Aditya Makkar

Aditya Makkar

Graduate student

Columbia University

I am a graduate student at Columbia University interested in probabilistic machine learning. I am affiliated with the Electrical Engineering Department and the Data Science Institute and am part of a thriving machine learning community here at Columbia. I am advised by Prof. John Paisley.

Previously, I was at Goldman Sachs working with Dr. Howard Karloff on machine learning applications for surveillance models. I received my Bachelors degree from Indian Institute of Technology (IIT) Delhi.

If you’d like to chat, feel free to write me at a ‘dot’ makkar ‘at’ columbia ‘dot’ edu.


I view the theory and practice of building systems that learn to make decisions under uncertainty mainly from a Bayesian perspective. My interests include probabilistic modeling and approximate Bayesian inference.

Current Projects

  1. Bayesian nonparametric ensemble: This is a follow-up on the work of Liu, Paisley, Kioumourtzoglou and Coull - Accurate uncertainty estimation and decomposition in ensemble learning (2019).
  2. Deep correlated topic models: This is an application of the work of Zhang and Paisley - Random Function Priors for Correlation Modeling (2019).