Tailin Wu (http://tailin.org) is an assistant professor in the Department of Engineering at Westlake University. He received his Ph.D. from MIT, where his thesis focused on the intersection of AI, Physics and information theory, and did his postdoc in Stanford Computer Science, working with Professor Jure Leskovec. His research interests include developing machine learning methods for large-scale scientific simulations and design, and neuro-symbolic methods for scientific discovery, using tools of graph neural networks, generative models, and physics. His work has been published in top machine learning conferences and leading physics journals, and featured in MIT Technology Review. His work has been applied in applications of physics, materials, energy, and scientific discovery. He also serves as a reviewer for high-impact journals such as PNAS, Nature Communications, Nature Machine Intelligence, and Science Advances.

Wei Duan received his bachelor's degree from Tongji University and an M.Sc. in computer science from the University of Technology Sydney, where he is pursuing a PhD with the Australian Artificial Intelligence Institute (AAII). He has published several research papers in international journals and top conferences such as IEEE TNNLS and AAAI. His research interests include multiagent reinforcement learning and graph neural networks. Mr. Duan also served as a Reviewer for journals such as IEEE TNNLS, IEEE SMAC and KBS.

Dr. Tan is a Senior AI Expert in Merck KGaA and an adjunct senior researcher at Shanghai Artificial Research Institute (SARI). He received his Ph.D. from the Department of Computing, The Hong Kong Polytechnic University in 2016. His main research interests are machine learning and its applications, including AI for healthcare and life sciences. He has published more than 20 papers at leading conferences/journals. He also holds 5 Chinese patents and submits 3 US patents.

Yiqing is a CS Ph.D. Student of Johns Hopkins University. He obtained his bachelor degree from Shanghai Jiao Tong University. His research interests span the fields of medical image analysis, computer vision, and particularly, their interdisciplinary direction.

Feng Xie is currently an Associate Professor in the Department of Applied Statistics at Beijing Technology and Business University. Prior to joining BTBU, he earned his Ph.D. in the School of Computer Science at Guangdong University of Technology from 2017 to 2020. Following that, he conducted postdoctoral research in the Department of Probability and Statistics at Peking University from 2020 to 2022. During 2019-2020, he served as a visiting scholar in the Department of Philosophy at Carnegie Mellon University. His research interests lie in machine learning and causal inference, especially in causal discovery and causal representation learning.

Yixin Liu received the BS degree and MS degree from Beihang University, Beijing, China, in 2017 and 2020, respectively. He is currently working toward a Ph.D. degree in computer science with Department of Data Science and Artificial Intelligence (DSAI), Faculty of IT, Monash University, Australia. His research concentrates on data mining, machine learning, graph analytics, and anomaly detection. To date, he has published several research papers in top-tier journals and conferences, including NeurIPS, KDD, AAAI, Web Conference, WSDM, CIKM, IEEE TKDE, and IEEE TNNLS. He also serves as a reviewer/PC member for high-impact journals and conferences such as IEEE TKDE, IEEE TIP, IEEE TCYB, IEEE TNNLS, KDD, ICLR, AAAI, IJCAI, and SDM. He is a recipient of Google Ph.D. Fellowship in 2022.

Eli Chien is currently a Postdoc fellow at the Georgia Institute of Technology, working with Professor Pan Li. He obtained his Ph.D. from the University of Illinois, Urbana-Champaign, advised by Professor Olgica Milenkovic. His current research focus is privacy aspects of graph machine learning, including differential privacy and machine unlearning. His previous research focuses on designing better Graph Neural Networks with theoretical guarantees. His works are mainly published in top-tier machine learning, data mining, and information theory venues, including ICML, NeurIPS, ICLR, KDD, Transaction IT, TheWebConf, AISTATS, AAAI, etc.

Dongmian Zou is an Assistant Professor of Data Science in the Division of Natural and Applied Sciences, Duke Kunshan University. He is also affiliated with the the Zu Chongzhi Center for Mathematics and Computational Sciences (CMCS) and the Data Science Research Center (DSRC). He received the B.S. degree in Mathematics (First Honour) from the Chinese University of Hong Kong in 2012 and the Ph.D. degree in Applied Mathematics and Scientific Computation from the University of Maryland, College Park in 2017. From 2017 to 2020, he served as a post-doctorate researcher at the Institute for Mathematics and its Applications, and the School of Mathematics at the University of Minnesota, Twin Cities. His research is in the intersection of applied harmonic analysis, machine learning and signal processing. His current interest includes geometric deep learning, robustness, anomaly detection, and applications in e.g., communication, circuits and medical imaging.

