Jun Yan

Welcome!

Currently I am working at Hudson River Trading as a quantitative researcher for systematic trading. I got my Ph.D. degree in Statistics from Stanford University in June 2020, advised by Professor Amir Dembo. Before coming to Stanford, I was major in mathematics and statistics in School of Mathematical Sciences at Peking University. My research interests span high dimensional statistics, probability theory, and machine learning. Contact My LinkedIn profile My Google scholar Email: junyan65@stanford.edu Personal email: yanjun9306@gmail.com

Research Publication

• Nonlinear Large Deviations: Beyond the Hypercube
  Jun Yan. The Annals of Applied Probability, 2020.

• An Algebra Model for the Higher Order Sum Rules
  Jun Yan. Constructive Approximation, 2018.

• Adapting to Unknown Noise Distribution in Matrix Denoising
  Andrea Montanari, Feng Ruan and Jun Yan, 2019. In submission.
  

• Multivariate Distributionally Robust Convex Regression under Absolute Error Loss
  Jose Blanchet, Peter W. Glynn, Jun Yan and Zhengqing Zhou. NeurIPS, 2019.
  

• Persistence versus stability for auto-regressive sequences
  Amir Dembo, Jian Ding and Jun Yan, 2019. Arxiv.
  

• The generalization error of max-margin linear classifiers: High-dimensional asymptotics in the overparametrized regime
  Andrea Montanari, Feng Ruan, Youngtak Sohn and Jun Yan, 2020. In submission.
  

• The Leverage Effect Puzzle under Semi-nonparametric Stochastic Volatility Models
  Chenxu Li, Cheng Yong Tang, Jun Yan, 2019.
  

• On the Minimax Spherical Designs
  Weibo Fu, Guanyang Wang, Jun Yan, 2021. In submission.
  

Teaching Assistantship at Stanford

• Theory of Statistics I (Stats 300A), Fall 2016.

• Introduction to Statistical Methods: Precalculus (Stats 160), Spring 2016.

• Introduction to Stochastic Processes (Stats 217), Summer 2016, Winter 2017, Summer 2017.

• Theory of Probability I (Stats 310A), Fall 2017.

• Theory of Probability II (Stats 310B), Winter 2018.

• Theory of Probability III (Stats 310C), Spring 2017, Spring 2018.

• Data Mining and Analysis (Stats 202), Summer 2018.

• Introduction to Applied Statistics (Stats 191), Winter 2019.

• Introduction to Statistical Learning (Stats 216), Winter 2019.

• Introduction to Time Series Analysis (Stats 207), Spring 2019.