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 researchers have received Schmidt AI in Science Postdoctoral Fellowships:
- Cheng Feng, Cornell Duffield Engineering, is developing a framework to address AI's sustainability issues, which will include quantifying global hydropower potential for AI energy demands, evaluating whether waste heat from data centers can be reused, and modeling the biodiversity impacts of hydropower-supported AI systems.
- Guido A. Herrera-R, College of Agriculture and Life Sciences (CALS), plans to synthesize global evidence of dam impacts on fisheries and predict future hydropower effects, addressing trade-offs between renewable energy and food security through AI-guided multi-objective optimization, while also improving global mapping of freshwater fish biodiversity.
- 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.
- Rong Li, CALS, is developing methods that use observations and AI to make more accurate estimates of forest biomass and to predict forest biomass dynamics.
- Caihua Liu, CALS, is creating a system to automatically convert unstructured biodiversity text, like species accounts and field guides, into structured, analysis-ready life history data, which will enable scientists to use the information for biodiversity research and conservation decisions.
- Chenbei Lu, Cornell Duffield Engineering, will use AI methods to investigate how to make controlled-environment greenhouses more sustainable and energy-efficient, quantify how global climate affects their performance, and identify ecological thresholds for sustainable large-scale deployment.
- 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.
- Siyuan Wang, Cornell Duffield Engineering, proposes to leverage advanced AI techniques to design novel peptides for sustainable energy and agricultural applications, including peptide engineering for green fuel generation, enhanced bioelectrochemical systems, and eco-friendly biopesticides.
- Rohan Singh Wilkho, Cornell Duffield Engineering, plans to use AI to learn from decades of water management records, developing decision-making models to guide sustainable water infrastructure management amid changing climate and policy conditions.
- Tianqi Xiao, Cornell Duffield Engineering, is creating scalable and sustainable solutions to the water and energy demands of AI computing infrastructure by integrating low-carbon renewable energy sources like geothermal power and developing strategies to minimize carbon and water footprints.
Patricia Waldron is a writer for the Cornell Ann S. Bowers College of Computing and Information Science.