I am a masters student in the Department of Statistics, Stanford University. As far as I can remember, I have always been drawn towards the dense logical framework of mathematics. In particular, I am fascinated by how simple and intuitive concepts in statistics contribute towards developing intricate methods of solving complex problems.

I completed my undergrad at the Indian Institute of Technology Kanpur (IITK) in Mathematics and Scientific Computing with a minor in Machine Learning. The excellent array of opportunities in IITK and my insatiable curiosity have allowed me to explore various areas in statistics. My work with Prof. Dootika Vats on output analysis of parallel Markov chain Monte Carlo has resulted in a manuscript currently under review with JMLR. I have had the privilege of developing a Bayesian framework for identifying dynamic models in climate change and healthcare under real-life assumptions like data sparsity and acute parameter interdependence. My manuscript addressing this problem with Dr. Vats and Prof. Snigdhansu Chatterjee (Dept. of Statistics, University of Minnesota) is currently under review with Bayesian Analysis.

I’ve also worked extensively on deep learning in college from a research as well as industrial perspective. My internship experience in developing online recommendation engines and image segmentation have familiarized me with complex neural network architectures. My recent experiences on developing trading strategies for a quantitative trading firm introduced me to the power and wide applicability of statistics in real-world problems. I am always looking for opportunities to work on interesting and challenging problems to keep my brain churning.