Distinguished Lecture || Can Large Language Models help with Scientific Hypothesis Generation?

Can Large Language Models help with Scientific Hypothesis Generation?

Machine learning foundation models, particularly large language models (LLMs), have revolutionized traditional applications in computer vision and natural language processing, marking a significant shift in recent years. Building on these advancements, recent efforts have explored the potential of foundation models in hypothesis generation, highlighting their possibility in aiding human researchers in scientific discovery. We are envisioning a future where academia increasingly integrates foundation models to accelerate and enhance the process of scientific discovery. Two key challenges that need to be addressed include: (1) how to effectively harness the parametric knowledge embedded in foundation models to propel scientific discovery? and (2) how to develop rigorous yet scalable methods to evaluate the effectiveness of foundation models in supporting scientific research? In this talk, I will discuss the current state-of-the art research work on this topic and present our most recent approaches to answer these questions. I will also talk about my NIH funded projects.

About the Speaker

Dr. Aidong Zhang is Thomas M. Linville Professor of Computer Science in the School of Engineering and Applied Sciences at University of Virginia (UVA). She also holds joint appointments with the Department of Biomedical Engineering and School of Data Science at University of Virginia. Her research interests include machine learning, data mining, bioinformatics, and health informatics. Dr. Zhang is a fellow of ACM, AIMBE, and IEEE. Dr. Zhang is also a member of the Virginia Academy of Science, Engineering and Medicine.

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