Yinqiu He

Welcome! 

I am an assistant professor in the Department of Statistics at the University of Wisconsin-Madison. 

Before joining the Wisconsin-Madison, I was a Postdoctoral Fellow in the Data Science Institute at the Columbia University during 2021-2022. I obtained by Ph.D. from the Department of Statistics at the University of Michigan-Ann Arbor, where I was advised by Prof. Gongjun Xu and Prof. Xuming He. Prior to coming to Michigan, I received my B.S. in Statistics from the University of Science and Technology of China (USTC) in 2016.  

My first name can be pronounced as "In-cho", and my name in Chinese is 何吟秋. 

Research Interests

I am broadly interested in developing theory and methodology for analyzing large-scale and complex-structured data to address scientific problems arising from interdisciplinary studies. My current research interests include high-dimensional and large-scale statistical inference, mediation pathway analysis, network analysis, statistical machine learning, and also applications in statistical genetics and genomics and metabolomics.  

Selected Awards

• • IMS New Researcher Travel Grant Award, Institute of Mathematical Statistics. (2023) 

  • ProQuest Distinguished Dissertation Awards, Rackham Graduate School, University of Michigan. (2022) 

  • IMS Hannan Graduate Student Travel Award, Institute of Mathematical Statistics. (2020)

  • JSM Travel Award, Biometrics Section, American Statistical Association. (2020)

  • Rackham Predoctoral Fellowship, Rackham Graduate School, University of Michigan. (2020-2021) 

  • Proposal Award of Shark Tank for Research Ideas in Data Science & Statistics (STRIDES), Departments of Biostatistics and Statistics, University of Michigan. (2020)

  • Rackham International Student Fellowship, Rackham Graduate School, University of Michigan. (2017) 

Publications

Preprints: 

• D. Veitch, Y. He, and J. Park. Rank-adaptive covariance changepoint detection for estimating dynamic functional connectivity from fMRI data. [Preprint]
  (This paper received 2024 ENAR Distinguished Student Paper Award.)

• Y. Tian, J. Sun, and Y. He. Efficient Analysis of Latent Spaces in Heterogeneous Networks [Preprint] [Codes]

• C. Chen, Y. He, H. Wang, G. Xu, and P. Song. Quantile Mediation Analytics [Preprint]

Accepted & Published: 

• Y. He*, J.  Sun*, Y. Tian*, Z. Ying, and Y. Feng (2025). Semiparametric Modeling and Analysis for Longitudinal Network Data.
  Accepted at Annals of Statistics. [Preprint] [Codes] 

• Y. He (2024) Extended Asymptotic Identifiability of Nonparametric Item Response Models.
  Psychometrika. [DOI]

• Y. He, P.X.K. Song, and G. Xu (2024).  Adaptive Bootstrap Tests for Composite Null Hypotheses in the Mediation Pathway Analysis.   
  Journal of the Royal Statistical Society Series B.  [DOI] [Codes] [Package]

• Y. He, Y. Gu, and Z. Ying (2023) Discussion of ”Vintage factor analysis with Varimax performs statistical inference” by Karl Rohe and Muzhe Zeng in the The Journal of the Royal Statistical Society Series B. [DOI]

• Y. Deng*, Y. He*, G. Xu, and W. Pan (2022).  Speeding Up Monte Carlo Simulations for the Adaptive Sum of Powered Score Test with Importance Sampling.  (* Co-first author)
  Biometrics, 78(1), 261-273. [DOI]

• Y. He, G. Xu, C. Wu, and W. Pan (2021).  Asymptotically Independent U-Statistics in High-Dimensional Testing.
  Annals of Statistics, 49(1),154-181. [DOI]  [Codes]
  (An earlier version of this paper received JSM Travel Award, Biometrics Section, American Statistical Association.)

• Y. He, B. Meng, Z. Zeng, and G. Xu (2021).  On the Phase Transition of Wilks' Phenomenon. 
  Biometrika, 108(3),741-748.  [DOI]   

• Y. He, T. Jiang, J. Wen, and  G. Xu (2021).  Likelihood Ratio Test in Multivariate Linear Regression: from Low to High Dimension. 
  Statistica Sinica, 31,1215-1238.  [DOI]  

• Y. He, Z. Wang, and G. Xu (2021).  A Note on the Likelihood Ratio Test in High-Dimensional Exploratory Factor Analysis.
  Psychometrika, 86, 442-46. [DOI]

• Y. He and G. Xu (2018).  Estimating Tail Probabilities of the Ratio of the Largest Eigenvalue to the Trace of a Wishart Matrix. 
  Journal of Multivariate Analysis, 166, 320-334.  [DOI]  

* Co-first author

Conference: 

• I. Zhang, R. Gao, and Y. He (2025). Mechanisms of Embodied Learning for Learners with Different Prior Knowledge. CogSci 2025.
  Poster presentation with full paper publication (Acceptance rate: 56.6%).  

Teaching

At the University of Wisconsin-Madison: 

• STAT 456: Applied Multivariate Statistics. Spring 2023-2025. 

             Advanced undergraduate-level statistics course. 

• STAT 849: Theory And Application Of Regression And Analysis Of Variance – I. Fall 2022-2024. 

      One of the core courses of the first-year Ph.D. students. 

Mentoring

At the University of Wisconsin-Madison: 

MS student:

Yuhan Zheng (Initial placement: Ph.D. in Statistics at UW-Madison)

Xiangyi Liao (Ph.D. in Educational Psychology).