Y. Samuel WangAssistant Professor
I am currently an assistant professor at Cornell in the Department of Statistics and Data Science. I was previously a post-doc at the University of Chicago’s Booth School of Business, and I completed my PhD in Statistics at the University of Washington. I recieved my undergraduate degree in applied math and economics at Rice University and worked in management consulting for two years prior to graduate school.
Antoine de Saint-Exupéry, the author of The Little Prince, once said, “If you want to build a ship, dont drum up the men to gather wood, divide the work, and give orders. Instead, teach them to yearn for the vast and endless sea.” Having worked across the spectrum of statistical theory, methodology, and applications, I regularly see the “vast and endless sea” that is statistics. I see the structure and creativity of mathematical statistics, I see how statistical methodology provides powerful tools for making better decisions, and I see the ways statistics applied to scientific problems can uncover meaningful insights to support human flourishing. Throughout my various teaching experiences—both formal and informal—I have sought to reveal these “glimpses of the sea” to others so that they might better grasp the beauty of their work and gladly partake in the necessary “gathering of wood” and “dividing of work.” Certainly, the details and more tedious tasks are important and should not be forgotten. However, I have found that clearly giving vision for the ultimate goal—not just the immediate tasks at hand—is key to a successful teaching experience. For each audience, the “appeal of the sea” or end goal will vary, and as a teacher, I seek to first understand those goals and tailor the instruction and material to that end.
I enjoy thinking about problems where the goal is to discover interpretable structure which underlies the data generating process. This includes problems in the areas of causal discovery, graphical models, and mixed membership models. In many cases, the methods are tailored for the high-dimensional setting where the number of variables considered may be large when compared to the number of observed samples. My applied interests vary, but are generally social science related.
For departmental service, in the fall I held an informal seminar series for first year PhD students titled “How to be a grad student.” I hope that the course allowed students to feel more confident in starting grad school. I also serve on the computing committee and review applications for the MPS program.
Externally, for the past couple years, I have given a “Using Data for Equity” talk at the Des Moines Public School Counselors “Interrupting Racism Workshop” In addition, I moderated the online panel discussion “How to be a good mentor.” for the National Institute of Statistical Sciences. This past year, I have also served as a reviewer for 12 journal papers and 2 machine learning conferences.
For the past couple years, I have given the talk “Using Data for Equity” at the Des Moines Public School Counselors “Interrupting Racism” workshop series. In addition, this past year, I moderated the online panel discussion “How to be a good mentor” for the National Institute of Statistical Sciences.