Cornell researchers interested in diverse topics ranging from peptide engineering and cellular metabolites to quantum physics and sustainable computing are among the newest cohort selected by the Eric and Wendy Schmidt AI in Science Postdoctoral Fellows program.
The 11 scholars constitute the fourth cohort from the six-year Eric and Wendy Schmidt AI in Science Postdoctoral Scholars initiative, a Schmidt Sciences program, which extended an invitation to Cornell in 2022. The $148 million initiative – part of a broader $400 million investment from Schmidt Sciences – funds investigators employing AI to advance exploration in science, technology, and engineering. To date, 46 researchers from Cornell have been awarded fellowships via the initiative.
"This year's cohort exemplifies the transformative potential of AI-driven scientific research," said Carla Gomes, the Ronald C. and Antonia V. Nielsen Professor in Cornell Bowers and co-director of the Cornell University AI for Science Institute (CUAISci), which collaborates closely with the fellowship initiative to identify and develop recipients from Cornell. "From biochemical innovations to global climate solutions, these fellows are demonstrating how AI can accelerate breakthrough discoveries that address some of our most pressing scientific challenges."
"These remarkable fellows are at the vanguard of a new era in AI research," said Fengqi You, the Roxanne E. and Michael J. Zak Professor in Energy Systems Engineering in the Cornell Duffield College of Engineering and co-director of CUAISci. "From understanding hydropower dam impacts to designing better catalysts to make green fuels and modeling global forest biomass, the fellows' contributions are positioned to tackle essential scientific obstacles in sustainability, the physical sciences, and beyond."
The following A&S researchers have received Schmidt AI in Science Postdoctoral Fellowships:
- Ellis Kennedy, College of Arts and Sciences, aims to enable the design of more durable and efficient catalysts for converting carbon dioxide into fuels by using machine learning (ML) and four-dimensional scanning transmission electron microscopy (4D-STEM) to understand how existing copper nanocatalysts break down during electrochemical reactions.
- Tamra Nebabu, College of Arts and Sciences, is using ML methods to tackle problems in quantum many-body physics — the study of large collections of interacting quantum particles like electrons or atoms — and using problems in the field to drive innovation in ML.
Tyler Schwertfeger, College of Arts and Sciences, aims to apply AI approaches to study the metabolome – the complete set of small molecules inside living cells – and to use the AI Metabolome Explorer, developed at Cornell, to identify and characterize the millions of currently unknown metabolites that constitute the vast "dark matter" of biological systems.
