Fengqi You is the Roxanne E. and Michael J. Zak Professor in Energy Systems Engineering at Cornell University. He is affiliated with multiple Graduate Fields at Cornell, encompassing Chemical Engineering, Computer Science, Electrical and Computer Engineering, Operations Research and Information Engineering, Systems Engineering, Mechanical Engineering, Civil and Environmental Engineering, and Applied Mathematics. Within Cornell, he serves as the Chair of Ph.D. Studies in Systems Engineering, Co-Director of the Cornell University AI for Science Institute (CUAISci), Co-Lead of the Schmidt AI in Science Program, and Co-Director of the Cornell Institute for Digital Agriculture (CIDA). Before starting his tenure at Cornell in 2016, he worked at Argonne National Laboratory’s Mathematics and Computer Science Division and served as a faculty member at Northwestern University. His research focuses on fundamental theory and methods of systems engineering and artificial intelligence, with applications spanning materials informatics, smart manufacturing, digital agriculture, quantum computing, energy systems, and sustainability. Fengqi has an h-index of 80 and authored over 250 refereed articles in journals such as Science, Nature Sustainability, Nature Communications, Science Advances, and PNAS. Parts of his research have earned editorial highlights in Science and Nature, features on dozens of journal covers (e.g., Energy & Environmental Science), and coverage in leading media outlets (e.g., TheNew York Times, BBC, Reuters, The Washington Post, TheWall Street Journal, Fortune, Daily Mail, Agence France-Presse, Bloomberg, Scientific American, Newsweek, BusinessWeek, The Hill, The Guardian, New Scientist, Popular Science, and National Geographic). He is an award-winning scholar and teacher, earning over 20 major national and international awards over the past six years from leading professional organizations, such as the American Institute of Chemical Engineers (AIChE), American Chemical Society (ACS), Royal Society of Chemistry (RSC), American Society for Engineering Education (ASEE), American Automatic Control Council (AACC), in addition to multiple best paper awards. Selected ones include NSF CAREER Award (2016), AIChE Environmental Division Early Career Award (2017), AIChE Research Excellence in Sustainable Engineering Award (2017), Computing and Systems Technology (CAST) Outstanding Young Researcher Award from AIChE (2018), Cornell Engineering Research Excellence Award (2018), ACS Sustainable Chemistry & Engineering Lectureship Award (2018), AIChE Excellence in Process Development Research Award (2019), AIChE Innovations in Green Process Engineering Award (2020), Mr. & Mrs. Richard F. Tucker Excellence in Teaching Award (2020), ASEE Curtis W. McGraw Research Award (2020), O. Hugo Schuck Award from AACC (2020), AIChE Sustainable Engineering Forum Education Award (2021), AIChE George Lappin Award (2022), and Stratis V. Sotirchos Lectureship Award by Foundation for Research & Technology – Hellas (FORTH) (2022). He serves as an editor of Computers & Chemical Engineering; associate editor of AAAS journal Science Advances and IEEE Transactions on Control Systems Technology; consulting editor of AIChE Journal; subject editor of Advances in Applied Energy; guest editor of Energy, Journal of Cleaner Production, and Renewable & Sustainable Energy Reviews; and is on the editorial boards of ACS Sustainable Chemistry & Engineering, Industrial & Engineering Chemistry Research, PRX Energy, and more. He is an elected Fellow of the Royal Society of Chemistry (FRSC), Fellow of the AIChE, and Fellow of the American Association for the Advancement of Science (AAAS).
His research group comprises approximately 20 Ph.D. students and postdoctoral fellows, along with dozens of master's students. For further details about his research group, visit: www.peese.org
We are an interdisciplinary systems engineering and artificial intelligence research group that focuses on the development of advanced computational models, optimization algorithms, statistical machine learning methods, and multi-scale systems analysis tools for smart manufacturing, digital agriculture, data analytics, energy systems, and sustainability. We seek to provide a balance between theory, computation and real-world applications through our synergistic research that includes both fundamentals and applications. At the fundamental level, we focus on the development of novel and advanced mathematical, computing, and artificial intelligence technologies. At the application level, we concentrate our efforts on process, energy, and environmental systems engineering. Particular research interests lie in (1) decarbonization, carbon-neutrality, and sustainable design of energy systems, including biofuels, photovoltaics, waste-to-energy, carbon capture and separation, shale gas, geothermal, and battery systems, (2) systems analysis, modeling and optimization for the food-energy-water-waste nexus and circular economy, (3) industrial ecology and life cycle sustainability assessment of nanotechnology and advanced materials, (4) material informatics and computer-aided molecular design, (5) supply chain optimization and smart logistics, smart manufacturing, planning, scheduling and control for complex engineering systems, (6) industrial big data analytics and machine learning for soft sensor and IoT, (7) grey-box digital twins and hybrid modeling based on mechanistic and data-driven approaches, and (8) quantum computing and quantum artificial intelligence.
Computational Optimization, Industrial Big Data Analytics and Machine Learning, Deep Learning, Quantum Computing and Artificial Intelligence, Life Cycle Assessment and Industrial Ecology, Energy Systems Engineering, and Process Design
For a complete list of publications please visit: https://www.peese.org/publications/
B.Eng. Tsinghua University, 2005
Ph.D. Carnegie Mellon University, 2009
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