Research

My research interests span computer vision and medical image analysis, focusing on interpretability and explainable AI (X-AI). My work involves investigating different architectures of supervised, unsupervised and self-supervised deep neural networks to improve their generalizability, robustness, and trustworthiness. Specifically, I aim to answer the following research questions:
1. Can we decipher the failure modes of deep neural networks through multimodal vision-language representations and large language models (LLMs) for improved reliability and debugging? (Ongoing)
2. Can we develop new vision-language foundation models for previously unexplored imaging modalities, e.g, breast mammograms in biomedical AI? (In submission)
3. Can we extract interpretable models out of a pre-trained deep neural network, often treated as a black box, with the help of high-level human interpretable concepts? [MoIE (ICML 2023 + SCIS@ICML 2023)]
4. Can we use the concept-based interpretable models for better data and computational efficiency? [MoIE-CXR (MICCAI 2023 + IMLH@ICML 2023)]
5. Can we leverage weak labels from the radiology reports to localize a disease and its progression without relying on ground-truth bounding box annotation? [AGXNet (MICCAI 2022)]

During my time at UF, I was interested broadly in biomedical informatics with a focus on causal inference. I developed deep learning models, namely DPN-SA (JAMIA 2021), PSSAM-GAN (CMPB-U 2021) and DR-VIDAL (AMIA 2022), to compute propensity scores for the efficient estimation of individual treatment effects (ITE). For a detailed overview of my Master's research, refer to the slides available at this link.

News

[Oct 2023] I'm serving as a reviewer for ICLR 2024, Medical Image Analysis (MedIA) and CVPR 2024. [Aug 2023] I'm invited to serve as a Program Committee (PC) member for AAAI 2024. [Jul 2023] I'm serving as a reviewer for NeurIPS 2023 and the journal Computer Methods and Programs in Biomedicine. [Jun 2023] I'm now a PhD candidate. Also, two papers are accepted at SCIS and IMLH workshops at ICML 2023. [May 2023] Our work Distilling BlackBox to Interpretable models for Efficient Transfer Learning is accepted (Early accept, top ~ 14%) at MICCAI 2023. [Apr 2023] Our work Dividing and Conquering a BlackBox to a Mixture of Interpretable Models: Route, Interpret, Repeat is accepted at ICML 2023. [Dec 2022] I'm joining Boston University in Spring 2023 in the Department of Electrical and Computer Engineering following my advisor's move. My research will be supported by Doctoral Research Fellowship. [Jun 2022] Our work on doubly robust estimation of ITE is accepted as an oral presentation at the AMIA 2022 Annual Sympossium. [Jun 2022] Our work on weakly supervised disease localization is accepted at MICCAI 2022. [Aug 2021] I'm joining the University of Pittsburgh in the Intelligent Systems Program under the supervision of Dr. Kayhan Batmanghelich in Fall 2021. [May 2021] I graduated with a Master's degree in Computer Science from the University of Florida . Go Gators!! [Apr 2021] Our work to balance the unmatched controlled samples by simulating treated samples using GAN, is accepted in the Journal of Computer methods and programs in biomedicine update. [Dec 2020] Our work to estimate the Propensity score by dimensionality reduction using an autoencoder, is accepted in the Journal of the American Medical Informatics Association. [Apr 2020] I'm joining DISL lab as a graduate assistant under the supervision of Prof. Mattia Prosperi and Prof. Jiang Bian. [Aug 2019] I'm moving to the US to join the Master's program in the department of Computer Science in the University of Florida in Fall 2019.