Renyuan Xu

I am currently an assistant professor in the Department of Management Science and Engineering at Stanford University. Prior to joining Stanford, I held positions at New York University and the University of Southern California (2021–2025) and was a Hooke Research Fellow at the Mathematical Institute, University of Oxford (2019–2021). I received my Ph.D. in 2019 at the University of California, Berkeley in the Department of Industrial Engineering and Operations Research.

My research interests include mathematical finance, stochastic analysis, stochastic controls and games, and machine learning theory. I am also interested in interdisciplinary topics that integrate methodologies in multiple fields such as applied probability, statistics, and optimization, along with their applications in addressing high-stake decision-making problems in modern large-scale systems, such as financial and economic systems. Some of the topics that I have been working on recently:

mathematical foundation of Generative AI,
optimal stopping and dynamic information acquisition,
stochastic control, stochastic games, and mean-field games,
reinforcement learning theory,
and their applications in market microstructure and risk management

We are organizing a month-long program on Bridging Stochastic Control And Reinforcement Learning jointly at Isaac Newton Institute and Alan Turing Institute (Nov 3 - Nov 28, 2025). Also excited to announce that we are organizing a workshop on Generative AI in Finance at NeurIPS 2025 in San Diego (Dec 6-7, 2025).

I am co-organizing the World Online Seminar on Machine Learning in Finance (2021-). I co-organized a workshop on Advances in Stochastic Control and Reinforcement Learning at the Banff International Research Station (April 27 - May 2, 2025). I was the local organizing chair of the 5th and a program co-chair of the 3rd ACM International Conference on AI in Finance (ICAIF). I also served as the finance area chair of the Oxford Machine Learning Summer School in 2022 and 2023.

Please find my cv here.
Office: Room 355, Jen-Hsun Huang Engineering Center, Stanford
Email: renyuanxu (at) stanford (dot) edu

Diffusion Factor Models: Generating High-Dimensional Returns with Factor Structure
with Minshuo Chen, Yumin Xu and Ruixun Zhang (2025)
Preprint | SSRN|
Code | Github|

Multi-Task Dynamic Pricing in Credit Market with Contextual Information
with Jingwei Ji and Adel Javanmard (2024)
Preprint | SSRN|

Exploratory Optimal Stopping: A Singular Control Formulation
with Jodi Dianetti and Giorgio Ferrari (2024)
Preprint | arXiv|

Periodic Trading Activities in Financial Markets: Mean-field Liquidation Game with Major-Minor Players
with Yufan Chen, Lan Wu and Ruixun Zhang (2024)
Preprint | SSRN|

Decision Making Under Costly Sequential Information Acquisition: The Paradigm of Reversible and Irreversible Decisions
with Thaleia Zariphopoulou and Luhao Zhang (2023)
Submitted | SSRN|

Implicit Regularization and Convergence of Gradient Descent for Deep Residual Networks
with Rama Cont and Alain Rossier (2022)
Submitted | arXiv|

Asymptotic Analysis of Deep Residual Networks
with Rama Cont and Alain Rossier (2022)
Submitted | arXiv|

Risk-sensitive Markov Decision Process and Learning under General Utilities
with Zhengqi Wu (2023)
Revision, JMLR | SSRN|

Linear-quadratic Gaussian Games with Asymmetric Information: Belief Corrections Using the Opponents Actions
with Huining Yang and Ben Hambly (2023)
Revision, SIAM Journal on Control and Optimization | arXiv|

Risk-Aware Linear Bandits: Theory and Applications in Smart Order Routing
with Jingwei Ji and Ruihao Zhu (2023)
Revision, Operations Research| arXiv|

Fast Policy Learning for Linear Quadratic Control with Entropy Regularization
with Xin Guo and Xinyu Li (2023)
Accepted, SIAM Journal on Control and Optimization (2025) | SSRN|

Inference of Utilities and Time Preference in Sequential Decision-Making
with Haoyang Cao and Zhengqi Wu (2024)
Accepted, Applied Mathematics and Optimization (2025) | SSRN|

Policy Gradient Finds Global Optimum of Nearly Linear-quadratic Control Systems
with Yinbin Han amd Meisam Razaviyayn (2022)
Accepted, SIAM Journal on Control and Optimization (2025) | arXiv|

TailGAN: Nonparametric Scenario Generation for Tail Risk Estimation
with Rama Cont, Mihai Cucuringu and Chao Zhang (2022)
Accepted, Management Science (2025) | arXiv|
Code | Github|

Model-free Analysis of Dynamic Trading Strategies
with Rama Cont and Anna Ananova (2023)
Accepted, SIAM Journal on Financial Mathematics (2025) | arXiv|

Mean-Field Multi-Agent Reinforcement Learning: A Decentralized Network Approach
with Xin Guo, Haotian Gu and Xiaoli Wei (2021)
Mathematics of Operations Research (2024)
arXiv| DOI

Recent Advances in Reinforcement Learning in Finance
with Ben Hambly and Huining Yang (2021)
Mathematical Finance (2023)
arXiv| DOI

