Foundation Models Academic Research (AFMR) - circular background pattern

Accelerating Foundation Models Research

Engaging the broader community in reimagining computing research

“AI is evolving faster than we ever thought possible, and the opportunities it’s opening up are mind-blowing. But this is more than shiny new tech; it’s about making a difference in the world. That’s why we’re launching this program—to build a global network of researchers who are as diverse as the challenges we face. We’re not just talking about models and algorithms; we’re talking about crossing boundaries, be it language, culture, or academic fields, to tackle some really big issues. Simply put, we aim to ensure that AI isn’t just for the few, but a game-changer for us all.”

Peter Lee, Corporate Vice President, Microsoft Research & Incubations

Foundation models are fuelling a fundamental shift in computing research, natural sciences, social sciences and computing education itself. And as industry-led advances in AI continue to reach new heights, we believe that a vibrant and diverse research ecosystem is essential to realizing the promise of AI to benefit each individual, organization and society worldwide.

Accelerate Foundation Models Research (AFMR) is a new research initiative where we are on a journey, working together with the broader research community, to explore different aspects of foundation models to accomplish three goals:


Align AI with shared human goals, values, and preferences via research on models, which enhances safety, robustness, sustainability, responsibility, and transparency, while ensuring rapid progress can be measured via new evaluation methods


Improve human interactions via sociotechnical research, which increases trust, human ingenuity, creativity, and productivity, and decreases the digital divide while reducing the risks of developing AI which does not benefit individuals and society


Accelerate scientific discovery in natural sciences via proactive knowledge discovery, hypothesis generation, and multiscale multimodal data generation

AFMR is a global research network and a resource platform, which enables a network of researchers with different expertise to engage on solving together some of the greatest technical and societal challenges of tomorrow. AFMR includes a grant program that provides access to state-of-the-art foundation models hosted on Microsoft Azure through Microsoft Azure AI services.

Our goal is to foster more collaborations across disciplines, institutions, and sectors, and to unleash the full potential of AI for a wide range of research questions, applications, and societal contexts. To that effect we engage with the research community through Calls for Proposals, mind-swaps, workshops, conferences.

Our collaborators

Australian National University
Brigham and Women’s Hospital
Carnegie Mellon University
Charité – Universitätsmedizin Berlin, University of Berlin, Germany
Cornell University
Ecole Polytechnique Federale De Lausanne (EPFL)
Emory University
George Mason University
Georgia Institute of Technology
Gwangju Institute of Science And Technology
Harvard University
Hebrew University
Ho Chi Minh City University of Technology (HCMUT)
Illinois Institute of Technology
Imperial College London
Indian Institute of Science
Indian Institute of Technology, Bombay
Indian Institute of Technology, Delhi
Indian Institute of Technology, Gandhinagar
Indian Institute of Technology, Hyderabad
Indian Institute of Technology, Kharagpur
Indian Institute of Technology, Tirupati
Johns Hopkins University
KAIST South Korea
Kennesaw State University
Loughborough University
Massachusetts Institute of Technology
Monash University Malaysia
National University of Singapore
New York University
North Carolina A&T State University
Northeastern University
Oregon Health & Science University
Pennsylvania State University
Polytechnique Montreal
Princeton University
Rice University
Saarland University
Seoul National University
Simon Fraser University
Singapore University of Technology
Stanford University
The Ohio State University
The University of Hong Kong
The University of Nottingham
Tokyo Institute of Technology
Université de Montréal and Mila – Quebec AI Institute
University College London
University of Arizona
University of Bath
University of British Columbia
University of California, Berkeley
University of California, Los Angeles
University of California, San Francisco
University of California, Santa Cruz
University of California, Irvine
University of Cambridge
University of Chicago
University of Darmstadt
University of Illinois, Urbana-Champaign
University of Leeds
University of Maryland
University of Massachusetts, Amherst
University of Michigan, Ann Arbor
University of Missouri-Kansas City
University of New South Wales
University of North Carolina, Chapel Hill
University of North Carolina, Charlotte
University of North Texas
University of Notre Dame
University of Oxford
University of Pennsylvania
University of Rochester
University of Southern California
University of Texas, Austin
University of Texas, Southwestern Medical Center
University of Toronto
University of Toronto Scarborough
University of Virginia
University of Washington
University of Waterloo
University of Wisconsin-Madison
Waseda University
William & Mary
Yale University
York University

Interested in collaborating?

If you are interested in finding out more about our existing projects and potential expansion of the program in FY24 please contact the team at