👋 Welcome!
My name is Jiahao Tian. I am an undergraduate student majoring in Statistics at the School of Mathematical Sciences, Zhejiang University (ZJU).
Prior to this, I was selected for the Chu Kochen Honors College (Physics). Driven by a profound passion for rigorous theoretical foundations, I transferred to Statistics to pursue strict mathematical training. My coursework and self-study heavily concentrate on advanced pure mathematics and measure-theoretic probability. Currently, my research focuses on high-dimensional statistical inference, distribution-free prediction, and their theoretical robustness.
🔍 Research Interests
- Core: High-Dimensional Statistical Inference, Conformal Prediction, Statistical Learning.
- Extended: Stochastic Analysis, Bayesian Inference, Quantitative Finance.
🛰️ Research Experience
Robustness of Multiple Knockoff Aggregation Methods
- Advisor: Prof. Lijun Wang, Zhejiang University. (Sept. 2025 – Present)
- Systematically evaluated the robustness of state-of-the-art aggregation methods (e.g., e-value, based-voting, MDS) under extreme heavy-tailed noise and high collinearity. Diagnosed failure mechanisms within thresholding steps and explored empirical CDF approximation frameworks to dynamically calibrate selection power.
- Status: First-author manuscript in preparation.
Uncertainty Quantification via Conformal Prediction
- Advisor: Prof. Guanhua Chen, UW–Madison. (Mar. 2026 – Present)
- Investigating conformal inference frameworks to construct distribution-free and covariates shift predictive intervals for complex statistical models.
🎤 Academic Talks
- Convergence of Random Series and Large Deviations (Fall 2025) Delivered a 120-minute lecture in the Advanced Probability Theory Seminar, focusing on rigorous proofs of limit theorems and large deviation principles.
- Robustness of Multiple Knockoff Methods (Fall 2025) Presented a comprehensive review of Model-X and derandomized knockoff procedures at the Weekly Statistical Seminar.
📚 Academic Garden
Beyond my research, I maintain a detailed, open-source collection of course notes and rigorous mathematical derivations (e.g., Stochastic Differential Equations, Asymptotic Statistics).
📮 Connection
I am always open to academic discussions and collaborations.