Prof. Huanhuan Chen, University of Science and Technology of China
Bio: Huanhuan Chen (IEEE Fellow), is a professor in School of Computer Science, University of Science & Technology of China (USTC), Hefei, China. He received the B.Sc. degree from USTC, Hefei, China, in 2004, and Ph.D. degree, sponsored by Dorothy Hodgkin Postgraduate Award, in computer science at the University of Birmingham, Birmingham, UK, in 2008. He worked in University of Birmingham and University of Leeds in the UK from 2008 to 2012, respectively. His PhD thesis "Diversity and Regularization in Neural Network Ensembles" has received 2011 IEEE Computational Intelligence Society Outstanding PhD Dissertation award (the only winner) and 2009 CPHC/British Computer Society Distinguished Dissertations Award (the runner up). His work “Probabilistic Classification Vector Machines” on Bayesian machine learning published in IEEE Transactions on Neural Networks, has been awarded as IEEE Transactions on Neural Networks Outstanding 2009 Paper Award (bestowed in 2012, and only one paper in 2009 receive this award). In 2015, Dr. Chen received the International Neural Network Society (INNS)Young Investigator Award in 2015 for his significant contributions in the field of Neural Networks.His research interests include computational intelligence, statistical machine learning, data fusion, neural networks, Bayesian inference and evolutionary computation, etc.In 2015, Dr. Chen received the International Neural Network Society (INNS)Young Investigator Award in 2015 for his significant contributions in the field of Neural Networks.His research interests include computational intelligence, statistical machine learning, data fusion, neural networks, Bayesian inference and evolutionary computation, etc.
Prof. Sinno Pan, The Chinese University of Hong Kong, China
Bio: Prof. Sinno Jialin Pan (Fellow, IEEE) is a Professor of Computer Science and Engineering at the Chinese University of Hong Kong (CUHK) in Hong Kong. Before joining CUHK, he was a Provost’s Chair Professor of Computer Science and Engineering at Nanyang Technological University (NTU) in Singapore. He received his Ph.D. in computer science from the Hong Kong University of Science and Technology (HKUST) in 2011. He was a scientist and Lab Head of text analytics with the Data Analytics Department at the Institute for Infocomm Research in Singapore from 2010 to 2014. After that, he joined NTU as a Nanyang Assistant Professor (university named assistant professor) in 2014 and was promoted to Associate Professor and Professor in 2019 and 2022, respectively. He was appointed as a Provost’s Chair in Computer Science and Engineering in 2019. He was named “AI 10 to Watch” by the IEEE Intelligent Systems magazine in 2018. He serves as an Associate Editor for IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), Artificial Intelligence (AIJ), and ACM Transactions on Intelligent Systems and Technology (TIST).
Prof. Kaizhu Huang, Duke Kunshan University, China
Bio: Kaizhu Huang works on trustworthy AI, machine learning, and pattern recognition. He is Tenured Full Professor and Director of Data Science Research Center, at Duke Kunshan University (DKU). Prof. Huang obtained his PhD degree from Chinese University of Hong Kong (CUHK). He worked in Fujitsu Research Centre, CUHK, University of Bristol, National Laboratory of Pattern Recognition, Chinese Academy of Sciences from 2004 to 2012. He was the recipient of the 2011 Asia Pacific Neural Network Society Young Researcher Award. He published more than 250 international conference papers including 130+ SCI indexed journal papers and 60+ CCF-A or IEEE/ACM transactions. He received best (runner-up) paper or book award about 10 times in major AI conferences. In particular, he has recently received the 2024 IEEE ICDM 10 Years High-Impact Paper Award. He acts as Editor-in-Chief of Elsevier CSSI and serves as associated editors/advisory board members in 6 international journals and book series (e.g. Pattern Recognition Journal, and Neural Network Journal). He was invited as keynote/tutorial speaker in more than 50 international conferences or workshops.
Speech Title: Learning with Robust Generalization: Challenges, Methods, and Outlook