CMX Lunch Seminar

Artificial intelligence (AI) holds tremendous promise to accelerate scientific discovery. However, significant gaps exist in translating these models to complex, real-world challenges in science, including integrating domain knowledge, improving data efficiency, and tailoring solutions to the unique needs of each lab. My work focuses on collaborative AI systems designed to bridge these gaps, in order for scientists to extract insights from high-dimensional data (e.g. animal behavior videos). First, we demonstrate how general-purpose video foundation models, by leveraging Internet-scale datasets, enable new ways to tackle domain-specific problems in behavior analysis. Next, we explore how recent advancements, such as large language models (LLMs), facilitate neurosymbolic approaches for analyzing complex scientific data through techniques such as library learning and external knowledge integration. Looking ahead, we envision AI agents collaborating with scientists throughout the scientific process to understand the world around us.