About me

I’m a PhD student at the Department of Statistics and Data Science, Wharton School.

I am fascinated by the success of perturbation genomics, with CRISPR technology as the leading example. In modern genomics, statistical methods must address calibration, power, and computation jointly — weaknesses on any one axis undermine the value of the others. My research advances all three fronts:

• Calibration and robustness. Perturbation-based testing — resampling, permutation, and subsampling — for sharp Type-I error control under low signal-to-noise ratio and complex null dependence.
• Power. Compound power analysis along two tracks: practical platforms for experimental design, and theory through the compound decision framework and nonparametric techniques.
• Computation. Mathematical theory drawing on classical asymptotic analysis and random geometric graph models (e.g., nearest-neighbor constructions) to keep methods scalable and provably reliable.

In addition to research, I’m passionate about teaching and mentoring since I am deeply grateful for and benefit from the supervisors I have met, who have taught me far beyond just statistics. I have been a mentor for Undergraduate Research in Probability and Statistics and Directed Reading Program at UPenn.