I recently defended my Ph.D. thesis in the Department of Computer Science at the Machine Learning Research Group of the University of Texas, Austin. I worked with my advisor Prof. Ray Mooney on problems in NLP, Vision and at the intersection of NLP and Vision. My research proposed a new ensembling algorithm called Stacking With Auxiliary Features (SWAF). The idea behind SWAF is that an output is more reliable if not just multiple systems agree on it but they also agree on “why” that output was predicted. SWAF effectively leverages component models by integrating such relevant information from context to improve ensembling. We demonstrated our approach to challenging structured prediction problems in NLP and Vision including Information Extraction, Object Detection, and Visual Question Answering.
Most recently, I have worked on problems in Explainable AI (XAI) wherein I proposed a scalable approach to generate visual explanations for ensemble methods using the localization maps of the component systems. Evaluating explanations is also a challenging problem and I proposed two novel evaluation metrics that does not require human generated GT.
I completed my MS in CS with thesis advised by Jason Baldridge on new topic detection using topical alignment from tweets based on their author and recipient. In the past I have also worked with Maytal Saar-Tsechansky on rumour detection using tweets and with Inderjit Dhillon on analysis of time series tweets as well as fast parallel graph mining.