AI for Science in the Era of Large Language Models

Xuan Wang

Department of Computer Science, Virginia Tech, USA

Time: TBD

Location: Philadelphia, Pennsylvania

Abstract:

The capabilities of AI in the realm of science span a wide spectrum, from the atomic level, where it solves partial differential equations for quantum systems, to the molecular level, predicting chemical or protein structures, and even extending to societal predictions like infectious disease outbreaks. Recent advancements in large language models (LLMs), exemplified by models like ChatGPT, have showcased significant prowess in tasks involving natural language, such as translating languages, constructing chatbots, and answering questions. When we consider scientific data, we notice a resemblance to natural language in terms of sequences – scientific literature and health records presented as text, bio-omics data arranged in sequences, or sensor data like brain signals. The question arises: Can we harness the potential of these recent LLMs to drive scientific progress? In this tutorial, we will explore the application of large language models to three crucial categories of scientific data: 1) textual data, 2) biomedical sequences, and 3) brain signals. Furthermore, we will delve into LLMs’ challenges in scientific research, including ensuring trustworthiness, achieving personalization, and adapting to multi-modal data representation.

Slides:

TBD

Presenters:

Xuan Wang is an Assistant Professor in the Computer Science Department at Virginia Tech. Her research focuses on natural language processing and text mining, emphasizing applications to science and healthcare domains. Her current projects include NLP and text mining with extremely weak supervision; text-augmented knowledge graph reasoning; fact-checking and trustworthy NLP, AI for science; and AI for healthcare. She received a Ph.D. degree in Computer Science, an M.S. degree in Statistics, and an M.S. degree in Biochemistry from the University of Illinois Urbana-Champaign in 2022, 2017, and 2015, respectively, and a B.S. degree in Biological Science from Tsinghua University in 2013. She has delivered tutorials in IEEE-BigData 2019, WWW 2022, and KDD 2022.