Professor Yi He's research spans extreme value theory, high-dimensional statistics, and financial econometrics. In extreme value theory, he focuses on developing novel frameworks for heterogeneous large-scale data, proposing new statistical laws and their theoretical foundations, and advancing superefficient prediction of natural disasters and climate extreme events. In high-dimensional statistics, he extends random matrix theory to study non-sparse models and derives optimal statistical inference and forecasting methods through rigorous theoretical analysis In financial econometrics, he concentrates on building data-driven statistical inference frameworks for financial time series, and providing methodological support and theoretical guarantee for real-time risk management.
BACKGROUND INFORMATION
Professor Yi He is an expert in mathematical statistics. He received his master’s degree from the University of Cambridge and his Ph.D. in Econometrics from Tilburg University in the Netherlands, under the supervision of the renowned mathematical statistician John Einmahl. He previously held a tenured associate professorship at the University of Amsterdam and served as an assistant professor at Monash University in Australia. He has also been a research fellow at the Tinbergen Institute and an external reviewer for major Dutch research funding agencies. His research has been published in leading international journals in mathematical statistics and econometrics, including JASA, AoS, JRSS-B, JBES, and JoE. He has made notable contributions to extreme value statistics and was nominated for the Van Danzig Award in 2025.
RESEARCH FIELD
Professor Yi He's research spans extreme value theory, high-dimensional statistics, and financial econometrics. In extreme value theory, he focuses on developing novel frameworks for heterogeneous large-scale data, proposing new statistical laws and their theoretical foundations, and advancing superefficient prediction of natural disasters and climate extreme events. In high-dimensional statistics, he extends random matrix theory to study non-sparse models and derives optimal statistical inference and forecasting methods through rigorous theoretical analysis In financial econometrics, he concentrates on building data-driven statistical inference frameworks for financial time series, and providing methodological support and theoretical guarantee for real-time risk management.
EDUCATIONAL BACKGROUND
2013–2016: Ph.D., Department of Econometrics and Operations Research, Tilburg University, the Netherlands
2012–2013: M.Phil., Finance (Financial Engineering track), University of Cambridge, United Kingdom
2009–2012: B.Sc., Econometrics and Operations Research, Tilburg University, the Netherlands
WORK EXPERIENCE
2026–present: Professor, School of Mathematical Sciences, Eastern Institute of Technology, Ningbo
2022–2025: Tenured Associate Professor, Quantitative Economics Section, Amsterdam School of Economics, University of Amsterdam, the Netherlands
2019–2022: Assistant Professor, Quantitative Economics Section, Amsterdam School of Economics, University of Amsterdam, the Netherlands
2016–2019: Assistant Professor, Department of Econometrics and Business Statistics, Monash University, Australia
ACADEMIC EXPERIENCE
2024: Visiting Scholar, School of Economics, Singapore Management University
2015: Visiting Scholar, J. Mack Robinson College of Business, Georgia State University, USA
ACADEMIC PART-TIME JOBS (PARTIAL)
2024–present: Council Member, Chinese Australian Society of Econometrics
2025–present: Associate Editor, Journal of Mathematical Study
2021–2025: Research Fellow, Tinbergen Institute, the Netherlands
2024: Reviewer for the Netherlands Organisation for Scientific Research (NWO)
2019–2022: Adjunct Research Fellow, Monash University, Australia
AWARDS AND HONORS
2025: Ranked among the top 10 in the Economists Parade 2025 (Netherlands)
2024: Ranked among the top 20 in the Economists Parade 2024 (Netherlands); additionally, his supervised student won the national Best Master’s Thesis Award (REmagine Award) and was listed among the top five papers of the year
2022: Teacher of the Year, Master’s Program in Econometrics, University of Amsterdam
2013: Thesis Award, Netspar (Network for Studies on Pensions, Aging and Retirement), the Netherlands
2011: Huygens Scholarship, the highest national scholarship awarded by the Dutch Ministry of Education
2006: First Prize, National High School Mathematics Competition
General Information
Dr. Yi He’s research has been widely published in the top international journals in mathematical statistics and econometrics.
Personal website: yi-he.org
Selected Publications:
1. He, Y., Ridge regression under dense factor augmented models. Journal of the American Statistical Association, 119(546), 1566-1578, 2024.
2. Einmahl, J.H.J., and He, Y., Extreme value inference for heterogeneous power law data. The Annals of Statistics, 51(3), 1331-1356, 2023.
3. He, Y., Hou, Y., Peng, L., and Shen, H., Inference for conditional value-at-risk of a predictive regression. The Annals of Statistics, 48(6), 3442-3464, 2020.
4. He, Y., and Einmahl, J.H.J., Estimation of extreme depth-based quantile regions. Journal of the Royal Statistical Society Series B: Statistical Methodology, 79(2), 449-461, 2017.
5. He, Y., Jaidee, S., and Gao, J., Most powerful test against a sequence of high dimensional local alternatives. Journal of Econometrics, 234(1), 151-177, 2023.
6. He, Y., Peng, L., Zhang, D., and Zhao, Z., Risk analysis via generalized Pareto distributions. Journal of Business & Economic Statistics, 40(2), 852-867, 2022.
7. Einmahl, J.H.J., and He, Y., Extreme value estimation for heterogeneous data. Journal of Business & Economic Statistics, 41(1), 255-269, 2023.
8. He, Y., Hou, Y., Peng, L., and Sheng, J., Statistical inference for a relative risk measure. Journal of Business & Economic Statistics, 37(2), 301-311, 2019.
