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 goal is to develop a model for the binding, internalization, and tumor-killing dynamics of liposome-enclosed doxorubicin targeted to cancer cells and develop design principles for creating more effective therapeutics.
Our goal is to develop lightweight tools that allow programmers to better understand the cache performance of their applications. Tasks include designing profilers, performance engineering existing ones, and exploring different metrics for cache interactions.
Our research aims to scale hard-to-parallelize applications through new programming models and multicore architectures. Our goal is to enable
programmers to write efficient and scalable parallel programs as easily as they
write sequential programs today.
We develop, parameterize, and validate a model for tumor growth inhibition using in vivo mouse data and study the effects of modeling uncertainty and inter-individual variability on drug candidate efficacy predictions.
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).
We develop algorithms, systems and software architectures for automating reconstruction of accurate representations of neural tissue structures, such as nanometer-scale neurons' morphology and synaptic connections in the mammalian cortex.
This project focuses on altering the way trusted execution environments handle paging to decouple page access patterns from the control flow of the program, preventing an attacker from using these access patterns to learn information about program secrets.
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 professor and CSAIL principal investigator Barbara Liskov was selected to receive the 2018 IEEE Computer Society’s Computer Pioneer Award for her early concepts and developments in the electronic computer field.
Last week CSAIL hosted the second “Hot Topics in Computing” speaker series, a monthly forum where computing experts hold discussions with community members on various topics in the computer science field.
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.
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).