MGAIC Projects

“A Pilot Study on LLM-Based Extension Services in Smallholder Agriculture,” led by Ali Aouad (MIT Sloan);

“AI Adoption and Organizational Inequality,” led by Nathan Wilmers (MIT Sloan);

“The Impact of US Electricity Infrastructure on Data Center Development,” led by Mert Dermier (MIT Sloan);

“HypSynth: Generative AI for Science through Multi-Agent Hypothesis Refinement,” led by Satrajit Ghosh (McGovern Institute for Brain Research);

“Leveraging Large Language Models to Incorporate Qualitative Information into Decision-Making Models in Transportation and Logistics,” led by Alexandre Jacquillat (MIT Sloan);

“Pareto Machines: Training AI Agents to Find Positive-Sum Solutions,” led by Sinan Aral (MIT Sloan);

“Physics Predictions from Physical Generative AI,” led by Jesse Thaler (Physics).

“3D Generative AI for Physical Realism: Encoding Physical Properties into Generative AI models to Create Physically Valid 3D Models,” led by Stefanie Mueller (EECS; MechE);

“Advancing the Potential of In-Silico Trials and Generative AI in Health with Controllable Diffusion Models and Global Outreach,” led by Elazer Edelman (IMES) and Joseph J. Frassica (IMES);

“Agentic AI for Interpretative and Predictive Bio-inspired Materials Discovery Using Autonomous Experimentation and Self-Learning,” led by Markus J. Buehler (CEE);

“AI Coach for Skilled Career Navigation,” led by John Hart (MechE) and John Liu (MechE);

“AI Empowered Human-Centered Design: From Identifying Latent Human Needs to Generating Human-Guided Designs of Physical Systems,” led by Caitlin Mueller (CEE; Architecture) and Maria Yang (MechE);

“Building a Time Series Foundation Model to Listen to the Universe,” led by Philip Harris (Physics), Erik Katsavounidis (Physics), Yury Polyanskiy (EECS), George Ricker (MIT Kavli Institute), and Daniela Rus (CSAIL);

“AI-Driven Tutors and Open Datasets for Early Literacy Education,” led by Satrajit Ghosh (McGovern Institute for Brain Research) and John Gabrieli (McGovern Institute for Brain Research);

“Bridging Statistical Physics and Generative AI: Scaling Laws, Robustness, and Spectral Control,” led by Marin Soljacic (Physics);

“Conditional Generation of Micro-Level Household Consumption with Applications to Energy,” led by Christopher Knittel (MIT SLOAN);

“Developing jam_bots: Real-Time Collaborative Agents for Live Human-AI Musical Improvisation,” led by Joseph A. Paradiso (MIT Media Lab), Anna Huang (Music and Theater Arts; EECS), and Eran Ergozy (Music and Theater Arts);

“Efficient GPU Sparse Automatic Differentiation for Scientific Computing,” led by Justin Solomon (EECS) and Jonathan Ragan-Kelley (EECS);

“Empowering Underserved Students: AI-Driven Calculus Tutoring for Equitable Education,” led by Eric Klopfer (Comparative Media Studies/Writing) and Cynthia Breazeal (MIT Media Lab);

“Generative AI as a Therapeutic Intervention for Alleviating Insistence on Sameness in Autism,” led Pawan Sinha (Brain and Cognitive Sciences);

“Generative AI for Automated Experimental Setup and Alignment in Optics and Beyond,” led by Marin Soljacic (Physics) and Pulkit Agrawal (EECS);

“Generative AI for Predicting Citrus Greening Spread,” led by Sherrie Wang (MechE; IDSS);

“Generative AI for Enhancing Risk Management of Software Supply Chain,” led by Retsef Levi (MIT Sloan);

“GENIUS: GENerative Intelligence for Urban Sustainability,” led by John E. Fernández (Architecture), Omar Khattab (EECS), and Norhan Bayomi (MIT Environmental Solutions Initiative);

“Glia: An AI Assistant to Design High-Performance GenAI Systems,” led by Mohammad Alizadeh (EECS) and Hari Balakrishnan (EECS);

“Giving Power Back to Humans in Visual Generative AI though Modularity and Compositionality,” led by Fredo Durand (EECS);

“Leveraging LLM dialogues to Build Trust in Critical Institutions,” led by Adam Berinksy (Political Science) and David Rand (MIT Sloan; Brain and Cognitive Sciences);

“Measuring and Mitigating Homogenization in Generative AI,” led by Manish Raghavan (MIT Sloan; EECS);

“Multimodal Exploratory Data Analysis with Semiformal Programming,” led by Arvind Satyanarayan (EECS);

“Multimodal Generative AI Framework for Brain Age Prediction and Future MRI Forecasting,” led by Laura Lewis (EECS; IMES) and Marzyeh Ghassemi (EECS; IMES);

