Research & Projects

🔬 Research Projects

Robustness of Multiple Knockoff Aggregation Methods

  • Advisor: Prof. Lijun Wang, School of Mathematical Sciences, Zhejiang University
  • Period: Sept. 2025 – Present
  • Status: First-author manuscript in preparation
  • Keywords: False Discovery Rate (FDR) · Multiple Knockoffs · Heavy-tailed Distribution · Feature Collinearity · Copula

Innovation & Contributions:

  • Systematically evaluated the robustness of state-of-the-art aggregation methods (e-value based, based-voting, Multiple Data Splitting) under extreme heavy-tailed distributions, identifying severe power breakdowns (Power \(\to 0\)) under Cauchy noise.
  • Diagnosed failure mechanisms within the single thresholding step, demonstrating that overly strict thresholds lead to power loss, and empirically investigated strategies (e.g., relaxing FDR control thresholds) to rescue selection power.
  • Analyzed the impact of feature collinearity and different feature importance statistics (LASSO path difference, Random Forest), revealing that derandomized knockoffs recover their superior FDR-control performance at lower correlations (\(\rho = 0.1\)).
  • Investigated the parameter sensitivity of the \(\alpha_{kn}/\alpha_{ebh}\) ratio, exploring empirical CDF approximation frameworks to dynamically calibrate selection thresholds.
  • Researched Copula-based extensions (e.g., Latent Gaussian and Vine Copula Knockoffs) to address and relax the strict distributional assumptions of standard Model-X procedures.

Uncertainty Quantification via Conformal Prediction

  • Advisor: Prof. Guanhua Chen, School of Medicine and Public Health, UW-Madison
  • Period: Mar. 2026 – Present
  • Status: In progress
  • Keywords: Conformal Prediction · Distribution-Free Inference · Uncertainty Quantification

Innovation & Contributions:

  • Investigating conformal inference frameworks to construct distribution-free predictive intervals for complex statistical models.

💻 Selected Computational Projects

C-Based CNN Forward Inference Engine

  • Period: Spring 2025
  • Tech Stack: C, Model Optimization, Binary I/O

Key Implementations:

  • Implemented a convolutional neural network (CNN) forward inference engine completely from scratch in C.
  • Achieved a 75% latency reduction via rigorous channel pruning while maintaining \(\sim 99\%\) classification accuracy.

Uncertainty Quantification for PDEs in Random Domains

  • Period: Fall 2025
  • Tech Stack: Python, Stochastic Galerkin, Karhunen-Loève Expansions

Key Implementations:

  • Reproduced numerical solutions for elliptic Stochastic Partial Differential Equations (SPDEs) utilizing stochastic mappings.
  • Applied Legendre polynomial chaos and Karhunen-Loève expansions for robust uncertainty quantification in complex domains.