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 🧬💾. 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 📘🔌. With over 90 publications, HK$45M+ in grants, and multiple patents, his work bridges advanced electronics, machine learning, and brain-inspired systems 🌍📊. He actively mentors students, collaborates internationally, and serves on major editorial boards, shaping the future of intelligent computing technology 🚀🔍.

Prof Can Li, The University of Hong Kong, Hong Kong

Profile

SCOPUS

GOOGLESCHOLAR

Education 🎓

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 🧠📘. His dissertation, “CMOS Compatible Memristor Networks for Brain-Inspired Computing”, laid the foundation for his cutting-edge work in neuromorphic hardware ⚙️🧬. He holds both M.S. (2012) and B.S. (2009) degrees in Microelectronics from Peking University, China, mentored by Prof. Wengang Wu 📡🔌. His academic path combines solid-state electronics, AI hardware, and advanced semiconductor design, empowering his innovations in brain-inspired and in-memory computing systems 🌐🧑‍🔧.

Experience 💼

Prof. Can Li is currently an Assistant Professor at The University of Hong Kong (2020–Present) 🇭🇰, serving in the Department of Electrical and Electronic Engineering ⚡. His academic role focuses on advanced system design, hardware acceleration, and energy-efficient computing 💻🔋. Before this, he worked as a Research Associate at Hewlett Packard Labs in Palo Alto, California (2018–2020) 🇺🇸, contributing to architectural innovation within the System Architecture Lab 🧠🛠️. 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 🚀📊.

Achievements & Innovations 🏅

Prof. Can Li has received numerous prestigious honors, including being ranked in the Top 1% worldwide by citations (Clarivate, 2022–2024) 🌍📊, the Croucher Tak Wah Mak Innovation Award (2023) 🧠🏆, the RGC Early Career Award (2021) 🧑‍🔬, and the NSFC Excellent Young Scientists Fund (2021) 🌟. He holds 17 granted patents across the US and China, focused on analog content-addressable memory (CAM), fuzzy search, optical TCAMs, and neuromorphic systems 🔧💡. These contributions demonstrate his pioneering work at the interface of hardware acceleration, AI computing, and next-gen memory systems 🚀🖥️.

Book Contributions 📘

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 “In-Memory Computing with Non-volatile Memristor CAM Circuits” and “Ta/HfO₂ Arrays for In-Memory Memristor Computing” in Memristor Computing Systems (Springer, 2022) 📘⚡. Additionally, he authored “Silicon Based Memristor Devices and Arrays” in the Handbook of Memristor Networks (Springer, 2019) 🔍🧠. These works showcase his pioneering role in advancing memristor technology and its integration into next-generation computational architectures 🖥️🧩.

Research Focus 🔬

Prof. Can Li’s 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 🧠💾. He explores novel applications of memristor crossbar arrays, including image processing, hardware security, and pattern matching 🖼️🔐🔍. A core part of his research also involves the CMOS-compatible integration of memristor devices at the array level, enhancing scalability and manufacturability ⚙️🔧. By bridging nanotechnology with AI computing, Prof. Li is advancing the future of energy-efficient, high-performance computing systems for next-generation intelligent electronics 🚀🖥️.

Publications 📚

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: 💾🧠🧩📈

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