I am dedicated to building next-generation artificial intelligence (AI) models inspired by the functional and learning mechanisms of the human brain. My work focuses on developing algorithms that enable efficient, reliable, and adaptable AI models using a limited number of samples. In particular, I am interested in advancing the field of continual learning, self-supervised learning, and learning under noisy labels. My work also includes exploring the use of AI for scene understanding and multi-task learning, with the goal of advancing the adoption of AI in a wide range of applications that have a high impact on everyday life of people, including autonomous vehicles.

My research aims to bridge the gap between neuroscience and AI by applying insights from brain studies to the design of intelligent systems for next-generation AI models. I am passionate about driving the field of AI forward through innovative research that incorporates the latest findings in both neuroscience and machine learning. My work has been published in leading journals in both neuroscience and computer science, and I am committed to developing AI that is both effective and adaptable to various dynamic environments for use in real-world applications.