Research
I am broadly interested in research at the interface of machine learning and chemistry.
Computational Retrosynthesis and Synthesis Planning
Modern chemical synthesis benefits tremendously from existing knowledge. I’m particularly interested in building computational tools for applications to complex natural products and for therapeutic design.
Machine and Deep Learning for Molecules
A focus of my research is on new algorithmic innovations for learning meaningful representations of small molecules. I am generally interested in interpretable methods for small-molecule representation learning, and excited about innovations in graph neural networks and set-based learning.
Cheminformatics and Computational Chemistry
A core aspect of my research is not only in the design of new algorithmic approaches, but in the application of deep learning methods for understanding small molecule property and activity relationships. We use techniques from cheminformatics and computational chemistry to understand complex chemical and biological processes including drug promiscuity and polypharmacology.
Catalysis
Organotransition metal catalysis has continued to transform the landscape of organic synthesis. I am generally interested in the design and discovery of new catalytic processes to enable new chemoselective reactions for complex molecule synthesis.
Complex Molecule Synthesis
My doctoral research focused on the total synthesis of architecturally-complex natural products. I enjoy solving new synthetic challenges, particularly in designing new synthetic routes to caged, polycyclic scaffolds.