Shreya Shankar is a machine learning engineer and a PhD candidate in computer science at UC Berkeley, where she develops systems to help people effectively use AI for data work. Her research focuses on creating practical tools and frameworks for building reliable ML systems, including recent groundbreaking work on LLM evaluation and data quality. She has published influential papers on evaluating and aligning LLM systems, including “Who Validates the Validators?”, which examines how to systematically align LLM assessments with human preferences.
Before starting her PhD, Shreya worked as an ML engineer in the industry and received her bachelor's and master's degrees in computer science from Stanford. Her work has been published at leading conferences on data management and HCI, including SIGMOD, VLDB, and UIST. She is currently an NDSEG Fellowship recipient and actively collaborates with major tech companies and startups, implementing her research in real-world production environments. Her recent projects, such as DocETL and SPADE, demonstrate her ability to bridge theoretical foundations with practical solutions that help developers create more reliable AI systems.