Meet U of T’s newest cohort of Schmidt AI in Science Postdoctoral Fellows

Apr 2, 2026

U of T’s newest Eric and Wendy Schmidt AI in Science Postdoctoral Fellows are harnessing AI to revolutionize everything from sustainable development to expanding our understanding of the Milky Way.

*Left to right, top row: Addis Alaminie, Kevin McKinnon, Ameya Pore, Afeez Popoola; middle row: Ryan Yeung, Rohan Dahale, Ju Huang, Thomas Schnake; bottom row: Jiaru Bai, Ioana Zelko, Xu Charlie Chen*.

From advancing our understanding of how the Milky Way formed and evolved, to supporting global climate adaptation and sustainable water management, the University of Toronto’s newest Eric and Wendy Schmidt AI in Science Postdoctoral Fellows are pioneering revolutionary AI-driven research across diverse scientific fields.

“Partnering with Schmidt Sciences, U of T is cultivating safe and responsible AI research, supporting emerging scholars as they push the boundaries of innovation and discovery,” says Timothy Chan, U of T’s associate vice-president and vice-provost, strategic initiatives.

Powering the next generation of AI-driven research

Launched in 2022 by Schmidt Sciences – a philanthropic initiative founded by former Google CEO Eric Schmidt and his wife Wendy Schmidt – the Schmidt AI in Science Postdoctoral Fellowship program supports the next generation of AI-driven scientific research.

The program was funded through a gift spanning nine partner universities and aims to accelerate AI-driven discoveries across a wide range of scientific disciplines. It is designed to boost the work of early-career scholars in engineering, mathematics and natural science by offering them collaborative research opportunities, a strong lineup of workshops, seminars and conferences, as well as hands-on training in AI applications. U of T is the sole Canadian participant in the program.

“We are seeing firsthand how this fellowship is giving early-career scientists the freedom to explore new approaches and push the boundaries of discovery,” says Lisa Strug, director of U of T’s Data Sciences Institute and co-lead of the Schmidt AI in Science Postdoctoral Fellowship program.

Fellows sharing innovative research with global impact

The first cohort of Schmidt AI in Science Fellows have moved onto new opportunities, and the second cohort is now well into the final year of their fellowships, with a focus on sharing their innovative research. They have been busy presenting their work at international conferences through invited talks and panel discussions. Four of the cohort two fellows were also awarded a competitive workshop grant from Schmidt Sciences and held a successful workshop on foundation models in November 2025.
Third cohort tackling urgent global challenges

The new, third cohort of 11 researchers brings fresh expertise and ambition, with much anticipation surrounding the impact of their work.

“Armed with advanced AI tools and training, this exceptional group of scientists is ready to tackle some of the world’s most urgent challenges with innovative solutions,” says Alán Aspuru-Guzik, director of U of T’s Acceleration Consortium and co-lead of the Schmidt AI in Science Postdoctoral Fellowship program.

Meet the fellows advancing AI across disciplines

Consistent with the program’s mission, U of T’s third cohort of Schmidt AI in Science Postdoctoral Fellows is working across multiple areas of study, from engineering, astronomy and astrophysics, psychology and earth sciences. Here is a look at all 11 scholars and their broad, groundbreaking research studies:

Partnering with Schmidt Sciences, U of T is cultivating safe and responsible AI research, supporting emerging scholars as they push the boundaries of innovation and discovery.

Addis Alaminie: Using case studies in the Nile and Peace Rivers, Addis’ project, “AI-Driven Framework for Planning Water Resources in Large River Basins” supports global climate adaptation and sustainable development with its AI-powered framework for sustainable water management. Addis is working in the St. George campus’ Department of Civil and Mineral Engineering and Department of Mathematics under Professors Mohammed Basheer (engineering) and Jude Kong (mathematics).

Partnering with Schmidt Sciences, U of T is cultivating safe and responsible AI research, supporting emerging scholars as they push the boundaries of innovation and discovery.

Afeez Popoola is a geophysicist exploring the links between seismology and geomechanics using rock physics and numerical simulations. Through his study, “Artificial Intelligence for Optimizing Geothermal Reservoir Performance Using Coupled Surface Deformation and Subsurface Measurements,” he aims to optimize geothermal energy production and support scalable clean energy and decarbonization goals. Afeez works in the St. George Campus’ GeoDecision Lab in the Department of Earth Sciences under Associate Professor Andrei Swidinsky and Professor Sebastian Jaimungal (statistical sciences).

Amey Pore: Amey’s project, SurgicalVLA, uses vision-language to enable robot-assisted surgery. The technology is designed to interpret human instructions and adapt actions in real time, with potential for broad clinical and commercial impact. He works under Assistant Professor Lueder A. Kahrs at U of T Mississauga’s Department of Mathematics and Computational Sciences.

Ioana Zelko’s project, DarkMatterFM is an AI-powered platform that accelerates our understanding of the nature of dark matter. Her project combines a curated library of astronomical data and simulators, enabling quick and transparent comparison of evidence. She works under the supervision of Professor Jo Bovy, Chair of the David A. Dunlap Department of Astronomy and Astrophysics on the St. George Campus.

