About

I am a second-year Statistics Ph.D. student at Stanford and am grateful to be co-advised by Professors Scott W. Linderman and Emmanuel J. Candès. I am supported by an NSF Graduate Research Fellowship.

Broadly speaking, my research interests lie in computational statistics and statistical machine learning. I am particularly interested in developing scalable methods for probabilistic inference and sampling, as well as statistically principled methods for learning and decision-making in low signal-to-noise ratio (SNR) and other data-constrained settings — for example, situations where data may be scarce, low-quality, and/or very expensive to obtain. My current work spans Bayesian computation and MCMC, efficient statistical inference and data acquisition, and applications across science and AI. I am especially excited about the real-world applications of these ideas to disease forecasting and public health, AI evaluation and alignment, and Scientific Machine Learning (SciML), but I am always excited to engage with new application areas.

Big picture, I am very interested in (a) what statistics as a discipline can contribute in the age of AI; and (b) how various tools from adjacent (or not so adjacent) fields can be used to turbocharge statistical methods.

Before starting my Ph.D., I graduated from Harvard University with an A.B. in Statistics & Mathematics, an S.M. in Applied Mathematics, and a Secondary Field in Computer Science, where I was fortunate to be advised by Professor Samuel Kou. At Harvard, I also had the privilege of working under Professors Susan Murphy, Himabindu Lakkaraju, Lucas M. Stolerman, Mauricio Santillana, and Shihao Yang.

Outside of academia, a significant portion of my research training was conducted under the tutelage of Drs. Edward Raff and Fred Lu at Booz Allen Hamilton, to whom I am indebted.

I am passionate about teaching and mentorship, and was fortunate to be mentored on these fronts by Professor Joe Blitzstein and Dr. Weiwei Pan.

You can reach me at skylerw[at]stanford[dot]edu.