Overview
Kaizhu Huang works on machine learning, neural information processing, and pattern recognition. Before joining DKU, he was a full professor at Xi’an Jiaotong-Liverpool University (XJTLU) and Associate Dean of research in School of Advanced Technology, XJTLU. Prof. Huang obtained his PhD degree from Chinese University of Hong Kong (CUHK), Master degree from Institute of Automation, Chinese Academy of Sciences, and Bachelor degree from Xi'an Jiaotong University. He worked at Fujitsu Research Centre, CUHK, University of Bristol, Chinese Academy of Sciences from 2004 to 2012. He was the recipient of the 2011 Asia Pacific Neural Network Society Young Researcher Award. He received the best paper or book awards for seven times. He has published 9 books and over 230 international research papers including 120+ journal papers (e.g. IEEE T-PAMI, IEEE T-NNLS, IEEE T-BME, IEEE T-Cybernetics, JMLR) and 110+ conference papers (e.g. NeurIPS, IJCAI, SIGIR, UAI, CIKM, ICDM, ICML, ECML, CVPR). He serves as associated editors/advisory board members in a number of international journals and book series. He was invited as a keynote speaker in more than 30 international conferences or workshops.
Current Appointments & Affiliations
Professor of Electrical and Computer Engineering at Duke Kunshan University
·
2022 - Present
DKU Faculty
Director of Data Science Research Center (DSRC) at Duke Kunshan University
·
2023 - Present
DKU Faculty
Recent Publications
A benchmark and method for photographed table reasoning
Journal Article Pattern Recognition · October 1, 2026 With the advancement of large language models (LLMs) and multimodal LLMs (MLLMs), table reasoning has achieved significant progress. However, most existing works focus predominantly on textual or rendered tables, which differ substantially from real-world ... Full text CiteDiff-Oracle: Learning Styles and Contents to Augment Realistic Oracle Characters in Diffusion Model
Journal Article ACM Transactions on Multimedia Computing, Communications, and Applications · June 30, 2026 Recognizing oracle bone scripts plays an important role in Chinese archaeology and philology. However, a significant challenge remains because of the scarcity of oracle character images. To overcome this issue, we propose Diff- ... Full text CiteYou look from old classes: Towards accurate few shot class-incremental learning
Journal Article Pattern Recognition · April 1, 2026 Few-shot class incremental learning (FSCIL) is a common but difficult task that faces two challenges: catastrophic forgetting of old classes and insufficient learning of new classes with limited samples. Recent wisdom focuses on preventing catastrophic for ... Full text CiteEducation
Chinese University of Hong Kong (Hong Kong) ·
2004
Ph.D.