Conditional Independence Testing with Medical Imaging (currently focusing on Conditional Randomization-based Tests and Conditional Mutual Information Neural Estimators)

Probabilistic Generative Models for Image Generation (currently focusing on Diffusion Models, Generative Adversarial Networks (GANs), and Normalizing Flows)

Uncertainty Quantification for Deep Learning (e.g., Probabilistic Modeling, Dirichlet Modeling)

Learning Paradigms for Reduced Labeling Effort (e.g., Active learning, Semi-supervised learning)

Sequential Decision-Making (e.g., Reinforcement Learning, Inverse Reinforcement Learning)