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My role as a Research Engineer at Google DeepMind involves driving advancements in ML for Systems. I work to significantly improve the efficiency and performance of massive-scale systems by applying machine learning to uncover intricate features and patterns. My contributions span essential system components, from ensuring reliable consensus to optimizing bug finding, caching, indexing, and query processes.

I'm especially focused on the exciting potential of Large Language Models (LLMs) to create the next generation of smart infrastructure systems that can build, maintain, debug and configure themselves autonomously.

Prior to joining Google I received my PhD in Computer Science from Cornell University. During my PhD I focused on building Distributed Systems that rely on strong foundations in Distributed Systems Theory. I specialized in consensus protocols, replication, and evolving systems. Since my PhD I have also been maintaining paxos.systems, which focuses on helping developers understand and implement the notoriously difficult to understand Paxos consensus algorithm.

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