I am a fifth year doctoral student at Pennsylvania State University in Statistics. I am advised by Dr. Lingzhou Xue. I am a member of both the Statistical Learning and Data Mining (SLDM) Lab and Microbiome Data Science (MDS) Lab.
Previously, I attended The University of Chicago for a Master’s in Statistics and The University of Wisconsin at Madison for undergraduate degrees in Statistics, Computer Sciences, and Mathematics.
Modern computing has greatly accelerated the collection of large amounts of data. Searching for important statistical patterns within this data is harder than simply finding a needle in a haystack. It is more akin to looking for a specific needle in a box of needles in a needle box factory.
My work focuses on developing statistical methodology to analyze data from the human microbiome and determine which microbial taxa are linked to common human health issues such as inflammatory bowel disease and obesity. I am interested in establishing methods that guard against common threats to non-reproducibility. Specifically, my work broadly focuses on methods that control the number of false positive taxa and methods that are explicitly robust to outliers within the data.
False Discovery Rate Control:
- Srinivasan, A. (2017). Master’s Thesis: Calibrating Black Box Classification Models through the Thresholding Method.
Arun Srinivasan, Danning Li, Lingzhou Xue, and Xiang Zhan (Under Review 2020+) Robust Shape Matrix Estimation for High-Dimensional Compositional Data with Application to Microbial Inter-Taxa Analysis.
- Kalins Banerjee, Ni Zhao, Arun Srinivasan, Lingzhou Xue, and others (2019). An Adaptive Multivariate Two-Sample Test With Application to Microbiome Differential Abundance Analysis. frontiers in Genetics.
Additional Interdisciplinary Work: