معرفی
Jon Lee is the G. Lawton and Louise G. Johnson Professor of Engineering at the University of Michigan's College of Engineering. He previously held faculty positions at Yale University and the University of Kentucky, and served as an adjunct professor at New York University. Before his academic career, he was a Research Staff member at IBM T.J. Watson Research Center where he managed the mathematical programming group.
Lee's research focuses on mathematical optimization, particularly combinatorial optimization, integer programming, and maximum-entropy sampling. His work bridges theoretical foundations with practical applications in experimental design, statistical modeling, and computational mathematics. He has made significant contributions to D-optimal design theory, perspective relaxations for nonconvex optimization, and generalized inverse computations.
His recent publication trends (2022-2025) show consistent work in maximum-entropy sampling problems, D-optimal design algorithms, convex relaxations for nonconvex optimization, and generalized inverse computations. The articles demonstrate increasing sophistication in handling large-scale optimization problems while maintaining theoretical rigor, with particular emphasis on algorithmic efficiency for real-world applications.
Lee has received notable recognition including:
- INFORMS Computing Society Prize (2010)
- Fellow of INFORMS (since 2013)
As an academic leader, Lee has served as founding Managing Editor of Discrete Optimization (2004-06), currently serves as Co-Editor of Mathematical Programming, and is on editorial boards for Optimization and Engineering and Discrete Applied Mathematics. He chaired the Mathematical Optimization Society (2008-10) and the INFORMS Optimization Society (2010-12). His textbook A First Course in Combinatorial Optimization (Cambridge University Press) and open-source book A First Course in Linear Optimization have become standard references in the field.
Lee maintains active research collaborations through his work with the Mathematical Optimization Society and INFORMS, and has participated in significant research programs including the Fall 2017 program on Bridging Continuous and Discrete Optimization at the Simons Institute.
