Advisory Board and Editors Data Science
Brett Trost
Dr. Brett Trost is a Scientist in the Molecular Medicine Program at the Hospital for Sick Children, Toronto, Canada. He is a computational biologist with a particular interest in human genetics.
Md Zia Uddin
DR. MD ZIA UDDIN received his bachelor’s degree in computer science and engineering from International Islamic University Chittagong, Bangladesh, in 2004. He then completed his MS Leading to PhD degree in Biomedical Engineering from Kyung Hee University, South Korea, in 2011. Currently, he is a senior research scientist
in the human-computer interaction group at SINTEF Digital, Oslo, Norway, where he continues contributing to his research field. His research primarily focuses on data and feature analysis, physical and mental healthcare, human-machine interaction, pattern recognition, deep learning, and artificial intelligence. His innovative work has been published in prestigious journals such as Information Fusion, IEEE Transactions on Consumer Electronics, and Future Generation Computer Systems, showcasing his peers’ high regard for his research. His research outcomes have earned him best/outstanding paper awards at several peer-reviewed international conferences. He received a Gold Medal Award in 2008 for academic excellence in his undergraduate studies. He was also awarded the Korean Government IT Scholarship and the Kyung Hee University President Scholarship from March 2007 to February 2011 to pursue his PhD. He has extensive teaching experience, having taught more than 20 computer science-related courses at various academic levels, from bachelor’s to PhD, and supervised many students’ research works at these levels as well. He is a senior member of IEEE. He has been editors any several prestigious journals such as PLOS One, Sensors, IEEE Access, and the International Journal of Computers and
Applications, Frontiers in Human Neuroscience, and a keynote speaker at various international conferences. Dr. Zia has over 170 research publications (around half as the leading author), including international journals, conferences, book chapters, and single-authored books. His Google Scholar citations are more than 6000. He has led work packages and tasks in many national and international research projects. His significant contributions
have earned him recognition in the World’s Top 2% Scientists (career-long and single-year-based), a list by Stanford University and Elsevier BV.
Leonilde Varela
The main research interests of Leonilde Varela rely on the Manufacturing Management domain: Production Planning, Control and Optimization and in Collaborative Paradigms, Networks and Decision Making Models, Methods and Tools, and Systems, Web Applications and Services for supporting Engineering and Production Management. She focuses on exploring international scientific collaborations, mainly in terms of joint publications, projects, and special issues proposals, with colleagues from several institutions. She is an active member in the organizing and scientific committees of several internationa conferences and integrates several research networks and organizations, such as: Euro Working Group of Decision Support Systems (EWG-DSS); Institute of Electrical and Electronics Engineers (IEEE); Industrial Engineering Network (IE Network); Institute of Industrial and Systems Engineers (IISE); Machine Intelligence Research Labs, Scientific Network for Innovation and Research Excellence (MirLabs).
Shravan Vasishth
Professor of Psycholinguistics at the Department of Linguistics, University of Potsdam, Germany. Specialization in computational models of sentence comprehension; sentence processing in aphasia; working memory and language comprehension; Bayesian statistics.
Sebastian Ventura
Sebastián Ventura is professor of Computing Science and Artificial Intelligence at the University of Córdoba. His teaching is devoted to computer programming, machine learning and data mining in undergraduate and graduate studies. His research labor is developed as head of the "Knowledge Discovery and Intelligent Systems" (KDIS) research group, and it is focused on machine learning, data mining, big data, computational intelligence and its applications.
Chaman Verma
Dr. Chaman Verma is an Assistant Professor at the Department of Media and Educational Informatics, Faculty of Informatics, Eötvös Loránd University. He is also the project leader and chief researcher of his project sponsored by National Research, Development and Innovation (NRDI) Hungary. He also won a young educator scholarship for novel research sponsored by the EKÖP, NRDI Fund, and the Hungarian Government.
He pursued a post-doctorate at the Faculty of Informatics, Eötvös Loránd University, Budapest, Hungary, sponsored by UNKP, MIT (Ministry of Innovation and Technology), the National Research, Development and Innovation (NRDI) Fund, and the Hungarian Government. He received a Ph.D. in informatics from the Doctoral School of Informatics, Eötvös Loránd University, Budapest, Hungary, with the Stipendium Hungaricum Scholarship funded by the Tempus Public Foundation, Government of Hungary. During his Ph.D., he won the EFOP Scholarship, co-founded by the European Union Social Fund and the Government of Hungary, as a professional research assistant in a real-time system from 2018 to 2021. He also received the Stipendium Hungaricum Dissertation Scholarship of Tempus Public Foundation, Government of Hungary, from 2021 to 2022.