“Neuromechanics-Grounded Imitation Learning for Predicting Locomotor Stability,” led by Nidhi Seethapathi (Brain and Cognitive Sciences);

“P3: A Generative AI Foundation Model for PDE Surrogate Modeling and Discovery,” led by Sili Deng (MechE);

“Personalized Learning with GenAI for MIT Open Learning Courseware,” led by Dimitris Bertsimas (MIT Sloan) and Ana Trisovic (EECS);

“Random Utility Models Meet LLM Alignment,” led by Vivek Farias (MIT Sloan) and Ali Aouad (MIT Sloan);

“Spectrum-Conditioned Generative Elucidation of Unknown Molecules,” led by Connor Coley (ChemE; EECS);

“Understanding Human Music Perception Using Generative AI,” led by Josh McDermott (Brain and Cognitive Sciences) and Anna Huang (Music and Theater Arts; EECS);

“Zero-Waste Tree: Neural Radiance Field Segmentation for Generative Timber Design,” led by Caitlin Mueller (CEE; Architecture) and Skylar Tibbits (Architecture).

“AI Models for Improving Human Agency,” led by Daron Acemoglu (Economics), Jacob Andreas (EECS), and Asuman Ozdaglar (EECS);

“Enabling Inductive Scientific Reasoning In Generative Models,” led by Rafael Gómez-Bombarelli (Materials Science and Engineering), Ju Li (Nuclear Science and Engineering), and Kaiming He (EECS);

“From Data Silos to GenAI: Federated and Synthetic Data Methods for Design and Manufacturing,” led by Faez Ahmed (MechE), John Hart (MechE), and Duane Boning (EECS);

“Interaction-Aware Generative Design of Novel Antimalarial Small Molecules,” led by Connor Coley (ChemE; EECS) and Jacquin Niles (Biological Engineering);

“LLMs augmented Textbooks: Personalized and Interactive Learning,” led by Antonio Torralba (EECS), Phillip Isola (EECS), William T. Freeman (EECS), and Amy Brand (MIT Press);

“Robust Information Gathering via Generative AI for Enhanced Decision Support during Disasters,” led by Saurabh Amin (CEE), Sai Ravela (CCSE), and Emma McDaniel (Lincoln Lab).

“A2rchi – AI Support for Classes and Research Resources Teams,” selected by TWG, and led by Tim Kraska (CSAIL) and Christoph Paus (Physics);

“AI-Driven Optimization of Water Supply Networks for Wildfire Resilience,” selected by OpenAI, and led by Carlo Ratti (DUSP);

“Black-Box Auditing with LLM Simulated Agents,” selected by OpenAI, and led by Chara Podimata (MIT Sloan);

“Building Robust and Scalable Enterprise Data Curation Applications with Agentic AI,” selected by TWG, and led by Samuel Madden (EECS);

“Efficient Robot Learning via Multimodal Feedback and VLM-Guided Augmentation,” selected by Tata, and led by Andreea Bobu (Aeronautics and Astronautics);

“Enhancing Biodiversity Image Datasets with Generative AI for Improved Species Classification,” selected by OpenAI, and led by Sara Beery (EECS) and Fabio Duarte (DUSP);

“High-Resolution and Multi-Modality Sense of Touch: Compact Terahertz Tactile Chip System for Advanced Robotic Training and Manipulation,” selected by Analog Devices, and led by Ruonan Han (EECS);

“Leveraging Generative AI to Bioengineer Localized Plant Therapeutics to Save the Oranges,” selected by The Coca-Cola Company, and led by Sergey Ovchinnikov (Biology) and Tami Lieberman (CEE);

“Manufacturability-Aware Generative AI for Next-Gen Topology Optimization,” selected by Tata, and led by Faez Ahmed (MechE) and Josephine C. Carstensen (CEE);

“Multi-Agent Large Language Models for Battery and Semiconductor Applications,” selected by SK Telecom, and led by Ju Li (Nuclear Science and Engineering), Kaiming He (EECS), and Rafael Gómez-Bombarelli (Materials Science and Engineering);

“Multimodal Tactile Sensing for Robotics,” selected by Analog Devices, and led by Wojciech Matusik (EECS) and Paul Liang (MIT Media Lab; EECS);

“Patient Phenotyping and Clinical Forecasting Using a Histogram-based Generative Model on Irregularly Sampled Medical Time Series Data,” selected by OpenAI, and led by Collin Stultz (EECS);

“Recapitulating Experimental Economics with AI Agents,” selected by OpenAI, and led by John Horton (MIT Sloan);

“Understanding How LLM Agents Deviate from Human Choices,” selected by SK Telecom, and led by Pattie Maes (MIT Media Lab);

“Voices of the Poor,” selected by OpenAI, and led by Sendhil Mullainathan (EECS; Economics) and Ashesh Rambachan (Economics).