# MIT EECS Published articles for MIT EECS. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## MIT Schwarzman College of Computing launches pilot to help educators teach AI across disciplines DevFeed: [MIT Schwarzman College of Computing launches pilot to help educators teach AI across disciplines]() Original publisher: [Read original article]() Author: Amanda Diehl | MIT Schwarzman College of Computing Published: 2026-09-09T20:40:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [MIT Schwarzman College of Computing](), [Artificial Intelligence](), [Machine learning](), [Learning](), [Electrical engineering and computer science (EECS)]() Tags: [ai](), [ai-educators-pilot](), [ai-teaching-workshop](), [artificial-intelligence](), [asu-ozdaglar](), [civil-and-environmental-engineering](), [classes-and-programs](), [computer-science-and-technology](), [computing](), [dan-huttenlocher](), [education](), [education-teaching-academics](), [electrical-engineering-and-computer-science-eecs](), [machine-learning](), [mit-class-c01-c51-modeling-with-machine-learning](), [mit-eecs](), [mit-operations-research-center](), [mit-schwarzman-college-of-computing](), [mit-sloan](), [mit-sloan-school-of-management](), [pilot](), [saurabh-amin](), [school-of-engineering](), [shen-shen](), [special-events-and-guest-speakers](), [stem-education](), [teaching](), [teaching-ai]() ### AI overview MIT's inaugural AI Educators Pilot brought higher education faculty to campus for a weeklong workshop on adapting AI and machine learning materials for teaching across disciplines. ### Source excerpt A weeklong summer workshop brought higher education faculty to campus to explore how AI and machine learning materials can be adapted for their classrooms. ## GeoPT helps AI models simulate how objects respond to physical forces DevFeed: [GeoPT helps AI models simulate how objects respond to physical forces]() Original publisher: [Read original article]() Author: Alex Shipps | MIT CSAIL Published: 2026-08-10T19:25:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [AI Models](), [Simulation](), [AI Research](), [Computer Science and Artificial Intelligence Laboratory (CSAIL)](), [Robotics]() Tags: [3-d-imaging](), [ai](), [ai-models](), [artificial-intelligence](), [computational-fluid-dynamics-cfd](), [computer-graphics](), [computer-modeling](), [computer-science-and-artificial-intelligence-laboratory-csail](), [computer-science-and-technology](), [crash-simulation](), [design](), [electrical-engineering-and-computer-science-eecs](), [geometric-pre-training](), [geopt](), [haixu-wu](), [human-computer-interaction](), [kaiming-he](), [manufacturing](), [minghao-guo](), [mit-csail](), [mit-eecs](), [mit-schwarzman-college-of-computing](), [neural-physics-simulation](), [paper](), [physics](), [physics-aware-ai](), [physics-foundation-models](), [research](), [robotics](), [school-of-engineering](), [self-supervised-learning](), [simulation](), [surrogate-modeling](), [synthetic-dynamics](), [transformer-based-simulators](), [wojciech-matusik]() ### AI overview Researchers at MIT CSAIL and Tsinghua University developed GeoPT, a pre-training approach that uses 3D simulations of mechanical interactions to help AI models learn physics more efficiently. The article reports that models using the approach reached peak performance twice as fast and trained on up to 60 percent less data than leading models. ### Source excerpt "GeoPT" helps AI models understand the basics of physics so they can simulate how objects respond to things like wind and water more efficiently and accurately. ## Devavrat Shah's research and Ikigai Labs use tabular data for real-time forecasting and decision-making DevFeed: [Devavrat Shah's research and Ikigai Labs use tabular data for real-time forecasting and decision-making]() Original publisher: [Read original article]() Author: David Chandler | Laboratory for Information and Decision Systems Published: 2026-07-14T20:25:00Z Content type: article Language: en Sources: [MIT AI News]() Topics: [Artificial Intelligence](), [decision-making](), [Structured-data](), [AI Models](), [data](), [Time Series](), [Computer science](), [Electrical engineering and computer science (EECS)](), [Temporal data](), [systems]() Tags: [ai](), [ai-and-decision-making](), [ai-in-business-planning](), [ai-models](), [algorithms](), [artificial-intelligence](), [business-and-management](), [business-modeling](), [celonis-context-model](), [computer-science-and-technology](), [data](), [data-systems](), [decision-making](), [devavrat-shah](), [electrical-engineering-and-computer-science-eecs](), [faculty](), [forecasting](), [idss](), [ikigailabs](), [laboratory-for-information-and-decision-systems-lids](), [mit-eecs](), [mit-idss](), [mit-intellectual-property](), [mit-lids](), [mit-schwarzman-college-of-computing](), [predictions](), [profile](), [real-time](), [research](), [school-of-engineering](), [startups](), [structured](), [tabular-data](), [time-series-data]() ### AI overview MIT Professor Devavrat Shah's research led to a foundation model for tabular and time-series enterprise data. Developed with Ikigai Labs, the system continuously tests predictions against real outcomes to support large-scale forecasting and decision-making. ### Source excerpt Through research and entrepreneurship, Professor Devavrat Shah is helping to design methods that can handle constant decision-making using limited computational resources. ## SceneSmith uses collaborative AI agents to create 3D environments for robot training DevFeed: [SceneSmith uses collaborative AI agents to create 3D environments for robot training]() Original publisher: [Read original article]() Author: Alex Shipps | MIT CSAIL Published: 2026-07-13T18:50:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [AI research agents](), [robot grasping simulation](), [Simulation](), [vlm](), [Artificial Intelligence](), [Computer Science and Artificial Intelligence Laboratory (CSAIL)]() Tags: [3-d](), [3d](), [adversarial-machine-learning](), [agents](), [ai-agents](), [artificial-intelligence](), [computer-science-and-artificial-intelligence-laboratory-csail](), [computer-science-and-technology](), [electrical-engineering-and-computer-science-eecs](), [foundation-models](), [general-purpose-robotics](), [gpt-5-2](), [machine-learning](), [mit-csail](), [mit-eecs](), [mit-schwarzman-college-of-computing](), [national-science-foundation-nsf](), [nicholas-pfaff](), [research](), [robot-simulations](), [robotics](), [robots](), [russ-tedrake](), [scene-generation](), [scenesmith](), [school-of-engineering](), [simulation](), [simulation-ready-indoor-scenes](), [virtual-playgrounds](), [vision-language-models-vlms](), [zero-shot-policy]() ### AI overview MIT CSAIL and Toyota Research Institute researchers developed SceneSmith, a system that uses three collaborative AI agents to create realistic 3D environments for robot training. The scenes can be loaded into physics simulation software, allowing robots to practice tasks before real-world testing. ### Source excerpt "SceneSmith" system uses collaborative AI agents to create realistic 3D environments of places like kitchens, hotels, and living rooms, where robots can simulate everyday chores. ## Tiny robot boats build floating structures DevFeed: [Tiny robot boats build floating structures]() Original publisher: [Read original article]() Author: Rachel Gordon | MIT CSAIL Published: 2026-07-09T15:50:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Robotics](), [Computer Science and Artificial Intelligence Laboratory (CSAIL)](), [Computer science]() Tags: [alejandro-gonzalez-garcia](), [aquatic-robots](), [architecture](), [artificial-intelligence](), [autonomous-vehicles](), [bioinspiration](), [cities](), [computer-science-and-artificial-intelligence-laboratory-csail](), [computer-science-and-technology](), [daniela-rus](), [electrical-engineering-and-computer-science-eecs](), [fire-ants](), [floatform](), [floating-infrastructure](), [hybrid-coordination](), [infrastructure](), [machine-learning](), [mit-csail](), [mit-eecs](), [mit-schwarzman-college-of-computing](), [mit-sea-grant](), [mit-sea-grant-program](), [mit-senseable-city-lab](), [modular-robotic-boats](), [modular-self-reconfigurable-robots](), [niklas-hagemann](), [nonlinear-hydrodynamics](), [raft](), [research](), [robot-boats](), [robot-self-assembly](), [robotics](), [robots](), [school-of-architecture-and-planning](), [school-of-engineering](), [science](), [self-reconfiguration](), [senseable-city-lab](), [snap](), [swarm](), [swarm-robots](), [tiny](), [transportation](), [ultrasonic-beacon-system](), [urban-studies-and-planning](), [water](), [wei-wang]() ### AI overview MIT researchers developed FloatForm, a swarm of small robotic boats that can assemble into reconfigurable structures on the water and later break apart and reassemble into new configurations with minimal human direction. ### Source excerpt MIT researchers developed FloatForm, a swarm of small aquatic robots that snap together like ants forming a raft, assembling into reconfigurable structures on the water.