About

I am a PhD candidate in electrical engineering at the University of Southern California. I work as a research assistant at the Ming Hsieh Department of Electrical and Computer Engineering under the supervision of Mahdi Soltanolkotabi at the AI Foundations for the Sciences Center (AIF4S).

I am interested in artificial intelligence and its applications to scientific problems. My research focuses on deep learning approaches for computer vision problems in medical and computational imaging. Recently, I have been interested in diffusion models for image reconstruction and multi-modal foundation models for zero-shot recognition. During my PhD, I have also worked on projects in transfer learning, self-supervised learning, continual learning, learning theory and optimization.

Deep learning models today are data-hungry, fragile to small distribution shifts and computationally demanding. My long-term research goal is to lift the barriers of deep learning by designing more data-efficient, robust and light-weight algorithms, allowing deep learning to positively impact an even wider range of applications.