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MIT Computer Science and Artificial Intelligence Laboratory (CSAIL)
Higher Education
Cambridge, MA 178,373 followers
MIT CSAIL pioneers approaches to computing that improve how people work, play and learn.
About us
The MIT Computer Science and Artificial Intelligence Laboratory – known as CSAIL – is the largest research laboratory at MIT and one of the world’s most important centers of information technology research. CSAIL has played a key role in the computer revolution and developments such as time-sharing, massive parallel computers, public key encryption, mass commercialization of robots, and much of the technology underlying the ARPANet, Internet and the World Wide Web. CSAIL’s focus is developing the architecture and innovative applications for tomorrow’s information technology. Our research yields long-term improvements in how people live and work. CSAIL members (former and current) have launched more than 100 companies, including 3Com, Lotus Development Corporation, RSA Data Security, Akamai, iRobot, Meraki, ITA Software, and Vertica. The Lab is home to the World Wide Web Consortium (W3C), Wireless@MIT, BigData@CSAIL, Cybersecurity@CSAIL and the MIT Information Policy Project (IPP). Connecting to CSAIL CSAIL Alliances is your organization's pathway to CSAIL connections and serves as a gateway into the lab for industry and governmental institutions seeking closer engagement to the work, researchers and students of CSAIL. The program provides organizations with a proactive and comprehensive approach to developing strong connections with all CSAIL has to offer. Leading organizations come to CSAIL to learn about our research, to recruit talented graduate students, and to explore collaborations with our researchers. Through this program, we are able to better provide our members with access to our latest thinking and our deep pool of exceptional human and informational resources. For more information, please visit: http://cap.csail.mit.edu/
- Website
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http://www.csail.mit.edu/
External link for MIT Computer Science and Artificial Intelligence Laboratory (CSAIL)
- Industry
- Higher Education
- Company size
- 1,001-5,000 employees
- Headquarters
- Cambridge, MA
- Type
- Nonprofit
- Founded
- 2003
- Specialties
- Artificial Intelligence, Systems, and Theory
Locations
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Primary
Get directions
32 Vassar Street
Cambridge, MA 02139, US
Employees at MIT Computer Science and Artificial Intelligence Laboratory (CSAIL)
Updates
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MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) reposted this
we spoke about recursive self-improvement, pace of model development at frontier labs, Liquid, and many other timely matters. Moonshots with Peter H. Diamandis, Dave B. Blundin, Alex Wissner-Gross, and Salim Ismail. thanks for having me on the show! https://lnkd.in/dbc5FNKx
Mira Murati's 975B Open Model, Ramin Hasani on Post-Transformer AI, and Demis' AI FINRA | EP #271
https://www.youtube.com/
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MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) reposted this
Excited to be at SIGGRAPH 2026 showing two projects on how AI can move beyond the screen and support people through spatial and embodied interaction! 🧠 Tangible AI Mind-map: A Graph-Based Interface for Human-AI Brainstorming Using Projection-Based AR, Gesture Recognition, and Large Language Models With Alexander Htet Kyaw, Yifei Li, Maggie Chao, Jin Gao, Keyi Zhang, Yudian Xu, Hiroshi Ishii https://lnkd.in/eEngTkX6 🥽 Context-Aware AI Assistant and AR Interface for Lunar Extravehicular Activity Procedural Guidance With Rodrigo Gallardo, Qilmeg Doudatcz, Ganit Goldstein, Ilkyaz Sarimehmetoglu, Sergio Mutis, Clara Emmerling, Anita L., Alexander Htet Kyaw, Berfin Ataman, Skylar Tibbits https://lnkd.in/ejjtNiRv If you are attending SIGGRAPH, come find us in the West Hall Lobby and say hello! #SIGGRAPH2026 #AugmentedReality #ArtificialIntelligence #HumanAIInteraction #SpatialComputing #MIT #HCI #Research
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MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) reposted this
I'm extremely excited to share "Walk on Decomposed Subdomains: A Hybrid Monte Carlo–Deterministic Solver for Elliptic PDEs", which received a Best Paper Award 🏆 at #SIGGRAPH 2026! I will be presenting it next week in Los Angeles, together with my amazing collaborators and mentors Mohammad Sina Nabizadeh and Mina Konaković Luković. 🌐 Webpage: https://lnkd.in/g2BSvSVp 📄 Paper: https://lnkd.in/gmdfXAqX ✍️ Blogpost: https://lnkd.in/g2WFX9nK 🗣️ Talk: Wednesday, 22 July 2026, 4:10 PM, Room 408 A Elliptic partial differential equations (PDEs) are everywhere: they describe virtually anything that settles into a stable equilibrium — how heat spreads through a data center, how current flows through a circuit, or how wind circulates between the buildings of a city. Traditionally, these equations are solved with grid- or mesh-based methods, the most celebrated of which is arguably the finite element method (FEM). These methods are robust and remarkably well understood, but they all rest on one demanding requirement: spatial discretization. Before solving anything, we must tile the entire domain with a high-quality volumetric mesh or a sufficiently fine grid — and for complex, detailed, or evolving geometry, this step is often the most fragile and expensive part of the whole pipeline. A different paradigm, recently popularized in computer graphics, sets out to remove this requirement: grid-free Monte Carlo methods. Like Monte Carlo rendering, they solve PDEs using random samples, without ever discretizing the domain, and inherit the massive parallelism of Monte Carlo. The catch? Being stochastic, they trade bias for variance — and this variance leads to notoriously slow convergence, arguably the main reason these solvers have yet to gain traction beyond computer graphics. 💡With "Walk on Decomposed Subdomains" (WoDS), we asked ourselves: can we get the best of both worlds, namely the geometric flexibility of grid-free Monte Carlo and the fast, noise-free convergence of grid-based solvers? Our answer is a hybrid solver that uses each paradigm's strength to compensate for the other's weakness. It rests on two key ideas. First, by decomposing the domain into smaller, arbitrarily shaped subdomains, we significantly reduce the length of random walks while still benefiting from the mesh-free nature of Monte Carlo. Second, by coupling all subdomains together with a sparse deterministic solve, we eliminate variance at the cost of a controllable discretization bias. With this, we can solve potential flows to approximate wind patterns within a city with hundreds of buildings without ever meshing it (see the video!), find paths for a robot in a cluttered warehouse, or simulate extremely large lattices of microstructures. I'm very excited about this work! It's a small attempt at reconciling grid-free Monte Carlo methods with grid-based solvers, and I believe there are many more such attempts still to be made. It's only the beginning!
