Yikun Zhang (张奕堃)

My current theoretical research interests lie in

• Nonparametric Statistics (Kernel Smoothing),
• Optimization on Nonlinear Manifolds,
• High-Dimensional Inference with Missing Data,
• Causal Inference for Continuous Treatments,
• Transfer Learning and Domain Adaptation.

On the applied side, I am broadly interested in developing statistically principled and AI-driven methods for challenging problems in astronomy and related scientific domains. A central focus of my applied research is to detect, characterize, and extract scientific insights from the large-scale structure of the Universe (i.e., the cosmic web), with the broader goal of turning modern statistical learning tools into reliable instruments for uncovering previously inaccessible physical information.