Wojciech
Matusik
Biography
Wojciech Matusik is the Cadence Design Systems Professor of Electrical Engineering and Computer Science at MIT, where he leads the Computational Design and Fabrication Group at CSAIL and holds a joint appointment in Mechanical Engineering.
Before MIT he was at Mitsubishi Electric Research Laboratories, Adobe Systems, and Disney Research Zurich. He received his Ph.D. from MIT in 2003, an S.M. from MIT in 2001, and a B.S. from the University of California, Berkeley in 1997.
His group develops AI for the physical world. The work starts with representations of materials, structures, mechanisms, devices, and robots that AI can synthesize and a solver can verify. Physics simulators run forward from a design to its performance and backward from a target to a design. Discovery systems propose new materials and molecules, test them on real instruments, and learn from what came back. And the group builds the machines that make things: 3D printers that watch themselves print, knitting machines, robots, instruments. AI is not a layer on top of this but is built into the representations, the discovery loop, and the machines themselves.
Talks and press
TEDxMIT · April 2025
The Robot That Broke the Rules, and What It Means for the Future of AI
On engineering general intelligence: why the next step for AI may be
learning when to break the rules, and what changes when machines design things that have to
survive contact with the physical world.
Watch the talk on YouTube
MIT News
Reporting on the group's work
MIT News has covered the group's research on holography, tactile
sensing, machine knitting, computational design, and AI for discovery.
Read the coverage
Open problems
Three questions drive the group today. Each one is open, funded, and taking new students.
Neurosymbolic methods and domain-specific languages
Programs, not pixels. We build design languages that a machine can write and a solver can check, so that a generated artifact is correct by construction rather than plausible by appearance.
Neural physics surrogates
Learned simulators that stay physically valid. The aim is models that replace or accelerate classical solvers without giving up the guarantees that made the solvers useful.
AI for scientific discovery
Closing the loop from hypothesis to experiment. Discovery systems that propose candidates, run them on real instruments, and learn from what actually came back.
Foundations. These sit on long-running work in computer graphics, computational design and fabrication, computer vision, and robotics. The group's open problems in full.
Selected publications
Chosen for what the field did with them, not for how recently they appeared.
Selected among the field's seminal work
For SIGGRAPH's 50th anniversary, ACM republished the most significant papers of the field's second 25 years as Seminal Graphics Papers: Pushing the Boundaries, Volume 2. Three of that volume's 88 papers are his.
A data-driven reflectance modelFirst author- Design and fabrication of materials with desired deformation behavior
Computational design of mechanical characters
Published in Nature
Three papers in the flagship journal, two of them as senior author.
Learning the signatures of the human grasp using a scalable tactile gloveSenior author- Towards real-time photorealistic 3D holography with deep neural networksSenior author
Vision-controlled jetting for composite systems and robots
Communications of the ACM Research Highlights
Selected by CACM as work the wider computing field should know about.
OpenFab: a programmable pipeline for multimaterial fabricationResearch Highlight
The Frankencamera: an experimental platform for computational photographyResearch Highlight
Recent award-winning work
NeuralActuator: neural actuation modeling for robot dynamics and external force perceptionOutstanding Systems Paper
Fabrica: dual-arm assembly of general multi-part objectsBest Paper Award
TelePulse: enhancing the teleoperation experienceBest Paper Award
The complete list lives on the group site. All CDFG publications · Google Scholar
Honors and awards
- 2026Outstanding Systems Paper in Memory of Seth Teller, Robotics: Science and Systems NeuralActuator
- 2025Best Paper Award, Conference on Robot Learning Fabrica
- 2025Best Paper Award, ACM CHI TelePulse
- 2025Cadence Design Systems Professorship, MIT named chair
- 2023Joan and Irwin M. Jacobs Professorship, MIT named chair
- 2023Humboldt Research Award, Alexander von Humboldt Foundation
- 2022Best Paper Honourable Mention, British Machine Vision Conference VoRF
- 2021Best Paper Honourable Mention, ACM CHI KnitUI
- 2020Frank Quick Faculty Research Innovation Fellowship, MIT
- 2014Ruth and Joel Spira Award for Excellence in Teaching, MIT
- 2012Sloan Research Fellowship, Alfred P. Sloan Foundation
- 2012DARPA Young Faculty Award
- 2009ACM SIGGRAPH Significant New Researcher Award · award speech: part one, part two
- 2004MIT Technology Review TR100, Innovators Under 35
Where people go next
15 doctoral theses supervised, plus 34 master's and undergraduate theses. 62 former group members hold faculty, permanent research, or industry research positions. The group roster lists everyone, current and past.
Teaching
- 6.4420J / 6.8420Computational Design and Fabrication. Created by Matusik; offered jointly with Mechanical Engineering as 2.0911J.
- 6.4400Computer Graphics
- 6.3900Introduction to Machine Learning
- 6.838Advanced Topics: Computer Graphics
- Professional Ed.AI for Scientific Discovery
- Professional Ed.AI for Engineers
Join the group
The group takes students through both EECS and Mechanical Engineering. Work here usually spans an algorithm and something physical, so say which end you want to start from.
Email directly with your CV, two or three representative papers, and a short note on what you would want to build here.
Apply to the MIT EECS or MechE PhD program and name Wojciech Matusik as a potential advisor. Mention which of the three research threads above fits your interests.
UROP positions run every term. Email Wojciech or any current group member with your background and what you would like to learn.