Sara Venkatraman

Email: skv24 [at] cornell.edu
CV  •  Google Scholar  •  GitHub  •  LinkedIn

Welcome! I am a final-year PhD candidate 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.

My research is broadly in the analysis of temporal and spatial phenomena. I am particularly interested in nonlinear dynamical systems, and I enjoy thinking about theory and methods for learning systems of differential equations and their properties from 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 and numerical analysis.

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

Selected papers

Full list in CV. Co-first authorship denoted by *.

• Sparse reconstruction of ordinary differential equations with inference.
  Sara Venkatraman, Sumanta Basu, Martin T. Wells.
  Preprint, 2023.
  2nd place, Upstate NY Statistics Conference student paper competition.
  Paper  •  Slides  •  Poster

• An empirical Bayes approach to estimating dynamic models of co-regulated gene expression.
  Sara Venkatraman, Sumanta Basu, Andrew G. Clark, Sofie Delbare, Myung Hee Lee, Martin T. Wells.
  Data Science in Science, 2023.
  IISA student paper competition winner.
  Paper  •  Code  •  Slides  •  Poster

• Association between city-wide lockdown and COVID-19 hospitalization rates in multigenerational households in New York City.
  Arnab K. Ghosh*, Sara Venkatraman*, Evgeniya Reshetnyak, Mangala Rajan, Anjile An, John K. Chae, Mark A. Unruh, David Abramson, Charles DiMaggio, Nathaniel Hupert.
  PLOS ONE, 2022.
  Paper  •  Slides  •  Cornell Statistics article

More about me

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

I co-organize the Cornell math/statistics Directed Reading Program, 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: August 2023.