Anran Hu 胡安然

About me

Welcome to my homepage! I am an assistant professor in the Department of Industrial Engineering and Operations Research. I work at the intersection of stochastic control, game theory, optimization and machine learning. My current primary research areas are mean-field games, continuous-time stochastic control and reinforcement learning, and more recently FinTech and applying machine learning and reinforcement learning to finance. Before coming to Columbia University, I was a Hooke Research Fellow in the Mathematical Institute at the University of Oxford. I completed my Ph.D. in Berkeley IEOR, advised by Prof. Xin Guo. Before coming to Berkeley, I obtained my B.S. degree from School of Mathematical Sciences, Peking University. Please find my CV here. Email: ah4277 [at] columbia (dot) edu

Education

• Ph.D., IEOR, UC Berkeley, August 2016 – August 2022.

• M.S., IEOR, UC Berkeley, August 2016 – May 2017.

• B.S., School of Mathematical Sciences, Peking University, September 2012 – July 2016.

Research Interests

• Mathematical finance, financial engineering and FinTech

• Applied probability, stochastic control and games

• Reinforcement learning and machine learning

Publications & Preprints

• Policy Gradient Algorithms in Non-stationary Finite Horizon Environments.
  Anran Hu. Working paper. (2024)

• Homogenization and Mean-Field Approximation for Multi-Player Games.
  Rama Cont and Anran Hu. Working paper. (2024)

• MF-OML: Online Mean-Field Reinforcement Learning with Occupation Measures for Large Population Games.
  Anran Hu and Junzi Zhang. Submitted. (2024)

• MFGLib: A Library for Mean-Field Games.
  Xin Guo, Anran Hu, Matteo Santamaria, Mahan Tajrobehkar and Junzi Zhang.

  • MFGLib: open-source Python library for computing Nash equilibria of mean-field games.

• Optimization Frameworks and Sensitivity Analysis of Stackelberg Mean-Field Games.
  Xin Guo, Anran Hu, Jiacheng Zhang. Submitted. (2022)

• MF-OMO: An Optimization Formulation of Mean-Field Games.
  Xin Guo, Anran Hu, Junzi Zhang. SIAM Journal on Control and Optimization, 62(1), 243-270. (2024)
  

• Theoretical Guarantees of Fictitious Discount Algorithms for Episodic Reinforcement Learning and Global Convergence of Policy Gradient Methods.
  Xin Guo, Anran Hu, Junzi Zhang. (2021)
  Proceedings of the AAAI Conference on Artificial Intelligence 36 (6), 6774-6782.

• Reinforcement Learning for Linear-Convex Models with Jumps via Stability Analysis of Feedback Controls.
  Xin Guo, Anran Hu, Yufei Zhang. (2021)
  SIAM Journal on Control and Optimization, 61(2), 755-787.
  

• Logarithmic Regret for Episodic Continuous-Time Linear-Quadratic Reinforcement Learning over a Finite-Time Horizon.
  Matteo Basei, Xin Guo, Anran Hu, Yufei Zhang. (2020)
  Journal of Machine Learning Research, 23 (178), 1-34.

• A General Framework for Learning Mean-Field Games.
  Xin Guo, Anran Hu, Renyuan Xu, and Junzi Zhang. (2020)
  Mathematics of Operations Research, 48(2), 656-686.
  

• Learning Mean-Field Games. [Slides]
  Xin Guo, Anran Hu, Renyuan Xu, and Junzi Zhang. (2019)
  Advances in Neural Information Processing Systems, 32.

• Consistency and Computation of Regularized MLEs for Multivariate Hawkes Processes.
  Xin Guo, Anran Hu, Renyuan Xu, and Junzi Zhang. (2018)
  NeurIPS 2018 Workshop on Causal Learning.
  

Invited Talks

• IMSI Workshop on Decision Making and Uncertainty, Chicago, IL, 2024

• Finance and Stochastic Seminar, Imperial College London, London, UK, 2023.

• Stochastic Finance Seminar, University of Warwick, Coventry, UK, 2023

• INFORMS Annual Meeting, Phoenix, AZ, 2023.

• Advances in Stochastic Analysis for Handling Risks in Finance and Insurance, CIRM, Marseille, France, 2023.

• Recent Advances on Quantitative Finance, Hong Kong, 2023

• 10th International Congress on Industrial and Applied Mathematics, Tokyo, Japan, 2023.

• 11th General AMaMeF Conference, Bielefeld, Germany, 2023.

• Women in Mathematical Finance, New Brunswick, NJ, 2023.

• SIAM Conference on Financial Mathematics and Engineering, Philadelphia, 2023

• Oxford-Princeton Workshop on Stochastic Analysis and Mathematical Finance, Oxford, 2022.

• Mathematical and Computational Finance Seminar, University of Oxford, Oxford, 2022.

• North-East and Midlands Stochastic Analysis Seminar, Oxford, 2022.

• SIAM Annual Meeting, Pittsburgh, PA, 2022.

• IMSI Workshop on Machine Learning and Mean-Field Games, Chicago, IL, 2022.

• INFORMS Annual Meeting, Virtual, 2020.

• Neural Information Processing Systems, Poster, Vancouver, 2019.

• INFORMS Annual Meeting, Seattle, WA, 2019.

• INFORMS Annual Meeting, Phoenix, AZ, 2018.

• Berkeley-Stanford Workshop on Mathematical and Computational Finance, Stanford University, CA, 2018.

Academic Services

• Co-organizer of Mathematical and Computational Finance Seminar, University of Oxford.

• Reviewer of Mathematics of Operations Research, Quantitative Finance, Mathematical Finance, SIAM Journal on Control and Optimization, Applied Probability, Journal of Economic Dynamics and Control, Journal of Optimization Theory and Applications, Transactions on Machine Learning Research, Journal of Machine Learning, ICML, NeurIPS, AAAI, ICLR, ICAIF.

Teaching

• Instructor, Columbia University

  • IEOR 4500: Applications Programming for Financial Engineering, Fall 2024.

• Instructor, University of Oxford

  • MCF Statistics and Financial Data Analysis, Michaelmas Term 2023.

  • MCF Introduction to Statistics, Michaelmas Term 2023.

• Tutor, University of Oxford

  • MCF Market Microstructure and Algorithmic Trading, Hilary Term 2023.

  • MCF Asset Pricing, Hilary Term 2023.

  • MCF Quantitative Risk Management, Hilary Term 2023.

  • MCF Optimization, Hilary Term 2023.

  • B8.1 Probability, Measure and Martingales, Michaelmas Term 2022.

• Instructor, UC Berkeley

  • IEOR 242: Applications in Data Analysis, Spring 2022.

• Graduate Student Instructor, UC Berkeley

  • IEOR 263B: Applied Stochastic Process II, Spring 2020.

  • IEOR 241: Risk Modeling, Simulation, and Data Analysis, Fall 2019, Fall 2021.

  • IEOR 221: Introduction to Financial Engineering, Fall 2020.

  • IEOR 172: Probability and Risk Analysis for Engineers, Fall 2017.

  • IEOR 120: Principles of Engineering Economics, Fall 2018, Spring 2019.

  • IEOR 170: Industrial Design and Human Factors, Spring 2018.