AI for Science

Yuanqi Du · Tianfan Fu · Wenhao Gao · Kexin Huang · Shengchao Liu · Ziming Liu · Hanchen Wang · Connor Coley · Le Song · Linfeng Zhang · Marinka Zitnik

Room 309


Machine learning (ML) has revolutionized a wide array of scientific disciplines, including chemistry, biology, physics, material science, neuroscience, earth science, cosmology, electronics, mechanical science. It has solved scientific challenges that were never solved before, e.g., predicting 3D protein structure, imaging black holes, automating drug discovery, and so on. Despite this promise, several critical gaps stifle algorithmic and scientific innovation in AI for Science: (1) Under-explored theoretical analysis, (2) Unrealistic methodological assumptions or directions, (3) Overlooked scientific questions, (4) Limited exploration at the intersections of multiple disciplines, (5) Science of science, (6) Responsible use and development of AI for science. However, very little work has been done to bridge these gaps, mainly because of the missing link between distinct scientific communities. While many workshops focus on AI for specific scientific disciplines, they are all concerned with the methodological advances within a single discipline (e.g., biology) and are thus unable to examine the crucial questions mentioned above. This workshop will fulfill this unmet need and facilitate community building; with hundreds of ML researchers beginning projects in this area, the workshop will bring them together to consolidate the fast growing area of AI for Science into a recognized field.

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Timezone: America/Los_Angeles »