Overview
Hai (Helen) Li is the Marie Foote Reel E’46 Distinguished Professor and Department Chair of the Electrical and Computer Engineering Department at Duke University. She received her B.S. and M.S. from Tsinghua University and her Ph.D. from Purdue University. Her research interests include neuromorphic circuits and systems for brain-inspired computing, machine learning acceleration and trustworthy AI, conventional and emerging memory design and architecture, and software and hardware co-design. Dr. Li served/serves as the Associate Editor-in-Chief and Associate Editor for multiple IEEE and ACM journals. She was the General Chair or Technical Program Chair of numerous IEEE/ACM conferences and the Technical Program Committee member of over 30 international conference series. Dr. Li is a Distinguished Lecturer of the IEEE CAS Society and a Distinguished Speaker of ACM. Dr. Li is a recipient of the IEEE Edward J. McCluskey Technical Achievement Award, Ten Year Retrospective Influential Paper Award from ICCAD, TUM-IAS Hans Fischer Fellowship from Germany, ELATE Fellowship, ten best paper awards, and another ten best paper nominations. Dr. Li is a fellow of ACM, IEEE, and NAI.
Current Appointments & Affiliations
Recent Publications
Recap of the 62nd ACM/IEEE Design Automation Conference (DAC62): The “Chips to Systems Conference”
Journal Article IEEE Design & Test · December 2025 Full text CiteMulPi: A Multi-class and Patient-Independent Epileptic Seizure Classifier With Co-Designed Input-stationary Computing-in-SRAM.
Journal Article IEEE transactions on biomedical circuits and systems · August 2025 Unprovoked seizures have threatened epilepsy patients over 70 million. Automated classification to detect and predict seizures could bring seizure-free lives to epilepsy patients, delivering them from fatal danger and increasing the quality of life. Authen ... Full text CiteAutoRAC: Automated Processing-in-Memory Accelerator Design for Recommender Systems
Conference Proceedings of the ACM Great Lakes Symposium on VLSI Glsvlsi · June 29, 2025 The performance bottleneck of deep-learning-based recommender systems resides in their backbone Deep Neural Networks. By integrating Processing-In-Memory (PIM) architectures, researchers can reduce data movement and enhance energy efficiency, paving the wa ... Full text CiteRecent Grants
Center of Neuromorphic Computing under Extreme Environments
ResearchPrincipal Investigator · Awarded by University of Southern California · 2024 - 2029DoD Center of Excellence in Advanced Computing and Software (COE-ACS)
ResearchCo-Principal Investigator · Awarded by Georgia State University · 2023 - 2028PARTNER: Neuro-Inspired AI for the Edge at UTSA (NAIAD)
ResearchPrincipal Investigator · Awarded by The University of Texas at San Antonio · 2023 - 2027View All Grants