About me

I am a postdoctoral researcher in the Department of Biostatistics at the University of Michigan, working under the mentorship of Professor Peter X.-K. Song. I received my Ph.D. in Statistics in 2024 from the University of California, Irvine, advised by Professor Annie Qu. I previously earned an M.S. in Statistics from the University of Illinois Urbana–Champaign in 2018 and a B.S. in Mathematics and Applied Mathematics from the School of Mathematical Sciences at Zhejiang University in 2016.

I am on the 2026–2027 academic job market. Here is my CV.

Research Interests

My research develops statistical and machine learning methodology for high-dimensional and longitudinal data, motivated by problems in environmental health and mobile and wearable health studies. Current focus: interpretable neural network models and selection-aware inference for longitudinal exposome data and child growth outcomes.

• Neural networks and data-adaptive models with valid post-selection inference
• High-dimensional and longitudinal data; multi-resolutional and functional data
• Exposome modeling, environmental mixtures, and life-course child growth trajectories
• Mobile and wearable health; reinforcement learning for treatment effect evaluation

Recent Updates

• Oct 2026 New preprint on arXiv: Multi-Task Active Learning with Efficient Resource Allocation (with Hanwen Ye and Annie Qu).
• 2026 Discussion paper Comments on "An Overview of Large Language Models for Statisticians" accepted at The American Statistician.
• 2026 Paper on sleep disturbance subtypes among dementia caregivers accepted at Nursing Research.
• Feb 2026 Two new preprints on arXiv: Neural Network Machine Regression (NNMR) and cumulative treatment effect testing under continuous-time reinforcement learning.
• Aug 2025 Presented a scaling neural network approach for variable selection at JSM 2025 in Nashville, and chaired the session "Harnessing the Power of Large-Scale and Heterogeneous Data with Integrative Analysis."

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