One of the fundamental goals of computer vision is to understand a scene. Towards this goal, we want the system to answer several questions - who, what, when, why, how much, etc. pertaining to the visual scene. As a graduate student my research focused on problems trying to answer simple questions like ?who? and ?how much?. This involved the problems of re-identifying persons over a network of cameras and automatically summarizing large videos. As a postdoctoral researcher, I am working on complex questions like ?what? and ?when? for a visual scene. A challenging application scenario related to this is detecting activities in untrimmed videos. A step further into this problem is to describe what is going on in a video scene in natural language. Conducting research in these areas allows me to build on my previous experience on image/video analysis and provide opportunities to broaden my exposure to deep, end-to-end systems. Despite their enormous success, current deep neural networks (DNNs) are black boxes that do not expose their decision making process or whether they can be trusted and/or corrected. My future research will focus on the ?why? aspect of the DNNs to make them explainable and thus, more compatible with human reasoning.
Fair and Explainable Artificial Intelligence Science and Engineering Research Board (SERB)
Exploration and Evaluation of Explainability and Fairness in Artificial Intelligence ISIRD, SRIC
AWS:NAIRP:Creating a Collateral for Supporting Popular ML Applications on AWS Infrastructure through National AI Resource Platform Amazon Web Services, Inc.
Area of Research: Explainable AI