Hoi-To Wai received his PhD degree from Arizona State University (ASU) in Electrical Engineering in Fall 2017, B. Eng. (with First Class Honor) and M. Phil. degrees in Electronic Engineering from The Chinese University of Hong Kong (CUHK) in 2010 and 2012, respectively. He is an Assistant Professor in the Department of Systems Engineering & Engineering Management at CUHK. He is also an Associate Editor for the IEEE Transactions on Signal and Information Processing over Networks. He has held research positions at ASU, UC Davis, Telecom ParisTech, Ecole Polytechnique, MIT. Hoi-To's research interests are in the broad area of signal processing, machine learning and distributed optimization, with a focus of their applications to network science. His dissertation has received the 2017's Dean's Dissertation Award from the Ira A. Fulton Schools of Engineering of ASU and he is a recipient of Best Student Paper Award at ICASSP 2018.

Dr. Minjie Wang is a Senior Applied Scientist in Amazon AI Shanghai Lab. He obtained his Ph.D. degree from New York University. His research focus is the interdisciplinary area of machine learning and system including building deep learning systems with high usability and performance, applying machine learning in system optimization. He serves as the reviewer or area chair of top-tier conferences including Neurips, ICML, ICLR, MLSys, LoG, etc. He is also an open-source enthusiast; founder and major contributor of many well-known open source projects such as Minerva, Apache MXNet, MinPy and Deep Graph Library (DGL). Among them, Minerva has been remarked as one of the "Modern AI Engine" by NVIDIA; Apache MXNet is the deep learning framework of choice at AWS; DGL is one of the most popular frameworks for developing and applying Graph Neural Networks technology.

Yuanhong Jiang receives Ph.D degree from Institute of Natural Sciences, Shanghai Jiao Tong University. His research interest lies broadly in the area of medical image processing, protein design, deep learning and graph neural networks. He recently researches on graph neural networks from spectral perspective.

Lu Bai is now a Full Professor in Central University of Finance and Economics, Beijing, China. He received the PhD degree from the University of York, UK. He was a recipient of the National Award for Outstanding Self-Financed Chinese Students Study Aboard by China Scholarship Council, in 2015, and the best/outstanding paper awards of the International Conferences ICIAP 2015 (Eduardo Caianello Best Student Paper Award), ICPR 2018 and IEEE IEEM 2019. He is now supported by the National Excellent Young Scientist Fund of NSFC. He was selected as one of the 2022 Baidu Global Top Chinese Young Scholars in Artificial Intelligence. He has published more than 100 journal and conference papers, and about 40 papers was published on IEEE TPAMI, IEEE TKDE, IEEE TNNLS, IEEE TCYB, PR, IEEE TIM, IEEE TITS, Information Fusion, ICML, IJCAI, ICDE, ECML-PKDD, ICDM, and CIKM (include 4 ESI Highly Cited Papers). His current research interests include graph-based pattern recognition and machine learning. He is currently a member of the editorial board (Associate Editor) of the journal Pattern Recognition (PR). As the Leading Guest Editor, he also organized the special issue of Graph-based Methods on Large-scale Financial and Business Data Analysis on the journal PR.

Ting Gao obtained her Ph.D. from Illinois Institute of Technology in 2015. She has worked as a senior data scientist and Machine learning engineer in the big data product development department of MZ and Twitter in the US. She has developed several state-of-the-art recommendation systems based on deep reinforcement learning and deep learning, as well as real-time bidding models for online learning of big data streams. Now she is with the Center for Mathematical Sciences and the School of Mathematics and Statistics at Huazhong University of Science and Technology, and her main research interests in clude the identification of non-Gaussian stochastic dynamical systems, prediction and optimal control of effective dynamics, and applications in information communication, financial mathematics and brain science. She has published 20+ papers in SIAM series journals, Physica D, AMM etc..

Kun Zhan, is a professor and doctoral supervisor at the School of Information Science and Engineering, Lanzhou University. He specializes in Graph learning and Multiview learning. He conducted research visits at Dalhousie University in Canada and the University of Technology Sydney in Australia in 2009 and 2017, respectively. Over the past five years, Zhan Kun has made significant contributions to the field, publishing 6 papers in CCF-A conferences and journals. As a first author or corresponding author, he has contributed to 5 in prestigious IEEE Trans. Two of his papers have been highly cited according to the Essential Science Indicators (ESI). In recognition of his scholarly impact, he has been included in the "Annual Impact" list of the top 2% global scientists for 2023, jointly released by Elsevier and Stanford University.

Cheng Cheng is currently an associate professor at School of Mathematics, Sun Yat-sen University. Before that, she was a postdoc supervised by Prof. Ingrid Daubechies at Duke University. Her research interests include sampling theory, phase retrieval, graph signal processing and distributed algorithms. Cheng’s research has been published on Applied and Computational Harmonic Analysis, Journal of Functional Analysis, IEEE Transaction on Signal Processing, IEEE Transaction on Signal and Information Processing over Networks, Signal Processing, Journal of Fourier Analysis and Applications, etc.