Advisory Board and Editors Data Mining & Machine Learning
Meng Wang
Dr. Meng Wang is a professor at the Hefei University of Technology, China. His current research interests include multimedia content analysis, search, mining, recommendation, and large-scale computing. He received the best paper awards successively from the 17th and 18th ACM International Conference on Multimedia. He is the recipient of ACM SIGMM Rising Star Award 2014.
Kezhi Wang
Kezhi Wang received his Ph.D. degree from the University of Warwick, U.K. He was a Senior Research Officer in University of Essex, U.K. Currently He is a Senior Lecturer with Department of Computer Science, Brunel University, U.K.
Wei Wang
Dr. Wei Wang is currently a Full Professor at the Department of Engineering Shenzhen MSU-BIT University, Shenzhen, China. Before this, he had been an associate professor with the School of Intelligent Systems, Sun Yat-sen University, China from 2019 to 2022. He had been the UM Macao Research Fellow at the University of Macau, Macau SAR. He received his Ph.D. in Software Engineering from Dalian University of Technology in 2018. His research interests include Psychophysiological Computing and Complex network He has authored/co-authored more than 100 scientific papers in top-tier international journals and conferences, including more than 50 scientific papers as the first/corresponding author, such as IEEE TITS, TNNLS, TBD, TCSS, TII, TETC, JBHI, IoT, ACM TALLIP, TKDD, WWW, and PKDD. He is the Leading Guest Editor of IEEE TCSS, JBHI, SETA, and the Guest Editor of ACM TOIT. He is a Program Chair of ISoIRS 2022, Track Chair of IEEE SmartIoT 2019&2020, TPC member of IEEE HealthCom 2020, and a regular reviewer of extensive top-tier field journals and conferences, such as IEEE TII, TBD, IoT, IJCAI, and WWW. He has an h-index of 26, a cumulative impact factor of 300+, and a total of 2500+ citations based on Google Scholar.
Shuo Wang
Shuo Wang is an Assistant Professor in the School of Computer Science, at the University of Birmingham, UK. Her research interests include data stream classification, class imbalance learning and ensemble learning approaches in machine learning, and their applications in social media analysis, software engineering and fault detection. Her work has been published in internationally renowned journals and conferences, such as IEEE Transactions on Knowledge and Data Engineering and International Joint Conference on Artificial Intelligence (IJCAI). In addition, she was a guest editor of Neurocomputing and Connection Science and the workshop organizer of IJCAI'17 and ECML/PKDD'21, '22. She is currently in the editorial board of International Journal of Computational Intelligence and Applications.
Shuihua Wang
Shuihua Wang received her B.S. Degree in information science and engineering from Southeast University in Nanjing, China, in 2008; the M. S. degree in Electrical Engineering from the City College of New York, USA in 2012, and the Ph. D degree in Electrical Engineering from Nanjing University, Nanjing, China, in 2017. She visited Kyushu Institute of Technology in 2017. From 2013 to 2018 she joined Nanjing Normal University, and worked as an assistant professor. From 2018-2019, she served in Loughborough University. She is now working as a research associate at the University of Leicester. Her research interests focus on Machine learning, Deep learning, biomedical image processing. She has published over 30 papers in peer-reviewed international journals and conferences in these research areas. She was serving as a professional reviewer for many well-reputed journals and conferences including IEEE Transactions on Neural Networks and Learning Systems, Neuron Computing, Pattern recognition, scientific reports, and so on. She is currently serving as Guest Editor-in-Chief of Multimedia Systems and Applications, Associate editor of Journal of Alzheimer’s Disease and IEEE Access. She is a member of the IEEE.
Jingzhe Wang
Jingzhe Wang received his Ph. D in Cartography and Geographic Information System from Xinjiang University, Urumqi, China, in 2019. He is now working as a research associate at MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area, Shenzhen University. His research interests focus on Earth observation and remote sensing, spectral modeling, quantitative estimation of soil properties, digital soil mapping, GIS, spatial analysis, and environmental sustainability. He has published over 60 papers in peer-reviewed international journals in these related research areas and has served as a reviewer for many journals and conferences including Remote Sensing of Environment, Ecological Indicators, Computers and Electronics in Agriculture.
Lei Wang
Professor of Geography at Louisiana State University. Research interests include Geocomputation, GeoAI, Remote Sensing of water, Spectroscopic analyses, and mapping flood hazards.
Easton R White
I am a quantitative marine ecologist who uses mathematical and statistical tools, coupled with experiments and field observations, to answer questions in ecology, conservation science, sustainability, and ecosystem management. Most of my work is focused on marine systems, especially fisheries and spatial planning. I am a new Assistant Professor in the Department of Biological Sciences at the University of New Hampshire. Prior to joining UNH, I was a research associate at the University of Vermont with the QuEST program, a NSF-funded PhD traineeship focused on quantitative skills, interdisciplinary work, as well as diversity and inclusion.
I currently conduct research on assessing the effectiveness of protected area networks, improving species monitoring programs, and modeling socio-ecological systems in the context of fisheries. My work centers on how environmental variability, in particular rare events (e.g., hurricanes, COVID-19 pandemic), affects ecosystems and those that depend on them. My current work is funded through a NSF grant focused on interdisciplinary approaches to study coupled natural-human systems with Madagascar fisheries as a case study.
Matthew D Wilson
Matthew (Matt) Wilson is a Professor in Spatial Information and Director of the Geospatial Research Institute Toi Hangarau at the University of Canterbury in Christchurch, New Zealand. He is a surface water hydrologist and geographical information scientist with specialisations including flood risk, surface water dynamics, water resources, remote sensing, numerical model development, uncertainty analysis and the assessment of the potential impacts of climate change. Previous research has included the assessment of the potential impacts of climate change on flood risk and water resources in the Caribbean and the analysis of surface water hydrodynamics on a 300 km reach of the Amazon River in Brazil. In New Zealand, his current research includes leading the uncertainty theme for a national scale flood risk assessment, the creation of a digital twin for flood resilience, and the creation of algorithms for processing of novel airborne GNSS reflectometry measurements for estimation of soil water content.
Zhijin Wu
I develop statistical methodology and software for the analysis of -omics data. I am particularly interested in the regulation of transcription: the molecular mechanism as well as its association with disease.
Keli Xiao
Dr. Keli Xiao is an Associate Professor in the College of Business at Stony Brook University. He received his Ph.D. from Rutgers University. Dr. Xiao’s research interests include business analytics, data mining, real estate/urban computing, economic bubbles and crises, and asset pricing. His research has appeared in many high-quality journals and conference proceedings, such as IEEE Transactions on Knowledge and Data Engineering (TKDE), Real Estate Economics, ACM Transactions on Knowledge Discovery from Data (TKDD), ACM Transactions on Management Information Systems (TMIS), ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), etc. He regularly serves as an SPC or PC of numerous prestigious conferences, such as AAAI, IJCAI, KDD, ICDM, SDM, CIKM, etc.. He is a senior member of the IEEE and the ACM.