Thirteen Cornell postdoctoral researchers intent on leveraging artificial intelligence (AI) in areas as varied as astronomy, computational biology, and psychology have been named Eric and Wendy Schmidt AI in Science Postdoctoral Fellows, a Schmidt Futures program.
This is the second cohort of Schmidt AI in Science Postdoctoral Fellows. In 2022, Cornell was selected as one of nine universities worldwide to join the Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship, a $148 million program that is part of a larger $400 million effort from Schmidt Futures to support AI researchers.
Now in the second year of the six-year fellowship program, the Cornell University AI for Science Institute (CUAISci) will continue to recruit and train up to 100 Schmidt AI in Science Postdoctoral Fellows in the fields of natural sciences and engineering. CUAISci is part of the university’s larger Artificial Intelligence Radical Collaboration and consists of Cornell faculty and researchers from diverse fields who seek to apply AI for scientific discovery, with sustainability being the overarching goal.
“AI has arrived and is primed to radically transform science and our world,” said Carla Gomes, the Ronald C. and Antonia V. Nielsen Professor in Cornell Bowers CIS and co-director of CUAISci. “These newest Schmidt in Science AI fellows are among the young, bright minds exploring this new frontier in AI and striving to leverage its full potential to drive scientific breakthroughs for the greater good.”
“We are thrilled to welcome our second cohort of Schmidt AI in Science Postdocs,” said Fengqi You, the Roxanne E. and Michael J. Zak Professor in Energy Systems Engineering and co-director of CUAISci. “These are exceptionally talented scholars with diverse backgrounds who will leverage advanced AI and domain expertise to address pressing societal challenges, accelerate discoveries, and catalyze transformational impact across multiple disciplines in science and engineering.”
This year’s cohort of Schmidt AI in Science Postdoctoral Fellows are:
• Zhongmou Chao, chemical and biomolecular engineering (College of Engineering), uses synthetic biology to build an artificial nose on a chip, and decodes the smell using machine learning (ML).
• Sebastian Heilpern, public and ecosystem health (College of Veterinary Medicine), leverages AI to understand how best to balance nutrition, energy, and biodiversity goals in aquatic ecosystems.
• Ling-Wei Kong, computational biology (College of Agriculture and Life Sciences), applies ML to the complex nonlinear dynamics in ecology and climate systems. He also explores ML-assisted modeling in animal behavior and neuropsychological processes.
• Chia-Hao Lee, applied and engineering physics (Cornell Engineering), explores the fusion of AI with electron microscopy to achieve sub-angstrom resolution characterization of quantum and energy materials.
• Shuangqi Li, systems engineering (Cornell Engineering), studies AI techniques for battery material discovery, electrochemical structure design, performance prediction, and sustainable development, with a focus on transportation electrification and decarbonization.
• Krishnanand Mallayya, physics (College of Arts and Sciences), studies how to use AI and ML to gain theoretical physics insights from quantum matter using voluminous and complex experimental data such as synchrotron X-ray diffraction.
• Imanol Miqueleiz, natural resources and the environment (College of Agriculture and Life Sciences), studies how multi-objective optimization can address global priorities for freshwater conservation to expand the current network of protected areas.
• Roy Moyal, psychology (College of Arts and Sciences), develops spiking neural network algorithms for rapid chemosensory learning in natural environments, to be deployed on neuromorphic hardware like Intel Loihi, a tiny research chip.
• Chinthak Murali, astronomy (College of Arts and Sciences), works on building neural networks that can detect and characterize various astrophysical signals such as nanohertz gravitational waves and fast radio bursts more efficiently and robustly than conventional methods.
• Xin Sun, chemical and biomolecular engineering (College of Engineering), studies multiple objective optimization to simultaneously reduce the multidimensional sustainability impacts of global battery material flow network for improving the sustainability of the climate-energy-material nexus.
• Feng Tao, ecology and evolutionary biology (College of Arts and Sciences and College of Agriculture and Life Sciences), studies process-guided artificial intelligence and explores enhanced rock weathering to promote soil inorganic carbon as a scalable carbon dioxide removal method.
• Fan Wu, applied and engineering physics (College of Engineering), applies ML to a quantum imaging system, which is capable of performing super-resolution measurements close to the Heisenberg limit, via training and manipulating the complex highly entangled multimode field.
• Bu Zhao, civil and environmental engineering (College of Engineering), studies how to use ML to understand the distribution of microplastic in the global freshwater system.
By Louis DiPietro, a writer for the Cornell Ann S. Bowers College of Computing and Information Science.