Anirban Samaddar

Anirban Samaddar is a postdoctoral appointee in the Mathematics and Computer Science (MCS) division. His research interest is in Bayesian inference and machine learning. He is current research focuses on developing probabilistic machine learning methods and uncertainty quantification techniques in solving problems across fields like material science, and high-energy physics. 

Prior to joining Argonne, he obtained his doctoral degree in Statistics from Michigan State University where his research was focused on developing novel methods for analysis of large-scale genetic data sets.

Awards, Honors and Memberships:

• Selected in National Science Foundation (NSF) Mathematical Sciences Graduate Internship (MSGI) Program in Summer 2021.
• Received MSU graduate school travel fellowship for presenting at the Joint Statistical Meeting (JSM) 2022.
• Received Extraordinary Employee award in 2017 from Kantar IMRB.

Publications:

1. Sparsity-Inducing Categorical Prior Improves Robustness of the Information Bottleneck
2. Fine mapping and accurate prediction of complex traits using Bayesian Variable Selection models applied to biobank-size data
3. Machine Learning Methods for Feature Selection and Prediction Applied to Large Scale Genetics Data