Dr. Zhijian He is an associate Professor at School of Mathematics of South China University of Technology (SCUT). Before joining SCUT, he obtained a Ph.D. in Statistics from Department of Mathematical Science of Tsinghua University, advised by Prof. Xiaoqun Wang. His research interests are quasi-Monte Carlo methods and their applications in quantitative finance and statistics. He was a silver prize recipient of the New World Mathematics Awards (NWMA). He has published in top journals in the fields of statistics and computational mathematics, such as Journal of the Royal Statistical Society: Series B, SIAM Journal on Numerical Analysis, SIAM Journal on Scientific Computing, Mathematics of Computation. Part of his research is supported by National Science Foundation of China (NSFC).

According to https://scimeter.org/clouds/, my research interests are somewhere in that cloud:

◆ Ph.D., Statistics, Tsinghua University, 2015

◆ B.S., Mathematics and Applied Mathematics, South China University of Technology, 2010

◆ Associate Professor, School of Mathematics, South China University of Technology, 2018/01 - Present

◆ Research Associate, Lingnan (University) College, Sun Yat-Sen University, 2016/01-2017/11

◆ Lecturer, School of Economics and Commerce, South China University of Technology, 2015/09-12

◆ Visiting Student Researcher, Department of Statistics, Stanford University, 2014/01-07

◆ New World Mathematics Awards (Doctor Thesis), Silver Prize, 2016 [Link]

◆Best Paper Award (with G. Liu and Y. Liu), 14th International Symposium on Financial System Engineering and Risk Management, 2016

◆ Probability and Statistics, Spring 2018

◆ Mathematical Statistics, Fall 2018, Spring 2019

◆ Bayesian Data Analysis, Fall 2018

◆ Quasi-Monte Carlo Simulation in VaR and CVaR Computation, National Science Foundation of China, No. 71601189, 2017 - 2019, PI

(1) Z. He and X. Wang. Good Path Generation Methods in Quasi-Monte Carlo for Pricing Financial Derivatives, *SIAM Journal on Scientific Computing*, 36 (2), B171-B197, 2014. [abstract] [link]

(2) Z. He and X. Wang. On the Convergence Rate of Randomized Quasi-Monte Carlo for Discontinuous Functions, *SIAM Journal on Numerical Analysis*, 53 (5), 2488-2503, 2015. [abstract] [link]

(3) Z. He and A. B. Owen. Extensible Grids: Uniform Sampling on a Space-Filling Curve, *Journal of the Royal Statistical Society: Series B*, 78 (4), 917-931, 2016. [abstract] [link] [C++ Code]

(4) C. Weng, X. Wang, and Z. He. An Auto-Realignment Method in Quasi-Monte Carlo for Pricing Financial Derivatives with Jump Structures, *European Journal of Operational Research*, 254 (1), 304-311, 2016. [abstract] [link]

(5) C. Weng, X. Wang, and Z. He. Efficient Computation of Option Prices and Greeks by Quasi-Monte Carlo Method with Smoothing and Dimension reduction, *SIAM Journal on Scientific Computing*, 39 (2), B298-B322, 2017. [abstract] [link]

(6) Z. He and A. B. Owen. Discussion of: 'Sequential Quasi-Monte Carlo' by M. Gerber and N. Chopin, *Journal of the Royal Statistical Society: Series B*, 77 (3), 563-564, 2015. [note] [link]

(7) C. Schretter, Z. He, M. Gerber, N. Chopin, and H. Niederreiter. Van der Corput and Golden Ratio Sequences Along the Hilbert Space-Filling Curve,*Proceedings of the MCQMC 2014 conference*, R. Cools and D. Nuyens (Eds.), 531-544, 2016. [abstract] [link]

(8) Z. He. Quasi-Monte Carlo for Discontinuous Integrands with Singularities along the Boundary of the Unit Cube. *Mathematics of Computation*, 87 (314), 2857-2870, 2018. [abstract] [link]

