. It does so more accurately than state-of-the-art hand-written tools currently used in compiler backends and static machine code analyzers. In particular, Ithemal has less than half the error of state-of-the-art analytical models (LLVM's llvm-mca and Intel's IACA).
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.
The Imagination, Computation, and Expression Laboratory at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) has released a new video game called Grayscale, which is designed to sensitize players to problems of sexism, sexual harassment, and sexual assault in the workplace.
We live in the age of big data, but most of that data is “sparse.” Imagine, for instance, a massive table that mapped all of Amazon’s customers against all of its products, with a “1” for each product a given customer bought and a “0” otherwise. The table would be mostly zeroes.
