Research

Publications

For an updated list of my research publications, please visit my Google Scholar page. For my thoughts on my own papers, please see here.

Current work (as of 2023)

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)

General research interests

• Sequential decision making (e.g. Bayesian optimization)
• Gaussian processes and other kernel methods
• Learning on small datasets
• Model robustness and reliability
• Uncertainty quantification
• Evaluation of machine learning algorithms in a way that reflects their practical usage
• AI safety/alignment