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个人简介
In my PhD I am thinking about how machine learning methods can be applied to problems in the physical sciences. Due to issues of robustness, generalization, and small dataset sizes I generally do not believe that replacing existing systems with deep neural networks is the best strategy for most practical problems (at least with current technology). Instead, I think that the way forward is to identify small parts of larger systems in science where machine learning could have an advantage over existing approaches, then try to develop methods that are well-suited to these niches. Currently the niche I am focusing on is early-stage drug candidate generation. Some specific problems I am working on are:
Applying Bayesian optimization to drug screening
Molecular property prediction with quantified uncertainty
Retrosynthetic planning (how to synthesize novel molecules)
Meaningful evaluation of ML algorithms in chemistry (average loss on a test set can be misleading)
Applying Bayesian optimization to drug screening
Molecular property prediction with quantified uncertainty
Retrosynthetic planning (how to synthesize novel molecules)
Meaningful evaluation of ML algorithms in chemistry (average loss on a test set can be misleading)
研究兴趣
论文共 13 篇作者统计合作学者相似作者
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CoRR (2023)
引用0浏览0EI引用
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Soeren Brandt,Ida Pavlichenko,Anna V. Shneidman,Haritosh Patel,Austin Tripp, Timothy S. B. Wong, Sean Lazaro, Ethan Thompson, Aubrey Maltz, Thomas Storwick, Holden Beggs,Katalin Szendrei-Temesi,
Proceedings of the National Academy of Sciences of the United States of Americano. 31 (2023): e2303928120-e2303928120
arxiv(2023)
引用3浏览0EI引用
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Ryan-Rhys Griffiths,Leo Klarner,Henry B. Moss,Aditya Ravuri,Sang Truong, Bojana Rankovic,Yuanqi Du,Arian Jamasb, Julius Schwartz,Austin Tripp,Gregory Kell,Anthony Bourached,
引用17浏览0EI引用
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D-Core
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