The Technical University of Denmark features Professor Fengqi You. He discusses quantum computing opportunities in chemical and biological manufacturing and highlights the importance of international... Read more about Quantum computing: A new paradigm in manufacturing
Fengqi You is the Roxanne E. and Michael J. Zak Professor at Cornell University, and is affiliated with the Graduate Fields of Chemical Engineering, Electrical and Computer Engineering, Operations Research and Information Engineering, Systems Engineering, Mechanical Engineering, Civil and Environmental Engineering, and Applied Mathematics. He also serves as Chair of PhD Studies in Cornell Systems Engineering, Associate Director of Cornell Energy Systems Institute, and Associate Director of Cornell Institute for Digital Agriculture. He was on the faculty of Northwestern University from 2011 to 2016, and worked at Argonne National Laboratory as an Argonne Scholar from 2009 to 2011. He has published over 200 refereed articles in journals such as Science, Nature Sustainability and Science Advances, and has an h-index of 65. Some of his research results have been editorially highlighted in Science and Nature, featured on journal covers (e.g. Energy & Environmental Science, ACS Sustainable Chemistry & Engineering, and Industrial & Engineering Chemistry Research), and covered by major media outlets (e.g. The New York Times, BBC, The Wall Street Journal, BusinessWeek, New Scientist, and National Geographic). His recent awards include American Institute of Chemical Engineers (AIChE) W. David Smith, Jr. Publication Award (2011), Northwestern-Argonne Early Career Investigator Award (2013), National Science Foundation 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 Young Investigator Award for Innovations in Green Process Engineering (2020), Mr. & Mrs. Richard F. Tucker Excellence in Teaching Award (2020), Curtis W. McGraw Research Award from ASEE (2020), and American Automatic Control Council (AACC) O. Hugo Schuck Award (2020), AIChE Sustainable Engineering Forum Education Award (2021), as well as a number of best paper awards. He is currently an editor of Computers & Chemical Engineering, an associate editor of AAAS journal Science Advances and of IEEE Transactions on Control Systems Technology, a consulting editor of AIChE Journal, and an editorial board member of several journals (e.g. ACS Sustainable Chemistry & Engineering and Industrial & Engineering Chemistry Research). His research focuses on novel computational models, optimization algorithms, statistical machine learning methods, and multi-scale systems analytics tools for smart manufacturing, digital agriculture, energy systems, and sustainability.
For more information about his research group: 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.
- Energy Systems
- Sustainable Energy Systems
- 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
- COVID-19 Related Research
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
- Shang, C., & You, F. (2021). A Posteriori Probabilistic Bounds of Convex Scenario Programs with Validation Tests. IEEE Transactions on Automatic Control, 66, 9, 4015-4028.
- Ajagekar, A., & You, F. (2021). Quantum Computing based Hybrid Deep Learning for Fault Diagnosis in Electrical Power Systems. Applied Energy, 303, 117628.
- Ning, C., & You, F. (2021). Online Learning Based Risk-Averse Stochastic MPC of Constrained Linear Uncertain Systems. Automatica, 125, 109402.
- Tian, X., Stranks, S.D., Fengqi You. 2020. "Life-cycle energy use and environmental implications of high-performance perovskite tandem solar cells." Science Advances, 6, eabb0055.
- Shang, C., Chen, W., Abraham Duncan Stroock, Fengqi You. 2020. "Robust Model Predictive Control of Irrigation Systems with Active Uncertainty Learning and Data Analytics." IEEE Transactions on Control Systems Technology, 28, 1493-1504.
- Zhao, S., Fengqi You. 2020. "Distributionally Robust Chance Constrained Programming with Generative Adversarial Networks (GANs)." AIChE Journal, 66, e16963.
- Ajagekar, A., Humble, T., Fengqi You. 2020. "Quantum Computing based Hybrid Solution Strategies for Large-scale Discrete-Continuous Optimization Problems." Computers & Chemical Engineering, 132, 106630.
For a complete list of publications please visit: https://www.peese.org/publications/
Selected Awards and Honors
- AIChE Sustainable Engineering Forum Education Award, 2021
- Fellow of the Royal Society of Chemistry (FRSC), 2021
- Mr. & Mrs. Richard F. Tucker Excellence in Teaching Award, 2020
- American Automatic Control Council (AACC) O. Hugo Schuck Award, 2020
- Curtis W. McGraw Research Award, ASEE, 2020
- AIChE Program Committee’s Young Investigator Award for Innovations in Green Process Engineering, 2020
- AIChE Excellence in Process Development Research Award, 2019
- Cornell Engineering Research Excellence Award, 2018
- Computing and Systems Technology (CAST) Outstanding Young Researcher Award of AIChE, 2018
- ACS Sustainable Chemistry & Engineering Lectureship Award 2018
- AIChE Sustainable Engineering Research Excellence Award 2017
- AIChE Environmental Division Early Career Award 2017
- National Science Foundation CAREER Award 2016
- Northwestern-Argonne Early Career Investigator Award for Energy Research 2013
B.Eng. Tsinghua University, 2005
Ph.D. Carnegie Mellon University, 2009