Dr. Ramchandra Rimal, Department of Mathematics, will briefly discuss research that synthesizes advanced statistical network modeling and deep learning (DL) to analyze complex datasets in financial and medical domains. We’ll begin by exploring the theoretical foundations of statistical network analysis, including the Popularity Adjusted Block Model (PABM) and Sparse Subspace Clustering (SSC), for enhanced community detection in heterogeneous networks. We will then transition to the application of various DL architectures for sequential data modeling. The talk will conclude by highlighting the fundamental trade-off between maximizing model precision and maintaining interpretability, which is a common challenge across the fields.
Click here for the discussion.
