U of T Schmidt AI Fellows explore how artificial intelligence can accelerate scientific discovery
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U of T Schmidt AI Fellows explore how artificial intelligence can accelerate scientific discovery
Feb 26, 2026
Schmidt AI in Science Fellows and collaborators gather at the Foundation Models for Science workshop, celebrating three days of hands‑on exploration, multi-team problem‑solving and community-building at U of T.
With the rapid growth of artificial intelligence, a group of fellows at the University of Toronto set out to see how foundation models could be used to accelerate scientific discovery with a workshop that drew researchers from around the world.
Last November, Eric and Wendy Schmidt AI in Science Postdoctoral Fellows Ashley Dale, Biprateep Dey, Ishrath Mohamed Irshadeen and David Pellow organized the Foundation Models for Science workshop to explore the use of foundation models in science – think ChatGPT but trained on massive datasets specific to fields like biology, astrophysics or chemistry.
Artificial intelligence is no longer simply transforming our methods. It has become essential to the very questions we are able to ask and answer.
Foundation models are increasingly becoming a valuable tool for scientists, particularly in areas where there isn’t a lot of high-quality data available for traditional machine learning. They can be adapted to handle specific tasks or used to create data patterns or summaries that help train other models to make predictions.
Artificial intelligence is no longer simply transforming our methods. It has become essential to the very questions we are able to ask and answer.
Overwhelming response to workshop
Hands-on learning in full gear at the workshop as researchers dive into foundation model tutorials, sparking ideas, collaboration and rapid discovery.
With close to 70 participants from across Asia, Europe and North America, the response from the AI scientific community was overwhelmingly positive. Funded by a competitive Schmidt Sciences’ Community Initiative Fund grant, the event brought together a diverse group of postdocs, graduate students and early-career faculty for three days of learning, collaboration and hands-on problem-solving.
Held at the Schwartz Reisman Innovation Campus, the workshop set out to identify leading foundation models across disciplines, showcase multi-team problem-solving and foster a sense of community through collaborative engagement.
“This workshop was quite timely as we are approaching an era of AI for science where the availability of high-quality training data has become the bottleneck, as the architectures of machine learning models are evolving rapidly,” says co-organizer, Mohamed Irshadeen.
Yet, despite their growing importance, foundation models haven’t been exploited to their full potential in the scientific community – a critical gap the workshop set out to fill.
The four organizers, all members of the second cohort of the Schmidt AI in Science Postdoctoral Program at U of T, designed the workshop to be educational and practical with hands-on tutorials and hackathons – fast-paced sessions centred on innovation and problem-solving.
From hackathon project to published research
Kevin McKinnon presents his team’s climate‑aware cherry blossom prediction model, highlighting the power of foundation models in real‑world science.
One interdisciplinary team of material scientists, agriculturalists and astronomers included Assistant Professor Joshua Speagle from the Department of Statistical Sciences and the David A. Dunlap Department of Astronomy & Astrophysics (DADDAA), and Schmidt AI in Science Postdoctoral Research Fellow Kevin McKinnon (DADDAA). They converted their hackathon project into a published workshop paper, “Predicting Cherry Blossom Peak Bloom in Toronto Through Climate-Aware Tabular Foundation Models” – a true reflection of the collaborative nature of this workshop.
With the establishment of many institutional strategic initiatives (ISIs), including the Acceleration Consortium, Data Sciences Institute and the Schwartz Reisman Institute for Technology and Society – as well as many other tri-campus initiatives – the event strengthened U of T’s position as a leading force in the acceleration of AI.
Bridging the gap between AI and science
With Schmidt Fellows belonging to many different departments and divisions, the program fosters AI integration across diverse scientific domains.
Now in its fourth year, the program has fortified strong partnerships amongst program partners, including the Vector Institute for Artificial Intelligence, through the co-creation of multiple programs.
Collectively, these efforts are building a robust training community of junior researchers who are committed to bridging the gap between traditionally trained scientists and the AI revolution transforming research worldwide.
“The success of this workshop – from securing a highly competitive grant to drawing an international audience – highlights a pivotal shift in how we do science. Artificial intelligence is no longer simply transforming our methods. It has become essential to the very questions we are able to ask and answer,” says Lisa Strug, director of U of T’s Data Sciences Institute and co-lead of the Schmidt AI in Science Postdoctoral Fellowship program.
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