Policy Gradient Methods Find the Nash Equilibrium in N-player General-sum Linear-quadratic Games
with Ben Hambly and Huining Yang (2021)
Journal of Machine Learning Research (2022)
arXiv| DOI

Modelling COVID-19 Contagion: Risk Assessment and Targeted Mitigation Policies
with Rama Cont and Artur Kotlicki (2020)
Royal Society Open Science (2021)
medRxiv| DOI

Interbank Lending with Benchmark Rates: Pareto Optima for a Class of Singular Control Games
with Xin Guo and Rama Cont (2020)
Mathematical Finance (2021)
arXiv| DOI

Policy Gradient Methods for the Noisy Linear Quadratic Regulator over a Finite Horizon
with Ben Hambly and Huining Yang (2020)
SIAM Journal on Control and Optimization (2021)
arXiv| DOI

Entropy Regularization for Mean Field Games with Learning
with Xin Guo and Thaleia Zariphopoulou (2020)
Mathematics of Operations Research (2022)
arXiv| DOI

Mean-Field Controls with Q-learning for Cooperative MARL: Convergence and Complexity Analysis
with Xin Guo, Haotian Gu and Xiaoli Wei (2020)
SIAM Journal on Mathematics of Data Science (2021)
arXiv| DOI

A General Framework for Learning Mean-Field Games
with Xin Guo, Anran Hu and Junzi Zhang (2020)
Mathematics of Operations Research (2022)
arXiv| DOI

Delay-Adaptive Learning in Generalized Linear Contextual Bandits
with Jose Blanchet and Zhengyuan Zhou (2020)
Mathematics of Operations Research (2022)
arXiv| DOI

Dynamic Programming Principles for Mean-Field Controls with Learning
with Xin Guo, Haotian Gu and Xiaoli Wei (2019)
Operations Research (2022)
arXiv| DOI

Transaction Cost Data Analytics for Corporate Bonds
with Xin Guo and Charles-Albert Lehalle (2019)
Quantitative Finance (2022)
arXiv| DOI

A Class of Stochastic Games and Moving Free Boundary Problems
with Xin Guo and Wenpin Tang (2018)
SIAM Journal on Control and Optimization (2022)
arXiv| DOI

Stochastic Games for Fuel Followers Problem: N versus MFG
with Xin Guo (2018)
SIAM Journal on Control and Optimization (2019)
arXiv| DOI

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence
with Yinbin Han and Meisam Razaviyayn (2024)
International Conference on Machine Learning (ICML 2025) | arXiv|
Code | Github|

Neural Network-based Score Estimation in Diffusion Models: Optimization and Generalization
with Yinbin Han and Meisam Razaviyayn (2023)
International Conference on Learning Representation (ICLR) '24 | arXiv|

Risk-Aware Linear Bandits with Application in Smart Order Routing
with Jingwei Ji and Ruihao Zhu (2022)
proceeding

Scaling Properties of Deep Residual Networks
with Alain–Sam Cohen, Rama Cont, and Alain Rossier (2021)
ICML'21 | International Conference on Machine Learning
arXiv| proceeding

Learning in Generalized Linear Contextual Bandits with Stochastic Delays
with Jose Blanchet and Zhengyuan Zhou (2019)
NeurIPS'19 (Spotlight) | Conference on Neural Information Processing Systems
proceeding

Learning Mean-Field Games
with Xin Guo, Anran Hu and Junzi Zhang (2019)
NeurIPS'19 | Conference on Neural Information Processing Systems
arXiv| proceeding

I am very fortunate to advise and work with the following Ph.D. students:

Yinbin Han, Stanford (2021 - ), co-advised with Meisam Razaviyayn
Jingwei Ji, Stanford (2021 - ), co-advised with Jong-shi Pang
Zhengqi Wu, USC (2021 - ), co-advised with Karthyek Murthy

NSF CAREER Award, Division Of Mathematical Sciences , 2024
SIAM Activity Group on Financial Mathematics and Engineering Early Career Prize , 2023
JP Morgan AI Faculty Research Award, 2022
WiSE Gabilan Assistant Professorship, 2021 - 2024
Research Recognition Award, University of Oxford, 2020
Finalist, INFORMS Applied Probability Society Best Student Paper Competition, 2018

I am teaching the following courses at Stanford:

MS&E 245B: Advanced Investment Science, Winter 2026
MS&E 242: Machine Learning for Algorithmic Trading, Spring 2026
MS&E 342: Stochastic Systems and Learning Theory with Applications in Finance, Spring 2026

I was teaching the following Ph.D. level course at NYU:

FRE-GY 9073: Stochastic Systems and Modern Machine Learning Theory, Fall 2024

I was teaching the following courses at USC:

ISE537 (Master level): Financial Analytics (Machine Learning in Finance), Fall 2021/2022/2023
ISE599 (Ph.D. level): Special Topics in Control Theory and Reinforcement Learning, Fall 2022

I was the tutor for the following courses at the University of Oxford: