Research

  • Impact Areas
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Algorithms & Theory

Foundational work that includes complexity, parallel computing and game theory

AI & ML

Spanning natural language processing, deep-learning, computer vision and more

Computational Biology

Understanding disease via epigenomics, gene regulation and bioinformatics

Computer Architecture

How CPUs, memory and other systems are designed and organized

Graphics & Vision

Teaching computers to interpret, create and animate visual data

Human-Computer Interaction

Software and hardware that let us naturally interact with technology

Programming Languages & Software Engineering

From compilers and verification to software design and engineering

Robotics

Vision, actuation, sensing and manipulation of machines

Security & Cryptography

Developing technologies to prevent and recover from cyber-attacks

Systems & Networking

From distributed systems and databases to wireless

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1 Group Results matching all criteria

1 Group Results

3 Project Results

Project

Program Performance Prediction with Deep Learning

Programming Systems Group
Commit Group
Vertical AI Community of Research
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).
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Michael Carbin
Amarasinghe

Leads

Research Areas

Programming Languages & Software Engineering

Impact Areas

Big Data
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Michael Carbin
Amarasinghe

22 People Results

Chlipala-headshot

PI
Core/Dual

Adam Chlipala

Associate Professor
Programming languages, formal methods, security & privacy, systems
Chlipala-headshot
T 324-8439
RM 32-G842

Lead

Kami: A Modular Deductive Hardware Verification Platform
Compilation Using Correct-by-Construction Program Synthesis
Programming Languages & Verification
Hemiola: Structural Design and Proof of Cache-Coherence Protocols

Member

Compilation Using Correct-by-Construction Program Synthesis
Programming with spaces
Computation Structures Group
FSCQ: File-system verification
Systems Community of Research
Programming languages, formal methods, security & privacy, systems
R. David Edelman - Headshot

R. David Edelman

Director, Project on Technology, Economy & National Security
Machine learning, cybersecurity, governance
R. David Edelman - Headshot
T 258-6389
Machine learning, cybersecurity, governance
Jim Glass

PI
Core/Dual

Jim Glass

Senior Research Scientist
speech and language processing
Jim Glass
T 253-1640
RM 32-G444

Lead

Spoken Language Systems Group
Designing Information-Rich Embedding Spaces
Unsupervised Learning of Interpretable Representations from Sequential Data
Fact-Checking and Reasoning
Speaker Verification and Diarization
Automatic Speech Recognition
Language and Dialect Identification

Member

Using Computers to Eat Healthier
Low-Power Speech Processing Circuits
Crossing the Vision-Language Boundary
Unsupervised Speech Processing
Arabic Language Processing
Health-related Biomarkers from Speech
Community Question Answering
Understanding Language Representations in Deep Learning Models
Embodied Intelligence Community of Research
speech and language processing
Jackson image

PI
Core/Dual

Daniel Jackson

Professor
Software design, programming, systems
Jackson image
T 258-8471
RM 32-G704

Lead

Human-Computer Interaction
Software Design Group
Rethinking Software Design
Gitless: a simple version control system built on top of Git
Déjà Vu: Constructing Apps from Concepts
Security by Design for Cyberphysical Systems
Poirot: Secure Protocol Implementation by Design
An Interlock for Self Driving Cars
Alloy
Wildcard

Member

Espalier
Human-Computer Interaction Community of Research
Systems Community of Research
Wildcard
Software design, programming, systems
Karger

PI
Core/Dual

David Karger

Professor
Helping people manage information
Karger
T 258-6167
RM 32-G592

Lead

Haystack Group
Human-Computer Interaction
Algorithms Group
Mavo: Creating Interactive Data-Driven Web Applications by Authoring HTML
Wikum: Bridging Discussion Systems and Wikis with Collective Summarization
Squadbox: Combating Online Harassment using Friendsourced Moderation
Eyebrowse: Social and Public Web Browsing
Talkademic: Motivating Online Academic Discourse

Member

Human-Computer Interaction Community of Research
Theory of Computation Community of Research
Systems Community of Research
Helping people manage information
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Henry Lieberman

Research Scientist
Henry Lieberman is a Research Scientist at MIT CSAIL, in AI and HCI.
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Henry Lieberman is a Research Scientist at MIT CSAIL, in AI and HCI.
Howard Shrobe

PI
Core/Dual

Howard Shrobe

Principal Research Scientist
Architectures, cybersecurity, web policy
Howard Shrobe
T 253-7877
RM 32-225

Lead

RADMAX
Risk-aware Network Resource Allocation
Automated Attack Tree Generation for Critical Infrastructure
Securing IoT
Cognitive AI Community of Research
Artificial Social Intelligence for Successful Teams (ASIST)
Uhura: Personal Assistant that Manages Risk

Member

Computation Structures Group
Computing & Society Community of Research
Architectures, cybersecurity, web policy
Shun

PI
Core/Dual

Julian Shun

Assistant Professor
Parallel computing, applied algorithms, graph processing.
Shun
T 258-0669
RM 32-G736

Lead

Programming Abstractions for Dynamic Graph Analytics
Bridging Theory and Practice in Shared-Memory Parallel Algorithm Design
High-Performance Parallel Clustering

Member

Data Systems Group
Theory of Computation Community of Research
Vertical AI Community of Research
Systems Community of Research
Parallel computing, applied algorithms, graph processing.

7 News Results

MIT researchers have developed a new approach to training speech-recognition systems that doesn’t depend on transcription. Instead, their system analyzes correspondences between images and spoken descriptions of those images, as captured in a large collection of audio recordings.
AI & ML Human-Computer Interaction Big Data

Learning words from pictures

Speech recognition systems, such as those that convert speech to text on cellphones, are generally the result of machine learning. A computer pores through thousands or even millions of audio files and their transcriptions, and learns which acoustic features correspond to which typed words.But transcribing recordings is costly, time-consuming work, which has limited speech recognition to a small subset of languages spoken in wealthy nations.

Learning words from pictures

System that learns to distinguish words’ phonemes could aid development of speech-processing systems for under-studied languages
AI & ML Human-Computer Interaction Big Data Education Entertainment Internet of Things

Learning spoken language

Every language has its own collection of phonemes, or the basic phonetic units from which spoken words are composed. Depending on how you count, English has somewhere between 35 and 45. Knowing a language’s phonemes can make it much easier for automated systems to learn to interpret speech.In the 2015 volume of Transactions of the Association for Computational Linguistics, CSAIL researchers describe a new machine-learning system that, like several systems before it, can learn to distinguish spoken words. But unlike its predecessors, it can also learn to distinguish lower-level phonetic units, such as syllables and phonemes.

Learning spoken language