– Cross-Couplings in a New Age –
Enzymatic Organohalide Couplings: Cross-coupling reactions are indispensable in synthesis, but current methods remain inefficient, reliant on precious metals, and environmentally unsustainable. By replacing traditional chemical approaches with tunable enzymes, we seek to establish new, sustainable, and selective cross coupling reactions.
Carbonyls as Latent Ambiphiles: Carbonyls are among the most common feedstocks in chemistry, but direct routes to deoxygenative bifunctionalization of carbonyls are rare. By introducing new carbonyl reactivity logic, we seek to design multicomponent coupling methodologies to convert common carbonyls into high-value, highly-substituted deoxygenated carbon centers.
Decoding Graph Neural Networks: Machine learning (ML) models have been leveraged powerfully to find patterns in data that are otherwise obscure to intuition, but there are insufficient tools to extract enabling insights from state-of-the-art deep models. By developing chemistry-specific tools to interpret graph neural networks, we seek to use ML to guide reaction and reagent design.
Now accepting applications for students and post-doctoral fellows!