Area of research
Artificial Intelligence
Research interest
Research interests include Explainable Artificial Intelligence (XAI), Adversarial Robustness in Machine Learning, Topic Modeling, and Advanced Graph Neural Networks.
Improving Human Verification of LLM Reasoning through Interactive Explanation Interfaces
CoroNet: a deep network architecture for enhanced identification of COVID-19 from chest x-ray images
Explaining Image Classifiers by Removing Input Features Using Generative Models
SAM: The Sensitivity of Attribution Methods to Hyperparameters
SAM: The Sensitivity of Attribution Methods to Hyperparameters
DEEP-URL: A Model-Aware Approach to Blind Deconvolution Based on Deep Unfolded Richardson-Lucy Network
The shape and simplicity biases of adversarially robust ImageNet-trained CNNs
Improving Robustness to Adversarial Examples by Encouraging Discriminative Features
Accurate segmentation of lung fields on chest radiographs using deep convolutional networks
Automatic estimation of heart boundaries and cardiothoracic ratio from chest x-ray images