I am passionate about building systems that can perceive the world and behave in the wild, more like a human does, with explanations for the actions. Towards this goal, my research focuses on bridging the gap of human and machine intelligence through bringing inspiration from neuroscience to understand learning machines and facilitate improving the current state of interpretability and address the lack of robustness outside the training distribution.
Explainable, Trustworthy and Robust Artificial Intelligence and Causal Inference
Get StartedI am currently affiliated with Koo Lab under Simons Center for Quantitative Biology at Cold Spring Harbor Laboratory. Here I am a computational postdoctoral fellow and work closely with Dr. Peter Koo on explaining different biological data modalities i.e. interpreting model predictions with genomic sequences or indentifying important body parts for pancreatic cancer from CT scans.
Previously, I was a postdoctoral associate with Sinha Lab for Developmental Research under Brain and Cognitive Sciences department at Massachusetts Institute of Technology, working with Dr. Xavier Boix on explainability under distribution shift.
I completed my Ph.D. from Computer Science & Engineering department, Indian Institute of Technology Hyderabad, under the guidance of Dr. Vineeth N Balasubramanian. As part of my Ph.D., I assisted the usability of DNNs through enhanced explanation to seamlessly operate in any diverse situation, that was reflected in exploring problems such as enhancing model robustness against unseen noise, adversarial as well as attributional attacks, and investigating more on self-explaining neural nets.