Anran Hu 胡安然

About me

Welcome to my homepage! I'm 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. My research topics include applied probability, reinforcement learning, stochastic control and games. Recently, I am also interested in FinTech and applying machine learning and reinforcement learning to finance. Please find my CV here. Email: Anran.Hu [at] maths (dot) ox (dot) ac (dot) uk [ResearchGate] [Google Scholar] [LinkedIn]

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

• Reinforcement learning & machine learning

• Applied probability, stochastic control and games

• FinTech

Publications & Preprints

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

• MF-OMO: An Optimization Formulation of Mean-Field Games.
  Xin Guo, Anran Hu, Junzi Zhang. Submitted. (2022)
  

• 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)
  Accepted, SIAM Journal on Control and Optimization.
  

• 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)
  To appear, Mathematics of Operations Research.
  

• 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.
  

Work Experience

• Amazon.com LLC (Seattle), Applied Scientist Intern

  • Manager: Dr. Xinyang Shen.

  • Project: Data-Driven Large-Scale Inbound Behavior Prediction for Third-Party Sellers.

Invited Talks

• 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.

Teaching

• 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.

Recent Honors & Awards

• Outstanding Graduate Student Instructor, 2021.

• Berkeley Marshall-Oliver-Rosenberger Fellowship, 2020.

• NeurIPS Travel Award, 2019.

• Berkeley IEOR First Year Faculty Fellowship Award, 2017.