Sara Venkatraman

Email: skv24 [at] cornell.edu
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Welcome! I am a third-year PhD student in the Department of Statistics and Data Science at Cornell University. My advisors are Professors Martin Wells and Sumanta Basu. I also collaborate with the Division of General Internal Medicine at Weill Cornell Medical College in New York City, which is where I am currently based. For the summer of 2022, I am working on urban planning research with the NYC Department of Design and Construction through a Public Interest Technology fellowship from Cornell Tech.

My research is broadly in the analysis of temporal and spatial phenomena. I enjoy thinking about theory and methods for fitting models often used in applied mathematics, such as ordinary and partial differential equations, to time series data. I also work on statistical methods for identifying spatial patterns of evolution over time in demographic and epidemiological contexts. Other areas of statistics and math that I like studying include network science, numerical analysis, and optimal transport.

Previously, I studied statistics at Yale University as a master’s student and at Cornell as an undergraduate.

Papers

* denotes co-first authorship.

More about me

I am from Los Angeles, California and have also lived in France, where I attended middle and high school, and the United Kingdom. Outside of statistics, I enjoy classical piano, painting, photography, running, and exploring New York City. My favorite dataset is the New York Philharmonic Performance History Database.

I co-organize the math/statistics Directed Reading Program at Cornell, which pairs undergraduates with PhD students to work on semester-long reading projects on topics of mutual interest.

If I were a Springer-Verlag Graduate Text in Mathematics, I would be J.L. Doob's Measure Theory.

I am different from other books on measure theory in that I accept probability theory as an essential part of measure theory. This means that many examples are taken from probability; that probabilistic concepts such as independence, Markov processes, and conditional expectations are integrated into me rather than being relegated to an appendix; that more attention is paid to the role of algebras than is customary; and that the metric defining the distance between sets as the measure of their symmetric difference is exploited more than is customary.

Which Springer GTM would you be? The Springer GTM Test


Last updated: May 2022.