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AI for Science: Mind the Gaps
Payal Chandak · Yuanqi Du · Tianfan Fu · Wenhao Gao · Kexin Huang · Shengchao Liu · Ziming Liu · Gabriel Spadon · Max Tegmark · Hanchen Wang · Adrian Weller · Max Welling · Marinka Zitnik

Mon Dec 13 05:00 AM -- 01:55 PM (PST) @ None
Event URL: https://ai4sciencecommunity.github.io/ »

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) Unrealistic methodological assumptions or directions, (2) Overlooked scientific questions, (3) Limited exploration at the intersections of multiple disciplines, (4) Science of science, (5) 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.

Author Information

Payal Chandak (Harvard-MIT Health Sciences and Technology)
Yuanqi Du (George Mason University)
Tianfan Fu (Georgia Institute of Technology)
Wenhao Gao (Massachusetts Institute of Technology)
Kexin Huang (Stanford University)
Shengchao Liu (MILA-UdeM)
Ziming Liu (MIT)
Gabriel Spadon (University of Sao Paulo)
Max Tegmark (MIT)

Max Tegmark is a professor doing physics and AI research at MIT, and advocates for positive use of technology as president of the Future of Life Institute. He is the author of over 250 publications as well as the New York Times bestsellers “Life 3.0: Being Human in the Age of Artificial Intelligence” and "Our Mathematical Universe: My Quest for the Ultimate Nature of Reality". His AI research focuses on intelligible intelligence. His work with the Sloan Digital Sky Survey on galaxy clustering shared the first prize in Science magazine’s “Breakthrough of the Year: 2003.”

Hanchen Wang (University of Cambridge)

Hey, I am a 3rd Year PhD Student in Machine Learning at Cambridge, where I work with Joan Lasenby and Adrian Weller on Geometric Deep Learning (3D, Graph). Please check my website for my recent updates :)

Adrian Weller (Cambridge, Alan Turing Institute)

Adrian Weller is Programme Director for AI at The Alan Turing Institute, the UK national institute for data science and AI, where he is also a Turing Fellow leading work on safe and ethical AI. He is a Principal Research Fellow in Machine Learning at the University of Cambridge, and at the Leverhulme Centre for the Future of Intelligence where he is Programme Director for Trust and Society. His interests span AI, its commercial applications and helping to ensure beneficial outcomes for society. He serves on several boards including the Centre for Data Ethics and Innovation. Previously, Adrian held senior roles in finance.

Max Welling (University of Amsterdam / Qualcomm AI Research)
Marinka Zitnik (Harvard University)

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