About Me

I am a Teaching Fellow in the Department of Applied Mathematics at The Hong Kong Polytechnic University. I received my Ph.D. in Statistics and Actuarial Science from The University of Hong Kong, following an M.S. in Applied Statistics from the University of Michigan and a B.S. in Applied Mathematics from Wuhan University.

My research lies at the intersection of statistical genetics, high-dimensional statistical inference, and biomedical data science. I develop statistical and machine learning methods for complex genetic, genomic, and clinical data, with particular interests in reliable variable selection, mediation analysis, disease-gene discovery, polygenic risk prediction, and longitudinal electronic health records.

A central theme of my research is to connect statistical signals with biological and clinical mechanisms, combining methodological development with applications to large-scale biomedical datasets.

Research Focus

• Statistical Genetics and Genomics — disease-gene discovery, trans-regulatory mapping, transcriptome-wide association studies, and polygenic risk prediction
• High-dimensional Statistical Inference — variable selection, knockoff inference, false discovery rate control, and structured statistical learning
• Causal Inference and Mediation Analysis — identification of potential causal and mediating mechanisms in high-dimensional biomedical studies
• Biomedical Data Science — statistical and machine learning methods for longitudinal electronic health records and multimodal clinical data

Featured Research

From Genetic Signals to Disease Mechanisms

A major direction of my recent research is developing statistical genetic approaches that move beyond association signals toward biologically interpretable disease mechanisms.

In our recent work published in Cell, we developed a trans-regulatory gene mapping framework that integrates trans-regulatory information, rare-variant evidence, and functional validation to prioritize disease-driving genes in asthma.

Salamone, I. M., Tian, P., Qi, Z., et al.**
*Trans-regulatory gene mapping prioritizes disease drivers in asthma.
**Cell, 2026.

Reliable Inference in High-dimensional Biomedical Data

My methodological research focuses on statistical inference when the number of potential biomarkers or genetic features is large. I have developed methods based on knockoff inference, multiple data splitting, structured information, and transfer learning for high-dimensional mediation analysis, variable selection, transcriptome-wide association studies, and polygenic risk prediction.

The broader goal is to develop methods that achieve both statistical reliability and biomedical interpretability.

Selected Publications

* Indicates equal contribution as first author.

1. Salamone, I. M., Tian, P., Qi, Z., et al.**
   *Trans-regulatory gene mapping prioritizes disease drivers in asthma.
   **Cell, 2026.

2. Yao, M., Tian, P., Li, X., et al.**
   *CoxMDS: multiple data splitting for high-dimensional mediation analysis with survival outcomes in epigenome-wide studies.
   **Briefings in Bioinformatics, 2026.

3. Wang, A., Tian, P., Zhang, Y. D.**
   *TWAS-GKF: a novel method for causal gene identification in transcriptome-wide association studies with knockoff inference.
   **Bioinformatics, 2024.

4. Tian, P., Yao, M., Huang, T., Liu, Z.
   CoxMKF: a knockoff filter for high-dimensional mediation analysis with a survival outcome in epigenetic studies.
   Bioinformatics, 2022.

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