Hai "Helen" Li

Professor in the Department of Electrical and Computer Engineering

Hai “Helen” Li is the Marie Foote Reel E'46 Distinguished Professor and chair of Electrical & Computer Engineering at Duke.

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.

She received her B.S. and M.S. degrees from Tsinghua University, and her Ph.D. from Purdue University. Dr. Li served/serves as the 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 members of over 30 international conference series. Dr. Li is a Distinguished Lecturer of the IEEE CAS Society (2018-2019) and a Distinguished Speaker of ACM (2017-2020). Dr. Li is a recipient of the NSF Career Award, DARPA Young Faculty Award, TUM-IAS Hans Fischer Fellowship from Germany, ELATE Fellowship, nine best paper awards and another nine best paper nominations. Dr. Li is a fellow of ACM and IEEE.

 

Appointments and Affiliations

  • Professor in the Department of Electrical and Computer Engineering
  • Marie Foote Reel E'46 Distinguished Professor of Electrical and Computer Engineering
  • Chair of the Department of Electrical and Computer Engineering
  • Professor of Computer Science

Contact Information

Education

  • Ph.D. Purdue University, 2004

Research Interests

Neuromorphic computing systems
Machine learning acceleration and trustworthy AI
Emerging memory technologies, circuit and architecture
Low power circuits and systems

Awards, Honors, and Distinctions

  • Fellow, Executive Leadership in Academic Technology, Engineering and Science (ELATES). Drexel University. 2022
  • Distinguished Member. Association for Computing Machinery (ACM). 2018
  • Fellow. Institute of Electrical and Electronics Engineers (IEEE). 2018
  • Best Paper Award for the paper titled “Classification Accuracy Improvement for Neuromorphic Computing Systems with One-level Precision Synapses”. Asia and South Pacific Design Automation Conference (ASPDAC). 2017
  • Fulton C. Noss Faculty Fellow. University of Pittsburgh. 2016
  • Best Paper Award for the paper titled “Quantitative Modeling of Racetrack Memory - A Tradeoff among Area, Performance, and Power”. Asia and South Pacific Design Automation Conference (ASPDAC). 2015
  • Air Force Summer Faculty Fellowship Program Award (AF-SFFP). AFRL/RITC. 2015
  • Best Paper Award for the paper titled “A Weighted Sensing Scheme for ReRAM-based Cross-point Memory Array”. IEEE Computer Society Annual Symposium on VLSI (ISVLSI). 2014
  • Best Paper Award for the paper titled “Coordinating Prefetching and STT-RAM based Last-level Cache Management for Multicore Systems”. Proceedings of the 23rd ACM International Conference on Great Lakes Symposium on VLSI (GLSVLSI). 2013
  • Air Force Visiting Faculty Research Program (VFRP) Fellowship. AFRL/RIB. 2013
  • DARPA Young Faculty Award. Defense Advanced Research Projects Agency (DARPA). 2013
  • NSF Career Award. National Science Foundation (NSF). 2012
  • Air Force Summer Faculty Fellowship Program Award (AF-SFFP). AFRL/RITC. 2011
  • Best Paper Award for the paper titled “Combined Magnetic- and Circuit-level Enhancements for the Nondestructive Self-Reference Scheme of STT-RAM”. ACM/IEEE International Symposium on Low Power Electronics and Design (ISLPED). 2010
  • Best Paper Award for the paper titled “Design Margin Exploration of Spin-Torque Transfer RAM (SPRAM)”. the 9th International Symposium on Quality Electronic Design (ISQED). 2008

Courses Taught

  • ECE 891: Internship
  • ECE 661: Computer Engineering Machine Learning and Deep Neural Nets
  • ECE 550D: Fundamentals of Computer Systems and Engineering
  • ECE 494: Projects in Electrical and Computer Engineering
  • ECE 493: Projects in Electrical and Computer Engineering

In the News

Representative Publications

  • Kim, B., H. Li, and Y. Chen. “Processing-in-Memory Designs Based on Emerging Technology for Efficient Machine Learning Acceleration.” In Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI, 614–19, 2024. https://doi.org/10.1145/3649476.3660367.
  • Krestinskaya, Olga, Mohammed E. Fouda, Hadjer Benmeziane, Kaoutar El Maghraoui, Abu Sebastian, Wei D. Lu, Mario Lanza, et al. “Neural architecture search for in-memory computing-based deep learning accelerators.” Nature Reviews Electrical Engineering 1, no. 6 (May 20, 2024): 374–90. https://doi.org/10.1038/s44287-024-00052-7.
  • Li, S., Y. Wang, E. Hanson, A. Chang, Y. Seok Ki, H. Li, and Y. Chen. “NDRec: A Near-Data Processing System for Training Large-Scale Recommendation Models.” IEEE Transactions on Computers 73, no. 5 (May 1, 2024): 1248–61. https://doi.org/10.1109/TC.2024.3365939.
  • Wang, B., M. Lin, T. Zhou, P. Zhou, A. Li, M. Pang, H. Li, and Y. Chen. “Efficient, Direct, and Restricted Black-Box Graph Evasion Attacks to Any-Layer Graph Neural Networks via Influence Function.” In WSDM 2024 - Proceedings of the 17th ACM International Conference on Web Search and Data Mining, 693–701, 2024. https://doi.org/10.1145/3616855.3635826.
  • Yang, X., Z. Wang, X. S. Hu, C. H. Kim, S. Yu, M. Pajic, R. Manohar, Y. Chen, and H. H. Li. “Neuro-Symbolic Computing: Advancements and Challenges in Hardware-Software Co-Design.” IEEE Transactions on Circuits and Systems II: Express Briefs 71, no. 3 (March 1, 2024): 1683–89. https://doi.org/10.1109/TCSII.2023.3336251.