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Abstract: Machine learning for therapeutics offer incredible opportunities for expansion, innovation, and impact. Despite promises, many challenges exist. In this talk, the speaker will first highlight two high-impact but relatively understudied directions - ML-aided clinical trial design and low-data/cross-context biomedicine. Then, he will discuss challenges arising from therapeutics ML adoption in the wild, namely, generating actionable hypotheses and user interface with domain scientists. Lastly, challenges in infrastructure, such as data and benchmark, will be looked at. 

Speaker: Kexin Huang - https://www.kexinhuang.com/  

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