https://orcid.org/0000-0002-1486-8412
Overview
Yiran Chen earned his B.S. in 1998 and M.S. in 2001 from Tsinghua University and completed his Ph.D. in 2005 at Purdue University. In 2010, he joined the University of Pittsburgh as an Assistant Professor, where he was later promoted to Associate Professor with tenure in 2014, holding the prestigious Bicentennial Alumni Faculty Fellowship. He currently serves as the John Cocke Distinguished Professor of Electrical and Computer Engineering at Duke University.
Dr. Chen is the Director of the National Science Foundation (NSF) AI Institute for Edge Computing Leveraging (Athena), one of the 27 National AI Institutes in the United States. He also leads the NSF Industry-University Cooperative Research Center (IUCRC) for Alternative Sustainable and Intelligent Computing (ASIC) and co-directs the Duke Center for Computational Evolutionary Intelligence (DCEI). His research group focuses on innovations in emerging memory and storage systems, machine learning and neuromorphic computing, and edge computing.
Throughout his career, Dr. Chen has supervised or is currently supervising over 60 Ph.D. students and 4 postdoctoral scholars. Many of his mentees have achieved significant success, with 14 joining faculties at institutions in the United States, Turkey, Hong Kong, and China, including four NSF CAREER Awardees.
Dr. Chen has an extensive publication record, including one book, over 700 technical papers, and 96 U.S. patents. His work has earned 15 paper awards, including two Test-of-Time Awards, and 17 best paper nominations from leading international journals and conferences. Among his numerous accolades, he is one of only three individuals to receive Technical Achievement Awards from both the IEEE Circuits and Systems Society and the IEEE Computer Society, organizations with histories spanning 76 and 79 years, respectively.
He has served as a Distinguished Lecturer for the IEEE Council on Electronic Design Automation (CEDA) and the IEEE Circuits and Systems Society (CASS), as well as a Distinguished Visitor of the IEEE Computer Society (CS). He is a Fellow of the AAAS, ACM, IEEE, and NAI, and a member of the European Academy of Sciences and Arts. From 2021 to 2024, he chaired the ACM Special Interest Group on Design Automation (SIGDA), and he was Editor-in-Chief of the IEEE Circuits and Systems Magazine from 2020 to 2023. Currently, he is the inaugural Editor-in-Chief of the IEEE Transactions on Circuits and Systems for Artificial Intelligence (TCASAI) and the inaugural Chair of the IEEE Circuits and Systems Society Machine Learning Circuits and Systems (MLCAS) Technical Committee. He has served as a member of the Committee on Using Machine Learning in Safety-Critical Applications: Setting a Research Agenda, The National Academies of Sciences, Engineering, and Medicine.
Beyond academia, Dr. Chen has contributed to industry as Chairman of the Board, Independent Director, and consultant for several startups and venture capital firms. He is a passionate advocate for the responsible use of AI technologies. Dr. Chen is a founding member of the steering committee for the Academic Alliance on AI Policy (AAAIP) and a Fellow of the Asian American Scholar Forum (AASF).
Current Appointments & Affiliations
Recent Publications
AstroTune: AST-Assisted LLM Retrieval for Cross-Stage Design Flow Parameter Tuner
Conference Proceedings of the International Symposium on Physical Design · March 27, 2026 Modern VLSI design relies on EDA tools, which expose designers to high-dimensional and complex parameter spaces. Efficiently optimizing these parameters remains challenging, as manual tuning is time-consuming and heavily dependent on expert experience. Rec ... Full text CiteFlashSVD: Memory-Efficient Inference with Streaming for Low-Rank Model
Conference Proceedings of the Aaai Conference on Artificial Intelligence · January 1, 2026 Singular Value Decomposition (SVD) has recently gained traction as an effective compression technique for large language models (LLMs), with many studies reporting 2080% parameter reduction at minimal accuracy cost. However, despite reducing weight memory, ... Full text CiteFocus: A Streaming Concentration Architecture for Efficient Vision-Language Models
Conference Proceedings International Symposium on High Performance Computer Architecture · January 1, 2026 Vision-Language Models (VLMs) have demonstrated strong performance on tasks such as video captioning and visual question answering. However, their growing scale and video-level inputs lead to significant computational and memory overhead, posing challenges ... Full text CiteRecent Grants
Neuromorphic Computing Circuit Primitive Testbeds
ResearchPrincipal Investigator · Awarded by Department of Energy · 2025 - 2030Scalable Ultra-Low Variation Analog-Time Neuron Network (S-UATN) Accelerator
ResearchPrincipal Investigator · Awarded by Northwestern University · 2025 - 2030DoD Center of Excellence in Advanced Computing and Software (COE-ACS)
ResearchPrincipal Investigator · Awarded by Georgia State University · 2023 - 2028View All Grants