A machine learning model trained with years’ worth of forecast and weather data predicts the accuracy of the weather forecast – the basis of a system that can reduce buildings’ energy usage by up to...Read more about To conserve energy, AI clears up cloudy forecasts
Fengqi You is the Roxanne E. and Michael J. Zak Professor at Cornell University, and is affiliated with the Smith School of Chemical and Biomolecular Engineering and the graduate fields of Operations Research and Information Engineering, Electrical and Computer Engineering, Civil and Environmental Engineering, Mechanical Engineering, Center of Applied Mathematics, and Systems Engineering Program. He also serves as the Associate Director of Cornell Energy Systems Institute. Before joining Cornell, 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 more than 120 peer-reviewed articles in leading journals, and has an h-index of 48. 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, BusinessWeek, 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 Sustainable Engineering Research Excellence Award (2017), Computing and Systems Technology (CAST) Outstanding Young Researcher Award from AIChE (2018), Cornell Engineering Research Excellence Award (2018), and ACS Sustainable Chemistry & Engineering Lectureship Award (2018), as well as a number of best paper awards and most-cited articles recognition. He is currently an Associate Editor of Computers & Chemical Engineering, a Consulting Editor of AIChE Journal, and an editorial board member of several leading journals (e.g. ACS Sustainable Chemistry & Engineering). His research focuses on novel 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 are an interdisciplinary systems engineering 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 application level, we concentrate our efforts on process, energy, and environmental systems engineering. Particular research interests lie in (1) sustainable design and synthesis of energy systems, including biofuels, photovoltaics, algae based biorefinery, carbon capture and separation, shale gas, and battery systems, (2) systems analysis, modeling and optimization for the food-energy-water-waste nexus and circular economy, (3) life cycle sustainability assessment of nanotechnology and advanced materials, (4) supply chain optimization and smart logistics, (5) smart manufacturing, planning, scheduling and control for complex engineering systems, (6) machine learning for industrial and agricultural big data analytics, (7) grey-box digital twins integrated mechanistic and data-driven modeling, and (8) data-driven decision-making under uncertainty. At the fundamental level, we focus on the development of advanced mathematical, computing, and artificial intelligence technologies to support research in the aforementioned application areas.
- 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
Computational Optimization, Industrial Big Data Analytics and Machine Learning, Energy Systems Engineering, and Process Design
- Ning, C., Fengqi You. 2017. "Data-driven Adaptive Nested Robust Optimization: General Modeling Framework and Efficient Computational Algorithm for Decision Making Uncertainty." AIChe Journal 63: 3790-3817.
- Yue, D., S. Pandya, Fengqi You. 2016. "Integrating Hybrid Life Cycle Assessment with Multiobjective Optimization: A Modeling Framework." Environmental Science & Technology 50: 1501-1509.
- Shang, C., X. Huang, Fengqi You. 2017. "Data-Driven Robust Optimization Based on Kernel Learning." Computers & Chemical Engineering 106: 464-479.
- Gao, J., Fengqi You. 2017. "Economic and Environmental Life Cycle Optimization Based on Noncooperative Supply Chains and Product Systems: Modeling Framework, Mixed-Integer Bilevel Fractional Programming Algorithm, and Shale Gas Application." ACS Sustainable Chemistry & Engineering 5: 3362-3381.
- Gong, J., S. B. Darling, Fengqi You. 2015. "Perovskite Photovoltaics: Life-Cycle Assessment of Energy and Environmental Impacts."Energy & Environmental Science 8: 1953-1968.
Selected Awards and Honors
- 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
BEng Tsinghua University, 2005
PhD Carnegie Mellon University, 2009