Prof Can Li | Electrical Properties of Materials | Best Researcher Award

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 πŸ§¬πŸ’Ύ. 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 πŸ“˜πŸ”Œ. With over 90 publications, HK$45M+ in grants, and multiple patents, his work bridges advanced electronics, machine learning, and brain-inspired systems πŸŒπŸ“Š. He actively mentors students, collaborates internationally, and serves on major editorial boards, shaping the future of intelligent computing technology πŸš€πŸ”.

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

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GOOGLESCHOLAR

Education πŸŽ“

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 πŸ§ πŸ“˜. His dissertation, β€œCMOS Compatible Memristor Networks for Brain-Inspired Computing”, laid the foundation for his cutting-edge work in neuromorphic hardware βš™οΈπŸ§¬. He holds both M.S. (2012) and B.S. (2009) degrees in Microelectronics from Peking University, China, mentored by Prof. Wengang Wu πŸ“‘πŸ”Œ. His academic path combines solid-state electronics, AI hardware, and advanced semiconductor design, empowering his innovations in brain-inspired and in-memory computing systems πŸŒπŸ§‘β€πŸ”§.

Experience πŸ’Ό

Prof. Can Li is currently an Assistant Professor at The University of Hong Kong (2020–Present) πŸ‡­πŸ‡°, serving in the Department of Electrical and Electronic Engineering ⚑. His academic role focuses on advanced system design, hardware acceleration, and energy-efficient computing πŸ’»πŸ”‹. Before this, he worked as a Research Associate at Hewlett Packard Labs in Palo Alto, California (2018–2020) πŸ‡ΊπŸ‡Έ, contributing to architectural innovation within the System Architecture Lab πŸ§ πŸ› 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 πŸš€πŸ“Š.

Achievements & Innovations πŸ…

Prof. Can Li has received numerous prestigious honors, including being ranked in the Top 1% worldwide by citations (Clarivate, 2022–2024) πŸŒπŸ“Š, the Croucher Tak Wah Mak Innovation Award (2023) πŸ§ πŸ†, the RGC Early Career Award (2021) πŸ§‘β€πŸ”¬, 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 πŸ”§πŸ’‘. These contributions demonstrate his pioneering work at the interface of hardware acceleration, AI computing, and next-gen memory systems πŸš€πŸ–₯️.

Book Contributions πŸ“˜

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 β€œIn-Memory Computing with Non-volatile Memristor CAM Circuits” and β€œTa/HfOβ‚‚ Arrays for In-Memory Memristor Computing” in Memristor Computing Systems (Springer, 2022) πŸ“˜βš‘. Additionally, he authored β€œSilicon Based Memristor Devices and Arrays” in the Handbook of Memristor Networks (Springer, 2019) πŸ”πŸ§ . These works showcase his pioneering role in advancing memristor technology and its integration into next-generation computational architectures πŸ–₯️🧩.

Research Focus πŸ”¬

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 πŸ§ πŸ’Ύ. He explores novel applications of memristor crossbar arrays, including image processing, hardware security, and pattern matching πŸ–ΌοΈπŸ”πŸ”. A core part of his research also involves the CMOS-compatible integration of memristor devices at the array level, enhancing scalability and manufacturability βš™οΈπŸ”§. By bridging nanotechnology with AI computing, Prof. Li is advancing the future of energy-efficient, high-performance computing systems for next-generation intelligent electronics πŸš€πŸ–₯️.

Publications πŸ“š

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: πŸ’ΎπŸ§ πŸ§©πŸ“ˆ

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

Dr Zhang Jiayang | Electrical Properties of Materials | Best Researcher Award

Dr Zhang Jiayang | Electrical Properties of Materials | Best Researcher Award

Dr. Jiayang Zhang (Student Member, IEEE) is a dedicated young researcher born in 2000 in Liaoning Province, China πŸ‡¨πŸ‡³. He earned his B.S. in Electrical Engineering from Liaoning Institute of Technology in 2022 πŸŽ“ and is currently pursuing his Ph.D. at Northeast Electric Power University, Jilin πŸ”ŒπŸ“˜. His research focuses on power conversion and control, electronic converter modeling, and renewable energy regulation ⚑🌱. With a growing publication record and technical engagement in smart grid systems, Jiayang aims to contribute to the development of sustainable and intelligent power infrastructure worldwide πŸŒπŸ’‘.

Dr Zhang Jiayang, Northeast Electric Power University, China

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ORCID

Education πŸŽ“

Dr. Jiayang Zhang began his academic journey in Electrical Engineering at Liaoning Institute of Technology, where he earned his B.S. degree in 2022 πŸŽ“πŸ”Œ. He is currently pursuing his Ph.D. in Electrical Engineering at Northeast Electric Power University in Jilin, China (2024–2027) 🏫⚑. His doctoral studies are focused on advanced topics such as power conversion, converter control modeling, and renewable energy regulation πŸŒΏπŸ’‘. Through rigorous academic training and active research engagement, Jiayang is building a strong foundation to contribute meaningfully to the development of smarter and more sustainable power systems globally πŸŒπŸ“˜.

Experience πŸ”Œ

Dr. Jiayang Zhang is currently pursuing his Ph.D. in Electrical Engineering at Northeast Electric Power University, Jilin, China πŸŽ“πŸ”Œ. As a doctoral researcher, he is deeply involved in cutting-edge work on power electronic converter control, renewable energy integration, and grid stability ⚑🧠. He has contributed to multiple scholarly projects and technical publications, showcasing skills in data analysis, model development, simulation, and system validation πŸ§ͺπŸ’». His growing experience reflects a strong commitment to addressing global energy challenges through intelligent and sustainable engineering solutions 🌱🌐.

Research Focus πŸ”‹

Dr. Jiayang Zhang’s research focuses on advancing technologies in power electronics and renewable energy systems βš‘πŸ”Œ. His primary interests include power conversion and control technology, aiming to optimize the performance of converters in dynamic power environments πŸ”„. He also specializes in control modeling of power electronic converters, which is crucial for improving grid stability and operational efficiency βš™οΈπŸ“ˆ. Additionally, his work on renewable energy regulation technology addresses the integration of wind and solar energy into modern power systems πŸŒ¬οΈβ˜€οΈ. His research contributes to the development of sustainable, efficient, and intelligent power gridsΒ for the future πŸŒπŸ”‹.

Publications πŸ“š

ESVG Adaptive Control Method for Fast Frequency Support of Wind Farm
✍️ Authors: Yong Sun, Haifeng Zhang, Xiaozhe Song, Yifu Zhang, Song Gao, Jiayang Zhang
πŸ“š Journal: Energy Engineering, 2025
⚑ Theme: Adaptive control, wind energy, frequency support, renewable power systems

High Frequency Oscillation Energy Propagation in MMC-HVDC Receiving-End Converter Station
✍️ Authors: Jikai Chen, Jiayang Zhang, Li Yang, Chongbo Sun, Yinghong Hu
πŸ“š Conference: 2023 IEEE 2nd International Power Electronics and Application Symposium (PEAS)
πŸ”Œ Theme: HVDC systems, energy propagation, converter station stability

Β Analysis and Optimization of Active Power-Frequency Support Capability of Static Synchronous Compensator in Wind Farm
✍️ Authors: Jikai Chen, Jiayang Zhang, Haoru Li, Zhuang Chu, Liwei Zhang, Hongpeng Liu
πŸ“š Journal: Automation of Electric Power Systems
πŸŒ€ Theme: Wind energy integration, STATCOM, power-frequency optimization