PhD student at Stanford University
Daniel Kang is a final year PhD student in the Stanford DAWN lab, co-advised by Profs Peter Bailis and Matei Zaharia. His research focuses on systems approaches for deploying unreliable and expensive machine learning methods efficiently and reliably. In particular, he focuses on using cheap approximations to accelerate query processing algorithms and new programming models for ML data management. Daniel is collaborating with autonomous vehicle companies and ecologists to deploy his research.
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Monitoring and Quality Assurance of Complex ML Deployments via Assertions