Fengqi You

Biography

Fengqi You is the Roxanne E. and Michael J. Zak Professor in Energy Systems Engineering at Cornell University. He holds affiliations with multiple Graduate Fields at Cornell, including 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-Director of the Cornell Institute for Digital Agriculture (CIDA), and Director of the Cornell AI for Sustainability Initiative (CAISI). Before joining 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 theories and methods of systems engineering, with applications in materials informatics, smart manufacturing, digital agriculture, energy systems, and sustainability. Fengqi has an h-index of 88 and has authored over 300 refereed articles in journals such as Nature, Science, Nature Sustainability, Nature Food, Nature Communications, Science Advances, and PNAS. His research has garnered editorial highlights in Science and Nature, featured on dozens of journal covers (e.g., Energy & Environmental Science), and covered by leading media outlets (e.g., New York Times, BBC, Reuters, Washington Post, Forbes, Wall Street Journal, Fortune, Daily Mail, The Guardian, Agence France-Presse, Bloomberg, Scientific American, Newsweek, BusinessWeek, Hill, CNN, Harvard Business Review,New Scientist, and National Geographic).  He is an award-winning scholar and teacher, having received over 25 major national and international awards in 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), and American Automatic Control Council (AACC), in addition to multiple best paper awards. Selected ones include the 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), Stratis V. Sotirchos Lectureship Award by the Foundation for Research & Technology – Hellas (FORTH) (2022), and the Lawrence K. Cecil Award in Environmental Chemical Engineering (2024). He serves as an editor of Computers & Chemical Engineering; associate editor of the AAAS journal Science Advances, Applied Energy, and IEEE Transactions on Control Systems Technology; consulting editor of the AIChE Journal; subject editor of Advances in Applied Energy; guest editor of Energy, Journal of Cleaner Production, and Renewable & Sustainable Energy Reviews; and was 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 associates. For more information about his research group, please visit: www.peese.org

Research Interests

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.

• Energy Systems
• Sustainable Energy Systems
• Algorithms
• Artificial Intelligence
• Complex Systems, Network Science and Computation
• Energy and the Environment
• Statistics and Machine Learning
• Computational Science and Engineering
• Information Theory and Communications
• Scientific Computing
• Systems and Networking
• Infrastructure Systems
• Data Mining
• Data Science
• Sensors and Actuators
• Optical Physics
• COVID-19 Related Research
• Robotics and Autonomy
• Optimization