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== Di Wu

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I am a second-year Ph.D. student in the [https://amsc.umd.edu/ Applied Mathematics & Statistics, and Scientific Computation (AMSC)] program at the University of Maryland, College Park, a program whose name is a good summary of my research interests. I am fortunate to be advised by [https://haizhaoyang.github.io Prof. Haizhao Yang]. I also work closely with [https://liangling98.github.io Prof. Ling Liang]. I received my bachelor's and master's degrees in Computational Mathematics from Wuhan University, under the guidance of [https://scholar.google.com/citations?user=yFDDsVgAAAAJ&hl=en Prof. Yuling Jiao] and [https://scholar.google.com/citations?user=SIJCkXcAAAAJ&hl=en&oi=ao Prof. Xiliang Lu]. I also spent time as a research intern at JD Explore Academy, where I was mentored by [https://sites.google.com/site/mathshenli/home Prof. Li Shen]. \n

I am interested in a wide range of topics related to applied mathematics and machine learning. My recent research focuses on *Bayesian statistics*, *optimization*, and *reinforcement learning*.

e-mail: dwuwd 'at' umd 'dot' edu \n
~~~

== Research

- [https://arxiv.org/abs/2604.21849 Beyond Expected Information Gain: Stable Bayesian Optimal Experimental Design with Integral Probability Metrics and Plug-and-Play Extensions] \n
  Di Wu, Ling Liang, Haizhao Yang \n
  arXiv preprint, 2026

- [https://arxiv.org/abs/2604.17630 Randomized Subsystem Descent for Fermion-to-Qubit Mapping] \n
  Gengzhi Yang, Di Wu, Haizhao Yang, Xiaodi Wu, Ji Liu \n
  arXiv preprint, 2026

- [https://arxiv.org/abs/2502.03749 PINS: Proximal Iterations with Sparse Newton and Sinkhorn for Optimal Transport] \n
  Di Wu, Ling Liang, Haizhao Yang \n
  arXiv preprint, 2025

- [https://ojs.aaai.org/index.php/AAAI/article/view/29517 Neural Network Approximation for Pessimistic Offline Reinforcement Learning] \n
  Di Wu, Yuling Jiao, Li Shen, Haizhao Yang, Xiliang Lu \n
  Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2024

== Teaching

=== Teaching Assistant

*University of Maryland, College Park* \n
(leading discussion sections, holding office hours, and grading)

- MATH 240: Introduction to Linear Algebra
- STAT 400: Applied Probability and Statistics
- AMSC 460: Computational Methods

==== Earlier TA Experience

/Wuhan University/: Teaching assistant for graduate courses including Numerical Analysis, Statistical Computing, High Dimensional Data and Machine Learning, Theory and Algorithms of Machine Learning, and Advanced Engineering Mathematics.

/National Tianyuan Mathematics Center/: Teaching assistant for the summer school on ``Mathematical Theory and Applications of Deep Learning''.

== Activities

=== Talks

- *Beyond Expected Information Gain: Stable Bayesian Optimal Experimental Design with Integral Probability Metrics and Plug-and-Play Extensions* \n
  Sayas Numerics Day (formerly DelMar Numerics Day), May 2026

- *PINS: Proximal Iterations with Sparse Newton and Sinkhorn for Optimal Transport* [files/pins_gwu_2026.pdf \[Slide\]] \n
  Department of Mathematics, George Washington University, February 2026

- *Neural Network Approximation for Pessimistic Offline Reinforcement Learning* [files/rl_whu_2024.pdf \[Slide\]] \n
  School of Mathematics and Statistics, Wuhan University, March 2024

=== Professional Services & Activities

- Review Panelist, NASA Research Proposal Review
- Co-organizer, Minisymposium on Optimal Transport, SIAM MDS 2026 ([https://www.siam.org/conferences-events/siam-conferences/mds26/ link])


== Awards & Honors

- Mark E. Lachtman Award, University of Maryland, 2026
- Dean's Fellowship, University of Maryland, 2025, 2026

==== Earlier Awards & Honors

- Lei Jun Educational Fund (around 5,000 USD), Wuhan University
- First-Class Scholarship (Undergraduate & Graduate), Wuhan University
- First Prize, Contemporary Undergraduate Mathematical Contest in Modeling (Team Leader), CSIAM
- Outstanding Graduate, Wuhan University
- Merit Student (3 times), Wuhan University
- First Prize, National High School Mathematics Competition
- First Prize, National High School Physics Competition