I am currently a doctoral student 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,
My Ph.D. advisor is Prof. Thaleia Zariphopoulou. My doctoral committee members include Profs. Rui Gao, Irene M. Gamba, Mihai Sirbu, and Stathis Tompaidis.
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.
Luhao Zhang, Jincheng Yang, Rui Gao. Optimal Robust Policy for Feature-Based Newsvendor (2022), Management Science, forthcoming.
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.
Luhao Zhang, Jincheng Yang, Rui Gao. A Simple and General Duality Proof for Wasserstein Distributionally Robust Optimization (2022), under review for Management Science.
Jincheng Yang*, Luhao Zhang*, Ningyuan Chen, Rui Gao, Ming Hu. Decision-making with Side Information: A Causal Transport Robust Approach (2022), to be submitted to Operations Research.
Renyuan Xu*, Thaleia Zariphopoulou*, Luhao Zhang*. Decision Making with Learning and Opportunity to Reverse (2022), to be submitted to Operations Research.
I am interesting in teaching a wide range of courses, including different topics in probability, statistics, and optimization. I have taught several topics as follows:
Introduction to Mathematical Statistics
Applied Statistics (Python, R)
Numerical Analysis (C++, Matlab, Python)
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
The Directed Reading Program is an RTG program that pairs undergraduate students with graduate student mentors to undertake independent projects in mathematics.
Sonali Singh, on the topic of Stochastic Calculus for Finance2020 Spring
Wenxuan Jiang, on the topic of Stochastic Calculus for Finance2021 Fall
Haoze Yan, on the topic of Lectures on Stochastic Programming2022 Spring, Fall
Yuxiang Gao, on the topic of Elements of Statistical Learning2022 Spring
AI Researcher (Intern), JPMorgan Chase, New York, NY 2022 Summer
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
I have been teaching the following languages and their applications in Numerical Analysis and Applied Statistics:
Python (Pandas, NumPy, TensorFlow, Scikit-Learn, SciPy)
Frank Gerth III Teaching Excellence Award, UT Austin 2021
Graduate School Summer 2021 Fellowship, UT Austin 2021