I am a postdoctoral research fellow in the Department of Biostatistics and Department of Computational Medicine and Bioinformatics at the University of Michigan-Ann Arbor. I am also a Precision Health Scholar at the University of Michigan Precision Health. I am jointly mentored by Dr. Veerabhadran Baladandayuthapani and Dr. Arvind Rao. I obtained my PhD in Statistics from University of Connecticut under the supervision of Dr. Dipak Dey and Dr. Yuping Zhang.
My research interests include Bayesian inference, variable selection, geometric functional data analysis and applications in neuro- and cancer-imaging. My current research aims to build statistical methods to address relevant questions in the context of cancer, by integrating data from multiple sources such as radiological-imaging and genomics.
PhD in Statistics, 2018
University of Connecticut
MS in Statistics, 2017
University of Connecticut
MSc in Applications of Mathematics, 2014
Chennai Mathematical Institute
Indian Statistical Institute
Because of the immense technological advances, very often we encounter data in high dimensions. Any set of measurements taken at multiple time points for multiple subjects leads to data of more than two dimensions (a matrix of covariates for each subject). We present a Bayesian variable‐selection method to identify the active regions in the brain as a response to a certain stimulus. We build binary classification models of subject level responses by using binary regression with Gaussian models on the latent variables.
Background and Aim: We aim to build a classifier to distinguish between malaria-infected red blood cells (RBCs) and healthy cells using the two-dimensional (2D) microscopic images of RBCs. We demonstrate the process of cell segmentation and feature extraction from the 2D images. Methods and Materials: We describe an approach to address the problem using mixture discriminant analysis (MDA) on the 2D image profiles of the RBCs. The extracted features are used with Gaussian MDA to distinguish between healthy and malaria infected cells.
The insolvency of a financial entity such as a bank can trigger a sequence of defaults in a network of financial entities interconnected through mutual financial obligations, thus posing a systemic risk to all the financial entities that make up the network. This paper studies the well-known Eisenberg-Noe model for systemic risk from a dynamical systems perspective. In particular, we model the sequence of defaults in the form of a dynamical system, and provide results on its stability and asymptotic behavior.
At Tata Memorial Centre Advanced Centre for Treatment, Research and Education in Cancer, India.
As part of Statistical Consulting Services at University of Connecticut.
During fall session in the Department of Statistics at University of Connecticut.