Hai "Helen" Li

Clare Boothe Luce Professor of Electrical and Computer Engineering

Hai “Helen” Li is the Clare Boothe Luce 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 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
• Chair of the Department of Electrical and Computer Engineering
• Professor of Computer Science

Contact Information

• Office Location: #407 Wilkinson Building, 534 Research Drive, Durham, NC 27701
• Office Phone: (919) 660-1373
• Email Address: hai.li@duke.edu
• Websites:
  • Google Scholar
  • Personal page

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
• Fellow. Institute of Electrical and Electronics Engineers (IEEE). 2018
• Distinguished Member. Association for Computing Machinery (ACM). 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 493: Projects in Electrical and Computer Engineering
• ECE 494: Projects in Electrical and Computer Engineering
• ECE 550D: Fundamentals of Computer Systems and Engineering
• ECE 661: Computer Engineering Machine Learning and Deep Neural Nets
• ECE 891: Internship

In the News

• Duke Leads Regional Effort to Reinvigorate America’s Semiconductor Infrastructure (Mar 9, 2023 | Pratt School of Engineering)
• Meet Duke’s 2023 Fellows in the ACC Academic Leadership Network (Jan 17, 2023 | Office of Faculty Advancement)
• Co-Designing Tomorrow's Computers (Sep 27, 2021 | Duke Engineering News)
• Duke and Pitt Train Enormous AI Models in Health Care, While Protecting Data Privacy (Sep 7, 2021 | Duke Engineering News)
• Duke Engineers are Using Machine Learning and Radar to Detect Drones in Complex Urban Settings (May 11, 2021 | Duke Engineering News)
• On Security's Frontiers: Trustworthy Computing (Oct 12, 2020 | Duke Engineering News)
• Duke-Led Team to Develop Privacy-Minded AI Health Learning Platform: NSF Convergence Accelerator Award (Sep 17, 2020 | Duke Engineering News)
• Detecting Backdoor Attacks on Artificial Neural Networks (Dec 23, 2019 | Duke Engineering News)
• Hai "Helen" Li Elected a Fellow of the IEEE (Dec 13, 2018 | Duke Engineering News)
• Hai Helen Li: New Pratt Professor Models the Future of Computing After the Human Brain (Jan 3, 2017)

Representative Publications

• Wang, Y; Li, S; Zheng, Q; Chang, A; Li, H; Chen, Y, EMS-i
  : An Efficient Memory System Design with Specialized Caching Mechanism for Recommendation Inference, Acm Transactions on Embedded Computing Systems, vol 22 no. 5s (2023), pp. 1-22 [10.1145/3609384] [abs].
• Yang, X; Yang, H; Doppa, JR; Pande, PP; Chakrabartys, K; Li, H, ESSENCE: Exploiting Structured Stochastic Gradient Pruning for Endurance-Aware ReRAM-Based In-Memory Training Systems, Ieee Transactions on Computer Aided Design of Integrated Circuits and Systems, vol 42 no. 7 (2023), pp. 2187-2199 [10.1109/TCAD.2022.3216546] [abs].
• Taylor, B; Li, H, A Hardware and Software Co-design Framework for Energy Efficient Neuromorphic Systems (2023) [10.2172/1985762] [abs].
• Li, Z; Zheng, Q; Chen, Y; Li, H, SpikeSen: Low-Latency In-Sensor-Intelligence Design With Neuromorphic Spiking Neurons, Ieee Transactions on Circuits and Systems Ii: Express Briefs, vol 70 no. 6 (2023), pp. 1876-1880 [10.1109/TCSII.2023.3235888] [abs].
• Zhang, T; Cheng, D; He, Y; Chen, Z; Dai, X; Xiong, L; Yan, F; Li, H; Chen, Y; Wen, W, NASRec: Weight Sharing Neural Architecture Search for Recommender Systems, Acm Web Conference 2023 Proceedings of the World Wide Web Conference, Www 2023 (2023), pp. 1199-1207 [10.1145/3543507.3583446] [abs].