Dr. Sewon Min is an emerging leader in computational linguistics and artificial intelligence, renowned for her transformative contributions to large language model research. She currently serves as an Assistant Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley, where she is affiliated with the Berkeley Artificial Intelligence Research Lab and the Berkeley NLP Group. Additionally, she is a research scientist at the Allen Institute for AI, contributing to cutting-edge developments in natural language processing. Dr. Min earned her PhD and MSc degrees in Computer Science and Engineering from the University of Washington, supported by a J.P. Morgan PhD Fellowship, and completed her undergraduate studies with a BSc in Computer Science and Engineering from Seoul National University.
Dr. Min's groundbreaking research has fundamentally reshaped our understanding of how language models utilize textual data through her innovative work on nonparametric language modeling. Her doctoral dissertation Rethinking Data Use in Large Language Models earned her an Honorable Mention for the ACM Doctoral Dissertation Award, recognized for significantly advancing the field's comprehension of language model mechanics. She received the 2024 Western Association of Graduate Schools ProQuest Innovation in Technology Award for developing models that enable real-time information retrieval without requiring retraining, thereby creating more accurate and resource-efficient AI systems. Her conceptual framework, which shifts from memorization-based approaches to dynamically retrieving information as needed, has garnered substantial scholarly attention with over 15,100 citations as documented in Google Scholar.
Beyond her research contributions, Dr. Min actively shapes the direction of natural language processing through community leadership and mentorship, having co-organized influential workshops including the ACL 2023 event on Retrieval-based Language Models. She currently teaches advanced courses at UC Berkeley, including CS 294-288 on Data-Centric Large Language Models, helping to cultivate the next generation of researchers in this rapidly evolving field. Dr. Min continues to champion the nonparametric language modeling paradigm, articulating a vision for future AI systems that prioritize efficient scaling, improved factuality, and decentralization to address current limitations. Her ongoing research at the intersection of machine learning and natural language processing promises to further bridge the gap between theoretical innovation and practical applications in artificial intelligence systems.
