Yu Yang
Assistant Professor, Department of Industrial and Systems Engineering (ISE), UF
Assistant Director, Center for Applied Optimization (CAO)
Office: 401C Weil Hall
Address: P.O. Box 116595, Gainesville, FL 32611
Email: yu.yang@ise.ufl.edu
Phone: (352) 294-7727
Fax: 352-392-3537
Curriculum Vitae: PDF
Welcome to My Personal Webpage!
I currently serve as an assistant professor in ISE and an assistant director of the CAO at UF. I obtained my Ph.D. in Operations Research, with a minor in Mathematics, from ISyE at Georgia Institute of Technology in 2020. During my stay at Georgia Tech, I had the privilege to work with Prof. Martin Savelsbergh (advisor), Prof. Natasia Boland (co-advisor), Prof. Alan Erera, and Prof. Guanghui (George) Lan. Before that, I earned my B.S. in Applied Mathematics in 2016 from the School of Mathematical Sciences at Peking University and conducted undergraduate research under the guidance of Prof. Zaiwen Wen and Prof. Bin Dong.
My research focuses on advancing Mathematical Optimization to solve critical decision-making challenges in logistics, healthcare, public safety, and sustainable energy. By combining combinatorial optimization, machine learning (ML), and large-scale optimization theory, I develop innovative solutions that provide actionable insights for organizations facing complex, real-world problems, such as drone-aided delivery, radiotherapy in cancer treatment, and large-scale inland wind farm design. My work on novel modeling and ML-enhanced strategies for branching, variable reduction, and decomposition has led to breakthroughs in solving these problems optimally, as exemplified by RouteOpt, our open-source software achieving leading performance for optimizing vehicle routes in first- and last-mile logistics. I am also interested in new zeroth- and first-order algorithms with provable convergence rates tailored to handle large-scale, high-dimensional problems arising in modern AI/ML applications and business data analytics. Collectively, these efforts support data-driven decision-making under uncertainty and promote both strategic planning and operational efficiency across engineering and business domains.