(9) Z. He and L. Zhu. Asymptotic Normality of Extensible Grid Sampling. *Statistics and Computing*, 29 (1), 53-65, 2019. [abstract] [link]

(10) F. Xie, Z. He, and X. Wang. An Importance Sampling-Based Smoothing Approach for Quasi-Monte Carlo Simulation of Barrier Options. *European Journal of Operational Research*, 274 (2), 759-772, 2019. [abstract] [link]

(11) Z. He. On the Error Rate of Conditional Quasi-Monte Carlo for Discontinuous Functions. *SIAM Journal on Numerical Analysis*, 57(2), 854-874, 2019. [abstract] [link]

(12) X. Fei, M. Giles, Z. He. QMC Sampling from Empirical Datasets. *Proceedings of the MCQMC 2018 conference*, 2019 (accepted). [abstract] [link]

(1) Z. He and X. Wang. Dimension Reduction and Smoothing in Quasi-Monte Carlo Method for Financial Engineering. *Preprint, arXiv: 1709.02577*, 2017. [abstract] [arXiv]

(2) Z. He and X. Wang. Convergence of Randomized Quasi-Monte Carlo Sampling for Value-at-Risk and Conditional Value-at-Risk. *Preprint, arXiv: 1706.00540*, 2017. [abstract] [arXiv]

(3) Z. He. Sensitivity estimation of conditional value at risk using randomized quasi-Monte Carlo. *Preprint, arXiv:1908.07232*, 2019. [abstract] [arXiv]

School of Mathematics

South China University of Technology

Guangzhou, 510641

P. R. China

Office: 4301, Building #4

Email: hezhijian@scut.edu.cn

何志坚，男，1987年12月生，广东湛江人，华南理工大学数学学院副教授、硕士生导师。清华大学统计学博士，华南理工大学数学与应用数学本科。研究兴趣为统计计算与建模、随机模拟方法及其应用、金融工程。相关研究发表在统计学和计算科学领域顶级期刊，如统计学四大期刊Journal of the Royal Statistical Society: Series B，计算科学顶级期刊SIAM Journal on Numerical Analysis，SIAM Journal on Scientific Computing和Mathematics of Computation，运筹管理权威期刊European Journal of Operational Research等。博士论文获得新世界数学奖银奖。曾获第十四届金融系统工程与工程管理国际年会(FSERM2016)优秀论文奖。国家自然科学基金、广东省自然科学基金通讯评审专家。

根据https://scimeter.org/clouds/提供的词云分析，我的研究关键字如下：

◆ 2010/09-2015/07, 清华大学，统计学，博士

◆ 2006/09-2010/07, 华南理工大学，数学与应用数学，本科

本课题组现有3名硕士研究生，每年计划招生1-3名研究生，含1名推免生。现阶段研究方向有

1. 随机模拟算法研究，包括蒙特卡罗方法、拟蒙特卡罗方法、多层(multil-level)蒙特卡罗方法、MCMC、SMC

2. 贝叶斯计算研究，包括近似贝叶斯计算、变分推断

3. 大规模假设检验

欢迎对上述研究方向感兴趣的同学联系我。优先考虑数理基础扎实，有较强编程能力的同学。

◆ 2018/01至今, 华南理工大学数学学院，副教授

◆ 2016/01-2017/11, 中山大学岭南学院，特聘副研究员

◆ 2015/09-12, 华南理工大学经济与贸易学院，讲师

◆ 2014/01-07, 斯坦福大学统计系，访问学者

◆ 概率论与数理统计（本科）, 2018年春季

◆ 数理统计（本科）, 2018年秋季, 2019年春季, 2019秋季

◆ 高等统计（研究生）, 2019秋季

◆ 贝叶斯统计与知识推理（研究生）, 2018年秋季

◆ 博士论文获2016新世界数学奖银奖

◆ 第十四届金融系统工程与工程管理国际年会(FSERM2016)优秀论文奖

◆ 2018年EJOR期刊优秀审稿人

◆ 国家自然科学基金青年项目：基于拟蒙特卡罗模拟的VaR和CVaR计算问题研究（编号：71601189，执行期限：2017-2019，主持）

◆ 2019中央高校面上项目：条件拟蒙特卡罗模拟研究

(1) Z. He and X. Wang. Good Path Generation Methods in Quasi-Monte Carlo for Pricing Financial Derivatives, *SIAM Journal on Scientific Computing*, 36 (2), B171-B197, 2014. [摘要] [链接]

