I recently started as an Applied Scientist at AWS AI.
I finished my MS/PhD at the UMass Computer Vision Lab, advised by Prof. Erik Learned-Miller, and co-advised by Profs. Subhransu Maji and Liangliang Cao. My broad interests are in label-efficient approaches to learning, focusing on leveraging contextual cues such as temporal continuity in videos. I have worked on projects spanning unsupervised domain adaptation, object detection, fine-grained recognition and face recognition. Please check my Publications page for more details on my research.
Here is a link to my [CV].
I maintain the website for FDDB - a benchmark for detecting faces. I have been a reviewer for CVPR, ICCV, ECCV, NeurIPS and the journals TPAMI, TIP and CVIU. At UMass I helped organize the Machine Learning and Friends Lunch (MLFL) seminar series. Earlier, I was involved in GRiD, a multidisciplinary graduate student organisation for data science.
I interned with NEC Media Analytics Lab in San Jose over the summer of 2019, working with Xiang Yu, Kihyuk Sohn and Manmohan Chandraker. Earlier, I spent two happy summers in 2017 and 2014 interning at The Mathworks Inc. with the Computer Vision Toolbox team.