My research has centered on statistical learning methods/algorithms and application to very-large-scale text categorization, web-mining for concept graph discovery, semi-supervised clustering, multitask learning, novelty-based information retrieval, large-scale optimization for online advertising, social network analysis for personalized email prioritization, etc. My recent research focuses on the following topics:
Large-Scale Structured Learning for Hierarchical Classification (Gopal & Yang, KDD 2013; Gopal & Yang, ICML 2013 & Supplementary ; Gopal et al., NIPS 2012)
Providing organizational views of multi-source Big Data (e.g., Wikipedia, online shops, Coursera)
State-of-the-art classifiers for large-scale classification over hundreds of thousands of categories
Scalable variational inference for joint optimization of one trillion (4 TB) model parameters
Scalable Machine Learning for Time Series Analysis (Topic Detection and Tracking)
From scientific literature, news stories, sensor signals, maintenance reports, etc.
Modeling multi-source and multi-scale evidence of dynamic chances in temporal sequences. (On-going NSF project; Gopal, PhD Thesis)
A new family of Bayesian von Mices Fischer (vMF) clustering techniques (Gopal & Yang, ICML 2014 & Supplementary)
Unsupervised clustering and semi-supervised metric learning and supervised classification (Gopal & Yang, UAI 2014 & Supplimentary).