About

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

My research interests include statistical machine learning, Bayesian computational methods, online learning, reinforcement learning, and deep learning and LLMs. I am particularly excited about the real-world applications of the above to disease forecasting and public health, AI alignment, and Scientific Machine Learning (SciML), but am always excited to engage with new application areas. Practically, I enjoy working on methods in low signal-to-noise ratio (SNR) settings — for example, situations where data may be scarce, low quality, and/or very expensive to obtain. More broadly, 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.