Prof Can Li | Electrical Properties of Materials | Best Researcher Award
Prof. Dr. Can Li is an innovative researcher and Assistant Professor at the University of Hong Kong, specializing in neuromorphic computing, AI hardware, and memristor-based systems π§¬πΎ. He earned his Ph.D. in Electrical and Computer Engineering from UMass Amherst, after completing his M.S. and B.S. in Microelectronics at Peking University ππ. With over 90 publications, HK$45M+ in grants, and multiple patents, his work bridges advanced electronics, machine learning, and brain-inspired systems ππ. He actively mentors students, collaborates internationally, and serves on major editorial boards, shaping the future of intelligent computing technology ππ.
Prof Can Li, The University of Hong Kong, Hong Kong
Profile
Education π
Prof. Dr. Can Li earned his Ph.D. in Electrical and Computer Engineering from the University of Massachusetts Amherst in 2018, under the mentorship of Prof. Qiangfei Xia π§ π. His dissertation, βCMOS Compatible Memristor Networks for Brain-Inspired Computingβ, laid the foundation for his cutting-edge work in neuromorphic hardware βοΈπ§¬. He holds both M.S. (2012) and B.S. (2009) degrees in Microelectronics from Peking University, China, mentored by Prof. Wengang Wu π‘π. His academic path combines solid-state electronics, AI hardware, and advanced semiconductor design, empowering his innovations in brain-inspired and in-memory computing systems ππ§βπ§.
Experience πΌ
Prof. Can Li is currently an Assistant Professor at The University of Hong Kong (2020βPresent) ππ°, serving in the Department of Electrical and Electronic Engineering β‘. His academic role focuses on advanced system design, hardware acceleration, and energy-efficient computing π»π. Before this, he worked as a Research Associate at Hewlett Packard Labs in Palo Alto, California (2018β2020) πΊπΈ, contributing to architectural innovation within the System Architecture Lab π§ π οΈ. His industry and academic experience reflect a deep commitment to cutting-edge research in computer architecture and system performance optimization across real-world and theoretical applications ππ.
Achievements & Innovations π
Prof. Can Li has received numerous prestigious honors, including being ranked in the Top 1% worldwide by citations (Clarivate, 2022β2024) ππ, the Croucher Tak Wah Mak Innovation Award (2023) π§ π, the RGC Early Career Award (2021) π§βπ¬, and the NSFC Excellent Young Scientists Fund (2021) π. He holds 17 granted patents across the US and China, focused on analog content-addressable memory (CAM), fuzzy search, optical TCAMs, and neuromorphic systems π§π‘. These contributions demonstrate his pioneering work at the interface of hardware acceleration, AI computing, and next-gen memory systems ππ₯οΈ.
Book Contributions π
Prof. Can Li has significantly contributed to the field of neuromorphic and in-memory computing through key book chapters in high-impact scientific texts. He co-authored βIn-Memory Computing with Non-volatile Memristor CAM Circuitsβ and βTa/HfOβ Arrays for In-Memory Memristor Computingβ in Memristor Computing Systems (Springer, 2022) πβ‘. Additionally, he authored βSilicon Based Memristor Devices and Arraysβ in the Handbook of Memristor Networks (Springer, 2019) ππ§ . These works showcase his pioneering role in advancing memristor technology and its integration into next-generation computational architectures π₯οΈπ§©.
Research Focus π¬
Prof. Can Liβs research centers on cutting-edge innovations in artificial intelligence hardware and emerging memory technologies. His work focuses on developing AI inference and training accelerators using advanced memristor-based architectures π§ πΎ. He explores novel applications of memristor crossbar arrays, including image processing, hardware security, and pattern matching πΌοΈππ. A core part of his research also involves the CMOS-compatible integration of memristor devices at the array level, enhancing scalability and manufacturability βοΈπ§. By bridging nanotechnology with AI computing, Prof. Li is advancing the future of energy-efficient, high-performance computing systems for next-generation intelligent electronics ππ₯οΈ.
Publications π
Efficient Nonlinear Function Approximation in Analog Resistive Crossbars for Recurrent Neural Networks
Authors: Junyi Yang, Ruibin Mao, Mingrui Jiang, Can Li, Arindam Basu
Journal: Nature Communications, 2025
Current Opinions on Memristor-Accelerated Machine Learning Hardware
Authors: Mingrui Jiang, Yichun Xu, Zefan Li, Can Li
Journal: Current Opinion in Solid State and Materials Science, 2025
Emojis: πΎπ§ π§©π
Efficient Coherent Polarization Beam Combining of 16-Channel Femtosecond Fiber Lasers
Authors: Jiayi Zhang, Bo Ren, Can Li, Wenxue Li, Pu Zhou
Journal: Guangxue Xuebao/Acta Optica Sinica, 2025
Research Progress of Ultrafast Fiber Laser Amplifier Based on Gain Managed Nonlinearity (Invited)
Authors: Can Li, Bo Ren, Kun Guo, Jingyong Leng, Pu Zhou
Journal: Hongwai yu Jiguang Gongcheng/Infrared and Laser Engineering, 2025
Event-Based Multi-Object Tracking With Sparse Motion Features
Authors: Song Wang, Zhu Wang, Can Li, Xiaojuan Qi, Hayden Kwok Hay So
Journal: IEEE Access, 2025
An InGaZnO Synaptic Transistor Using Titanium-Oxide Traps at Back Channel for Neuromorphic Computing
Authors: B. F. Yang, Chen Zhang, Z. H. Zhang, Can Li, Xiaodong Huang
Journal: IEEE Transactions on Electron Devices, 2025