About me

I am an assistant professor at the Department of Applied Mathematics and Statistics at Johns Hopkins University, starting from January 2025.

I was a Postdoctoral Research Scientist in the Department of Industrial Engineering and Operations Research at Columbia University from September 2023 to December 2024, supervised by Prof. Xunyu Zhou.

I completed my Ph.D. in the Department of Mathematics at the University of Texas at Austin, advised by Prof. Thaleia Zariphopoulou.

My research lies at the intersection of data-driven decision making under uncertainty, stochastic and robust optimization, and machine learning. Specifically,

• Data-driven Decision Making under Uncertainty with Side Information. I develop robust and computationally efficient methodology with strong performance guarantees for decision-making problems involving contextual data. My works generate real-world impact and have industrial applications in supply chain management, revenue management, and fair machine learning.
• Optimal Information Acquisition. I develop stochastic control models that explore the optimal way to acquire information dynamically, and make a trade-off between the costly information learning and the expected return of the decision, given the limited information. My works draw inspiration from important real-world applications, including mechanism design and robo-advising.

I received my bachelor degree in Mathematics and Applied Mathematics (Honors Program) at Xi’an Jiaotong University in China, where I was also a student of the Speical Class for the Gifted Young before my undergraduate study.

Research

Publications

1. Luhao Zhang, Jincheng Yang, Rui Gao. Optimal Robust Policy for Feature-Based Newsvendor (2023), Management Science.

2. Luhao Zhang, Mohsen Ghassemi, Ivan Brugere, Alan Mishle, Niccolo Dalmasso, Vamsi Potluru, Tucker Balch, Manuela Veloso. Conditional Demographic Parity Through Optimal Transport (2022), NeurIPS Workshop on Algorithmic Fairness through the Lens of Causality and Privacy.

   • A conference version is submitted to 39th AAAI Conference on Artificial Intelligence (AAAI-25).

3. Luhao Zhang, Jincheng Yang, Rui Gao. A Simple and General Duality Proof for Wasserstein Distributionally Robust Optimization (2024), Operations Research, forthcoming.

Preprints

4. Renyuan Xu, Thaleia Zariphopoulou, Luhao Zhang. Decision Making under Costly Sequential Information Acquisition: the Paradigm of Reversible and Irreversible Decisions (2023), Submitted.

5. Jincheng Yang, Luhao Zhang, Ningyuan Chen, Rui Gao, Ming Hu. Decision-making with Side Information: A Causal Transport Robust Approach (2024), Submitted.

6. Rui Gao, Jincheng Yang, Luhao Zhang. A Class of Interpretable and Decomposable Multi-period Convex Risk Measures (2024), preprint.

Patents

1. Method and system for obtaining conditional demographic parity through optimal transport in data-driven model (2024), US 2024/0303532 A1.

Teaching & Services

Professional Services

• Mathematical Programming (2023 Meritorious Service Award)

• Operations Research

• Mathematics of Operations Research

• Management Science

• Production and Operations Management

• ACM International Conference on AI in Finance

• INFORMS 2021, Invited session of Learning and Decision-making with Contextual Information

• INFORMS 2022, Invited session of Data-driven Decision Making

• INFORMS 2023, Invited session of Decision-making under Uncertainty

• ICSP 2023, Mini-symposium of Causal transport and adapted Wasserstein distance

• INFORMS 2024, Invited session of Decision-making under Uncertainty

Directed Reading Program

The Directed Reading Program is an RTG program that pairs undergraduate students with graduate student mentors to undertake independent projects in mathematics.

Mentee

• Sonali Singh, on the topic of Stochastic Calculus for FinanceSpring 2020

• Wenxuan Jiang, on the topic of Stochastic Calculus for FinanceFall 2021

• Haoze Yan, on the topic of Lectures on Stochastic ProgrammingSpring, Fall 2022

• Yuxiang Gao, on the topic of Elements of Statistical LearningSpring 2022

Industry Experience

Industry Experience

• AI Researcher (Intern), JPMorgan Chase, New York, NY Summer 2022

  Developed an efficient algorithm to achieve conditional demographic parity using causal transport distance

  Preliminary result accepted by NeurIPS Workshop on Algorithmic Fairness through the Lens of Causality and Privacy

Miscellaneous

Awards & Fundings

• Meritorious Service Award, Mathematical Programming2023

• Graduate School Summer Fellowship, UT Austin Summer 2021, Summer 2023

• Graduate Continuing Bruton Fellowship, UT Austin Spring 2022

• Professional Development Award, UT Austin Spring, Fall 2022

• Frank Gerth III Teaching Excellence Award, UT Austin 2021