Guanyi Wang
Assistant Professor at the Department of Industrial Systems Engineering and Management, National University of Singapore.
CV Google Scholar Email: Guanyi.W@nus.edu.sg
Guanyi Wang is actively recruiting self-motivated Ph.D. students with solid mathematical backgrounds and coding skills.
If you are interested in working with us as a Ph.D. student, consider applying here and sending an email with your CV to Guanyi.W@nus.edu.sg. Students with a solid background in Mathematics (especially in Optimization and Statistics) and Coding skills are preferred.
Inquiries about Ph.D. summer research opportunities can be sent directly to Guanyi.W@nus.edu.sg with your CV and a brief description of your research interests. An additional brief recommendation from your advisor will be preferred.
Short Bio
Dr. Guanyi Wang (王冠一) is an assistant professor at the Department of Industrial Systems Engineering and Management, National University of Singapore. Before joining the NUS, he was a postdoctoral researcher at Polytechnique Montréal, under the supervision of Dr. Andrea Lodi from July 2021 to Jun 2022. Guanyi Wang received his Ph.D. degree in Algorithms, Combinatorics and Optimization (ACO) from the Milton Stewart School of Industrial and Systems Engineering (ISyE) at Georgia Institute of Technology, in May 2021, advised by Dr. Santanu S. Dey. Before joining Georgia Tech, Guanyi Wang received a Master's degree in Applied Mathematics and Statistics from the Department of Applied Mathematics and Statistics at Johns Hopkins University, advised by Dr. Amitabh Basu, and a Bachelor's degree in Mathematics from the University of Science and Technology Beijing.
Research Interest
Guanyi Wang's research interest lies broadly in mixed-integer nonlinear programming (MINLP) and statistical learning.
Theory Part. Relaxations and reformulations with theoretical guarantees.
Practical Part. Scalable and tractable algorithms for statistical learning with integer constraints, e.g., large-scale dimensionality reduction, feature selection, traveling salesman problem and its variants, fairness of decision-making problems, model compression in deep learning.