Jiaru Bai: Through his study, “Onboarding Human Intelligence in Remote Machines with Cognitive Operating Systems,” Jiaru is developing a cognitive operating system to enhance autonomous decision-making in scientific experimental processes. Jiaru is working in the Department of Chemistry on the St. George Campus under the supervision of Professor Alán Aspuru-Guzik.

Armed with advanced AI tools and training, this exceptional group of scientists is ready to tackle some of the world’s most urgent challenges with innovative solutions.

Ju Huang’s project, “Quantum Machine Learning Discovery of Nanoporous Materials for Direct Air Carbon Capture,” focuses on building models for nanoporous materials to speed up the discovery of new adsorbents for direct air capture. She works in chemical engineering on the St. George Campus under the supervision of Professors Mohamad Moosavi and Anatole von Lilienfeld.

Armed with advanced AI tools and training, this exceptional group of scientists is ready to tackle some of the world’s most urgent challenges with innovative solutions.

Kevin McKinnon is building an AI-driven framework to understand how the Milky Way formed and evolved. Through his project, “Combined Telescope Astrometry: The Best Telescope is All the Telescopes,” he combines hierarchical models with Hubble Space Telescope and other data to improve astrometry for faint stars. Kevin works on the St. George Campus under Professors Gwendolyn Eadie in the Department of Astronomy and Astrophysics and Aviad Levis of the Department of Computer Science.

Rohan Dahale: Rohan’s project, “Bridging Black Hole Simulations and Observations with Machine Learning,” uses a cutting-edge machine learning technique called neural radiance fields to link computer simulations of supermassive black holes with real astronomical observation across multiple wavelengths of light. Researchers hope to use this technique to map how magnetic fields behave near a black hole’s event horizon, something that cannot be observed directly. The work will sharpen existing simulations and shed new light on how black holes feed on surrounding matter and produce sudden, powerful flares. Rohan works under the supervision of Assistant Professors Bart Ripperda at the Canadian Institute of Theoretical Astrophysics and Aviad Levis of the St. George campus’ Department of Computer Science.

Ryan Yeung: Through his project, “Deep Neural Networks to Objectively Decode Subjective Experiences in the Brain,” Ryan is developing an AI-driven cognitive neuroscience tool that uses deep learning to model and quantify subjective (emotion and visual imagery) human experiences. The goal is to advance understanding and treatment of mental health disorders at an unprecedented scope and scale. Ryan works at the Rotman Research Institute and Baycrest Academy for Research and Education under Senior Scientist Brian Levine and Associate Professor Bradley Buchsbaum.

Thomas Shnake: Thomas’ project, “Revisiting Electronic Spin in Quantum Chemistry Using Machine Learning and Explainable AI,” examines ways in which to calculate ‘electron spin’ in quantum chemistry (QC) calculations. This advancement could benefit various applications of QC computation, such as drug design and molecular dynamics simulations, and achieve better accuracy with relatively low computational cost. Thomas works with Professors Anatole von Lilienfeld, Department of Chemistry and Chris J. Maddison, Department of Computer Science on the St. George campus.

Xu (Charlie) Chen: Charlie’s project, “Accelerated Design of Synthetic Microbial Communities for Sustainable Chemical Manufacturing,” focuses on how machine learning can transform our ability to engineer microbial communities. Charlies works in the Department of Chemical Engineering and Applied Chemistry on the St. George Campus under Assistant Professors Christopher E. Lawson and Mohamad Moosavi.

Building engagement and connection

An integral priority of the Schmidt Fellows programs is to build a community among the scholars. This year, Fellows participated in a number of workshops and hands-on career-building seminars to enhance their experience in the program.

We are seeing firsthand how this fellowship is giving early-career scientists the freedom to explore new approaches and push the boundaries of discovery.

One such workshop, Dream Job Academy (DJA) was held in November 2025. Led by career educators from U of T’s Career Centre, DJA was an all-day workshop featuring a hands‑on, arts‑based session inviting Fellows to explore their career aspirations through a series of creative activities designed to encourage meaningful reflection and personal insight.

We are seeing firsthand how this fellowship is giving early-career scientists the freedom to explore new approaches and push the boundaries of discovery.

Reflecting on aspirations through visual art

Fellows used a variety of materials to depict their earliest career inspirations; an exercise that encouraged them to reconnect with the values and interests that initially shaped their research interests. They then created zines – mini magazines that described their professional journeys from childhood through to adulthood – followed by collages to help visualize future possibilities, potential challenges, obstacles and fears.

To conclude the day, Fellows built 3-D dioramas representing their ideal work environments. One memorable example was Cohort Two Fellow Ashley Dale’s “perfect lab,” which included a therapy dog and an empty workspace, reflecting her commitment to a balanced and supportive research culture.

Many participants described DJA as a transformative opportunity to step outside the conventional boundaries of STEM and embrace the value of using art as a lens for personal reflection and career exploration. Moreover, participating in the Dream Job Academy gave Fellows the opportunity to reconnect with their own motivation – the ‘why’ behind their career paths, which can feel forgotten during the day-to-day of research.

The 2026 call for applications for the Schmidt AI in Science Postdoctoral Program will be announced this summer. Please visit https://schmidtfellows.utoronto.ca/ for the latest updates.