Fengqi You is the Roxanne E. and Michael J. Zak Professor at Cornell University (Ithaca, New York). He also serves as Chair of Ph.D. Studies in Cornell Systems Engineering, Associate Director of Cornell Energy Systems Institute, and Associate Director of Cornell Institute for Digital Agriculture. His research focuses on fundamental theory and methods in systems engineering and artificial intelligence, as well as their applications to smart manufacturing, digital agriculture, quantum computing, energy systems, and sustainability. He is an award-winning scholar and teacher, having received around 20 major national/international awards over the past six years from 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 a number of best paper awards. Fengqi is an elected Fellow of the Royal Society of Chemistry (FRSC) and Fellow of the American Institute of Chemical Engineers (AIChE Fellow). For more information about his research group:www.peese.org
Quantum computing is attracting growing interest due to its unique capabilities and disruptive potential. This presentation will briefly introduce quantum computing and its potential applications to systems optimization and machine learning. We will introduce several new algorithms and methods that exploit the strengths of quantum computing techniques to address the computational challenges of classically intractable optimization problems. Applications include molecular design, manufacturing systems operations, and supply chain optimization. In the second half of the presentation, we will focus on quantum machine learning and the emerging hybrid classical-quantum computing paradigm that exploit the strengths of quantum computing techniques to address the computational challenges of important AI-related problems. The presentation will conclude with a novel deep learning model and quantum computing algorithm for efficient and effective fault diagnosis in manufacturing and electric power systems.