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HKU-IDS Scholar

Dr Man Chung YUE
Assistant Professor
HKU Musketeers Foundation Institute of Data Science and
Department of Industrial and Manufacturing Systems Engineering, HKU  (852) 3917 7057
HW-804, Haking Wong Building, HKU
Department of Industrial and Manufacturing Systems Engineering
About Me
Dr. Man-Chung Yue is an Assistant Professor jointly affiliated with the Musketeers Foundation Institute of Data Science and the Department of Industrial and Manufacturing Systems Engineering at The University of Hong Kong. Prior to joining HKU, he was an Assistant Professor in the Department of Applied Mathematics at The Hong Kong Polytechnic University. He worked as a Research Associate at Imperial College London. He received both his Ph.D. in Systems Engineering and Engineering Management and B.Sc. in Mathematics from The Chinese University of Hong Kong.
Research Interests
Continuous Optimization; Algorithmic Design and Analysis; Mathematical Data Science; Decision Making under Uncertainty; Operations Research; Signal Processing
For prospective students, RAs and postdocs
I am always looking for motivated and hardworking students, RAs and postdocs who have a strong interest in solving important questions in optimization, mathematical data science and operations research. Candidates with a strong background in mathematics/statistics and proficient programming skills are especially welcome. If you are interested in working with me, please check out my website and send me an email.
Selected Publications
  • Short-step Methods Are Not Strongly Polynomial-Time. Mathematical Programming. (2023)
  • Approximate Secular Equations for the Cubic Regularization Subproblem. NeurIPS. (2022)
  • On Linear Optimization over Wasserstein Balls. Mathematical Programming. (2021)
  • Universal Barrier is n-Self-Concordant. Mathematics of Operations Research. (2021)
  • Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts. ICML. (2021)
  • Optimistic Distributionally Robust Optimization for Nonparametric Likelihood. NeurIPS. (2019)
  • Calculating Optimistic Likelihoods Using (Geodesically) Convex Optimization. NeurIPS. (2019)
  • On the Quadratic Convergence of the Cubic Regularization Method under a Local Error Bound Condition. SIAM Journal on Optimization. (2019)
  • A Family of Inexact SQA Methods for Non-Smooth Convex Minimization with Provable Convergence Guarantees Based on the Luo-Tseng Error Bound Property. Mathematical Programming. (2019)
  • On the Estimation Performance and Convergence Rate of the Generalized Power Method for Phase Synchronization. SIAM Journal on Optimization. (2017)
  • A Perturbation Inequality for Concave Functions of Singular Values and Its Applications in Low-Rank Matrix Recovery. Applied and Computational Harmonic Analysis. (2016)