Wednesday Sep 23, 12pm-1pm, [Meeting Link]
A Parallel Framework for Constraint-Based Bayesian Network Learning
Advisor: Prof. Srinivas Aluru
Bayesian networks (BNs) are a widely used graphical model in machine learning. As learning the structure of BNs is NP-hard, high-performance computing methods are necessary for constructing large-scale BNs. In this talk, I will present a parallel framework that we developed to scale BN structure learning algorithms to tens of thousands of variables. I will demonstrate the utility of our framework for parallelizing three different structure learning algorithms: Grow-Shrink (GS), Incremental Association MB (IAMB), and Interleaved IAMB (Inter-IAMB) and present the scalability results obtained using real and simulated data sets.
Ankit is a Ph.D. student in the School of Computational Science & Engineering, advised by Professor Srinivas Aluru. Before this, he worked as part of the parallel computational fluid dynamics team of ANSYS, Inc. after receiving his Bachelors degree from Indian Institute of Technology, Kanpur. His research interests lie in the fields of high-performance computing, parallel algorithms, and Bayesian networks.