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);

“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);

“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);

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

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

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

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

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

Analog Devices

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

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

The Coca-Cola Company

“Leveraging Generative AI to Bioengineer Localized Plant Therapeutics to Save the Oranges,” led by Sergey Ovchinnikov (Biology) and Tami Lieberman (CEE)

Intersystems

“Emergency Department Companion,” led by Dimitris Bertsimas (MIT Sloan)

“Optimized AI Systems for Data Extraction and Processing,” led by Michael Cafarella (CSAIL) and Samuel Madden (EECS)

“Towards Rational Generative AI for Data-Driven, Knowledge-Backed Expertise,” led by Josh Tenenbaum (Brain and Cognitive Sciences)

OpenAI

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

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

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

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

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

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

SK Telecom

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

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

Tata

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

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

TWG

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

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