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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

Sat Jul 23 06:00 AM -- 03:00 PM (PDT) @ Room 309
Event URL: http://www.ai4science.net/icml22/ »

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.

Author Information

Yuanqi Du (George Mason University)

Yuanqi Du Yuanqi Du is a senior undergraduate student studying Computer Science at George Mason University. He has broad interests in machine learning and data mining. He works on Outlier Detection, American Sign Language Recognition (Milimeter Wave Signals & Kinect), Medical Image Analysis, Protein Structure Prediction, Molecule Generation, Deep Generative Model and Deep Graph Learning. He worked with Dr. Liang Zhao, Dr. Amarda Shehu, Dr. Parth Pathak, Dr. Carlotta Domeniconi while he was at GMU. He worked with Dr. Hu Han and Dr. S. Kevin Zhou on Medical Image Analysis in the MIRACLE Lab. He worked with Dr. Jianwei Zhu on Protein Structure Prediction in Microsoft Research Asia Machine Learning and Computational Biology group. Besides Machine Learning, he is very fasinated by Sciences and very intrerested in developing ML tools for scentific problems.

Tianfan Fu (Georgia Institute of Technology)
Wenhao Gao (Massachusetts Institute of Technology)
Kexin Huang (Harvard University)
Shengchao Liu (Mila, Université de Montréal)
Ziming Liu (MIT)
Hanchen Wang (Cambridge; Caltech)

ML PhD Candidate

Connor Coley (MIT)
Le Song (Biomap & MBZUAI)
Linfeng Zhang (DP Technology)
Marinka Zitnik (Harvard University)

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