Ian Yihang Zhu

Ph.D. Candidate
Department of Mechanical and Industrial Engineering

Contact: i.zhu [at] mail.utoronto.ca
CV: [PDF] (updated Feb. 2022)

headshot
About Me

I am a 4th year PhD candidate at the University of Toronto, supervised by Professors Merve Bodur and Timothy Chan. Previously, I completed my Master of Applied Science at UofT and my Bachelor of Science in Mathematics at UBC. My research interests broadly fall into two categories:

  • Estimation of structural models: I am interested in problems that lie at the intersection of econometrics and optimization. I am particularly interested in inverse optimization, which seeks to infer parameters of an optimization model using observed solutions of the model. Practically, it can be used to learn utility functions and preferences of decision-making agents by observing their decisions. I have also used structural models to uncover hidden market dynamics in commodity markets using price data.
  • Optimization under uncertainty: I am interested in decision-making problems with imperfect information, particularly those that fall at the intersection of predictive and prescriptive analytics. Currently, I am developing new solution methods for robust combinatorial optimization problems. I am also involved in a clinical implementation of a predict-and-optimize paradigm to improve operational decisions at a nonprofit milk bank.

News/Upcoming Talks
  • 04/2022: I was invited to give a talk on Inverse Optimization, which you can find on [Youtube].
  • 02/2022: Our "Spatial Price Integration in Commodity Markets" paper has been accepted at Operations Research.
  • 09/2021: Our "Inverse Mixed Integer Optimization" paper has been accepted at INFORMS Journal on Computing.
  • 09/2021: Our review paper "Inverse Optimization: Theory and Applications" is finally up on ArXiv (see below).
  • 08/2021: I've been awarded the Ontario Graduate Scholarship and the MIE Group Teaching Assistant Award.
Submitted/Published Research
  • Inverse Optimization: Theory and Applications
    with Timothy C.Y. Chan and Rafid Mahmood
    Under review [PDF] [arXiv]
  • Advising Student-Driven Analytics Projects: A Summary of Experiences and Lessons Learned
    with Aaron Babier and Craig Fernandes
    Major revision, INFORMS Transactions on Education, 2022
  • Spatial Price Integration in Commodity Markets with Capacitated Transportation Networks
    with John R. Birge, Timothy C.Y. Chan and J. Michael Pavlin
    Operations Research, 2022 [DOI] [PDF]
  • Inverse Mixed Integer Optimization: Polyhedral Insights and Trust Region Methods
    with Merve Bodur and Timothy C.Y. Chan
    INFORMS Journal on Computing, 2022 [DOI] [PDF] [Poster]
  • Prediction of Protein and Fat Content in Human Donor Milk Using Machine Learning
    with Timothy C.Y. Chan, Rafid Mahmood, Rachel K. Wong et al.
    The Journal of Nutrition, 2021 [DOI]
In Progress
  • Constrained Network Flow Models for Two-Stage Robust Binary Optimization
    with Merve Bodur and Timothy C.Y. Chan
    In preparation
  • Milk Bank Batching Operations: A Data-Driven Optimization Approach
    with Timothy C.Y. Chan, Rafid Mahmood and Rachel K. Wong
    In preparation
Teaching
  • MIE368: Analytics in Action
    University of Toronto, Teaching Assistant, Fall 2020, 2021
  • MIE263: Stochastic Operations Research
    University of Toronto, Teaching Assistant, Winter 2019, 2020
  • MATH110/104/190: Differential Calculus
    UBC, Teaching Assistant, 2014-2015
Past Talks and Conferences
  • INFORMS Annual Meeting 2017, 2018, 2019, 2020, 2021
  • Canadian Operational Research Society 2021
  • MSOM International Meeting 2019
  • Mixed Integer Programming Workshop 2019
Follow me on [Twitter]