(2) Z. He and X. Wang. On the Convergence Rate of Randomized Quasi-Monte Carlo for Discontinuous Functions, *SIAM Journal on Numerical Analysis*, 53 (5), 2488-2503, 2015. [摘要] [链接]

(3) Z. He and A. B. Owen. Extensible Grids: Uniform Sampling on a Space-Filling Curve,*Journal of the Royal Statistical Society: Series B*, 78 (4), 917-931, 2016. [摘要] [C++ Code] [链接]

(4) C. Weng, X. Wang, and Z. He. An Auto-Realignment Method in Quasi-Monte Carlo for Pricing Financial Derivatives with Jump Structures,*European Journal of Operational Research*, 254 (1), 304-311, 2016. [摘要] [链接]

(5) C. Weng, X. Wang, and Z. He. Efficient Computation of Option Prices and Greeks by Quasi-Monte Carlo Method with Smoothing and Dimension reduction, *SIAM Journal on Scientific Computing*, 39 (2), B298-B322, 2017. [摘要] [链接]

(6) Z. He and A. B. Owen. Discussion of: 'Sequential Quasi-Monte Carlo' by M. Gerber and N. Chopin, *Journal of the Royal Statistical Society: Series B*, 77 (3), 563-564, 2015. [note] [链接]

(7) C. Schretter, Z. He, M. Gerber, N. Chopin, and H. Niederreiter. Van der Corput and Golden Ratio Sequences Along the Hilbert Space-Filling Curve,*Proceedings of the MCQMC 2014 conference*, R. Cools and D. Nuyens (Eds.), 531-544, 2016. [摘要] [链接]

(8) Z. He. Quasi-Monte Carlo for Discontinuous Integrands with Singularities along the Boundary of the Unit Cube. *Mathematics of Computation*, 87 (314), 2857-2870, 2018. [摘要] [链接]

(9) Z. He and L. Zhu. Asymptotic Normality of Extensible Grid Sampling.*Statistics and Computing*, 29 (1), 53-65, 2019. [摘要] [链接]

(10) F. Xie, Z. He, and X. Wang. An Importance Sampling-Based Smoothing Approach for Quasi-Monte Carlo Simulation of Barrier Options. *European Journal of Operational Research*, 274 (2), 759-772, 2019. [摘要] [链接]

(11) Z. He. On the Error Rate of Conditional Quasi-Monte Carlo for Discontinuous Functions. *SIAM Journal on Numerical Analysis*, 57(2), 854-874, 2019. [摘要] [链接]

(12) X. Fei, M. Giles, Z. He. QMC Sampling from Empirical Datasets. *Proceedings of the MCQMC 2018 conference*, 2019 (accepted). [摘要] [链接]

(1) Z. He and X. Wang. Dimension Reduction and Smoothing in Quasi-Monte Carlo Method for Financial Engineering. *Preprint, arXiv: 1709.02577*, 2017. [摘要] [预印本]

(2) Z. He and X. Wang. Convergence of Randomized Quasi-Monte Carlo Sampling for Value-at-Risk and Conditional Value-at-Risk. *Preprint, arXiv:1706.00540*, 2017. [摘要] [预印本]

(3) Z. He. Sensitivity estimation of conditional value at risk using randomized quasi-Monte Carlo. *Preprint, arXiv:1908.07232*, 2019. [摘要] [预印本]

华南理工大学数学学院

办公室：四号楼4301

邮箱：hezhijian@scut.edu.cn