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.

I completed my Ph.D. in the Department of Mathematics at the University of Texas at Austin.

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. 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.

News

• I am co-organizing an online reading group on Stochastic Control.

Research

Journal Publications

1. Decision-making with Side Information: A Causal Transport Robust Approach
   Jincheng Yang*, Luhao Zhang*, Ningyuan Chen, Rui Gao, Ming Hu (2026)
   Operations Research Minor Revision | OptOnline

2. A Short and General Duality Proof for Wasserstein Distributionally Robust Optimization
   Luhao Zhang, Jincheng Yang, Rui Gao (2024)
   Operations Research 73(4), 2146-2155 | arXiv

3. Optimal Robust Policy for Feature-Based Newsvendor
   Luhao Zhang, Jincheng Yang, Rui Gao (2023)
   Management Science 70(4), 2315-2329 | OptOnline

Conference Proceedings

1. Integrating Feature Correlation in Differential Privacy with Applications in DP-ERM
   Tianyu Wang†, Luhao Zhang, Rachel Cummings
   AISTATS (2026)

2. Conditional Demographic Parity Through Optimal Transport
   Luhao Zhang, Mohsen Ghassemi, Ivan Brugere, Alan Mishler, Niccolo Dalmasso, Vamsi Potluru, Tucker Balch, Manuela Veloso
   NeurIPS Workshop on Algorithmic Fairness through the Lens of Causality and Privacy (2022) (original version) | AAAI (2025) (later accepted) | arXiv

Preprints

1. Decision Making under Costly Sequential Information Acquisition: the Paradigm of Reversible and Irreversible Decisions
   Renyuan Xu*, Thaleia Zariphopoulou*, Luhao Zhang* (2026)
   Submitted | arXiv | SSRN

2. Dynamic Portfolio Control with Provable Learning Guarantees
   Ian McPherson†, Yizhe Huang†, Rui Gao*, Shuang Li*, Luhao Zhang* (2026)
   Submitted | SSRN

3. Dynamic Risk Measure under Filtration Mismatch
   Rui Gao*, Jincheng Yang*, Luhao Zhang* (2025)
   Preprint

Patents

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

Teaching & Mentoring

Ph.D. Students

• Haotian Zong, JHU AMS (2024 - )

Courses Taught

Johns Hopkins University

• Bayesian Statistics Spring 2025, Fall 2025

• EN.553.746 Stochastic Controls, Games, and Learning, Part I & IIFall 2025, Spring 2026

Doctoral Students Committees

GBO Committees

• Lang Lang, JHU AMS

• Hongyu Cheng, JHU AMS

• Himanshu Sharma, JHU CSE

• Aayush Mishra, JHU CS

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

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

• Yuxiang Gao, on the topic of Elements of Statistical Learning Spring 2022

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

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

Services

Professional Services

• Mathematical Programming (2023 Meritorious Service Award)

• Mathematics of Operations Research

• Management Science

• Operations Research

• Production and Operations Management

• AISTATS

• 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

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