Statistics Graduate Student

Stanford University

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.


  • Data Science
  • Quantitative Finance
  • Machine Learning
  • Monte Carlo methods
  • Debating


  • MS in Statistics, 2023

    Stanford University

  • BSc in Mathematics and Statistics, Minor in Machine Learning, 2021

    Indian Institute of Technology, Kanpur

Recent Publications

Bayesian equation selection on sparse data for discovery of stochastic dynamical systems

We present a Bayesian framework for discovering this system of differential equations under assumptions that align with real-life scenarios, including the availability of relatively sparse data.

Estimating Monte Carlo variance from multiple Markov chains

We propose a multivariate replicated batch means (RBM) estimator that utilizes information across multiple chains in order to estimate the asymptotic covariance matrix.

Recent Talks

Markov Chain Monte Carlo

A talk on MCMC under the aegis of Special Interest Group in Machine Learning (SIGML).