Before moving to academia, he spent years at Systems Research Group in Microsoft Research Redmond, working on advancing the state of the art in large-scale computing platforms supporting the major Microsoft services by making them more efficient, responsive, intelligent and programmable. In the context of machine learning plus systems, he has been working on scheduling deep learning workloads on multi-tenant GPU clusters to provide features like high efﬁciency, resource isolation, and fair sharing. He finished his Ph.D. in computer science at Rice University.