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MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) reposted this
Technical questions can sometimes be inspired by childhood dreams. Growing up, I often had to take a long route home from the gym because the bridge across the river was far away. I remember imagining: what if I could simply build a bridge wherever I needed one? Decades later, I am delighted to share our new paper in Nature Communications introducing FloatForm: miniature modular robotic boats that can autonomously organize, physically connect, reconfigure into new shapes, and move together as a larger structure. At its heart, this work asks a fundamental scientific question: What are the principles and mathematics that allow many individual agents to coordinate, self-organize, and become something greater than the sum of their parts? These principles are essential to understanding collective behavior in nature and to designing more adaptive, resilient systems for the built environment. Read the paper here: https://lnkd.in/gPCucd4A and see the system here: https://lnkd.in/gAWYuNGT Autonomous boats could change how we move, build, monitor, and respond on water, an environment that covers most of our planet, connects communities and economies, and remains difficult to navigate and serve. By enabling boats to coordinate, connect, and reorganize themselves, this work could support adaptive floating infrastructure, on-demand transportation, environmental monitoring, remote maintenance, and search-and-rescue operations. One especially urgent opportunity is large-scale water cleanup: addressing pollution across rivers, lakes, harbors, and oceans will require autonomous systems that can operate persistently, cooperatively, and at a scale people alone cannot reach. Self-organizing autonomous boats could also bring to life the idea I imagined as a child: a just-in-time bridge assembled by autonomous boats across the water where it is needed, able to adapt as conditions change and disperse when its work is done. Sometimes a childhood dream becomes a scientific question and, step by step, an engineering reality. This work was conducted at Massachusetts Institute of Technology through a collaboration between my research group at MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) and the MIT Senseable City Lab. Congratulations to my co-authors Wei Wang, Niklas Hagemann, Alejandro Gonzalez-Garcia, and Carlo Ratti. #Robotics #AI #SelfOrganization #AutonomousSystems #ModularRobotics #NatureCommunications
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Through research & entrepreneurship, MIT prof. Devavrat Shah is helping AI models meet the real world. He's working on methods that can handle constant decision-making using limited computational resources: https://lnkd.in/e3ftTmYw
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"How can we avoid all of the abusive behavior that we see on the Internet from being recast as Agentic misbehavior?" One of the original architects of the internet explores how lessons learned from the early days of the internet can help shape the AI age. Listen to the full episode of Building 32 featuring David Clark: https://bit.ly/4waTFoj
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"NeuralActuator," developed by researchers at MIT's CDFG Lab (cdfg.mit.edu) w/collaborators from Amazon Robotics, helps low-cost robot arms better model complex actuator behavior in the real world. The AI model enables sensor-less force perception & force-aware real-robot control, helping close the gap between simulation & reality. It won the Outstanding Systems Paper Award at #RSS2026: https:// bit.ly/4aW2VEg
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32(ish) questions w/MIT robotics researcher (& actor): https://lnkd.in/gAbVAEJk
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MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) reposted this
In January, I started "building something new" with an incredible team. Today I finally get to share some first details about what we've been building. We've called it Walden Robotics (http://waldenrobotics.com). I thought long and hard about my own reasons for starting this company. It's not only about the robots. It's also about people. I've tried to capture those thoughts in my first Walden blog post: https://lnkd.in/gjAjAUn2 It's been an incredible ride so far. Within just a few months of forming the company, we were already operating a general-purpose robot with an end-to-end policy in production in one of the most important factories in North America. It's amazing at how much I've already learned from that experience. There is a lot of work to do, but the mission has never been so clear. Please help me welcome Walden Robotics into the world. And stay tuned for more updates! https://lnkd.in/gQQiWRQi
Walden Robotics: General-purpose robots that continuously learn & improve while performing real work
https://www.youtube.com/