He has been awarded several Erasmus Scholarships for conducting international research and academic collaboration with European and non-European universities. He received the best scientific publication award from the Faculty of Informatics, Eötvös Loránd University, Budapest, Hungary, In the years 2021-2024. He has also been awarded the ÚNKP scholarship for research by the Ministry of Innovation and Technology and the National Research, Development and Innovation (NRDIO) Fund, Government of Hungary, 2021-2023.
He has around ten years of experience in teaching and industry. He has over 150 scientific publications in the IEEE, Elsevier, Springer, IOP Science, Walter de Gruyter and MDPI. His research interests include data analytics, feature engineering, real-time systems, and educational informatics. He is a life member of ISTE, New Delhi, India. He is a member of the editorial board and a reviewer of various international journals and scientific conferences. He was the leading guest editor of the special issue Advancement in Machine Learning and Applications in Mathematics, IF- 2.25, MDPI, Basel, Switzerland, in 2022. He was also a guest editor in two Springer journals. He is a co-editor in the series of conference proceedings of ICRIC-2021-24 published by Springer, Singapore. He reviews many scientific journals, including IEEE, Springer, Elsevier, Wiley, and MDPI. He has Scopus citations of 1603 with an H-index of 24. He has Web of Science citations of 355 with an H-index of 13.
Shibiao Wan
Dr. Shibiao Wan is currently an Assistant Professor in the Department of Genetics, Cell Biology and Anatomy, and the Co-Director for the Bioinformatics and Systems Biology (BISB) PhD Program at University of Nebraska Medical Center (UNMC). He is also an Assistant Professor (courtesy) in the Department of Biostatistics at UNMC.
With more than 15 years of experience in machine learning, bioinformatics, and computational biology, Dr. Wan has published >60 articles in top-tiered journals such as Genome Research, Nature Communications, Science Advances, Circulation Research, Briefings in Bioinformatics, and Bioinformatics. Dr. Wan is the Editor-in-Chief for Current Proteomics, and an Associate Editor/Academic Editor/Editorial Board Member for a series of prestigious journals such as Briefings in Functional Genomics, Heliyon, BMC Bioinformatics, International Journal of Microbiology, PeerJ Computer Science, BioMed Research International, and Computational and Mathematical Methods, and a guest associate editor for multiple high-impact journals.
He is a Scientific Program Committee (SPC) member for American Medical Informatics Association (AMIA) Annual Symposium and a Technical Program Committee (TPC) member for >20 machine learning related international conferences including IEEE ICTAI. Dr. Wan is also a reviewer for >70 prestigious journals including Nature Biotechnology, Nature Methods, Nature Communications, Nature Computational Science, Science Advances, Nucleic Acids Research, Advanced Science, Cancer Research, Genome Biology, and Genome Medicine. Dr. Wan has received a number of accolades including the Springer Nature Editor of Distinction Award in 2025 by Springer Nature, the New Investigator Award in 2024 by UNMC, the FIRST Award in 2023 by Nebraska EPSCoR, the Outstanding Young Alumni Award in 2022 by HK PolyU as well as the Global Peer Review Awards (top 1%) in “Cross-Field” and “Biology and Biochemistry” in 2019 by Clarivate. Dr. Wan is a member of AACR, AMIA, ISCB and ACM and an IEEE Senior Member.
Jingbo Wang
Prof Wang's research spans several disciplines including quantum dynamics theory, quantum computation and information, atomic physics, and computational science. She has published extensively, including a recent book published by Springer, four book chapters, and numerous journal papers. Prof Wang currently leads the quantum dynamics and computation group at The University of Western Australia. She and her research team have developed advanced numerical techniques to solve problems in both quantum and classical domain.
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.
Dapeng Wang
Dr Dapeng Wang is a Senior Bioinformatician in Integrative Analysis at the COMBAT consortium at the University of Oxford using multi-omics techniques in combination with the cutting-edge bioinformatic approaches and statistical methods to explore the pathogenesis of COVID-19 and stratification of patients as well as inform the treatment strategy based on genomics information.
Dr Wang received a bachelor’s degree in mathematics from the Shandong University in 2006 and obtained a PhD degree in bioinformatics from the Beijing Institute of Genomics of the Chinese Academy of Sciences in 2011. After his graduation, he continued to conduct research at the same institute from 2011 to 2014 and afterwards moved to the UK to take up various roles at the Cancer Institute at the University College London (2014-2016), the Department of Plant Sciences at the University of Oxford (2016-2018) and the LeedsOmics at the University of Leeds (2018-2020).
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.
Eric J Ward
I’m a statistician / quantitative ecologist at the Northwest Fisheries Science Center (NOAA) in Seattle and an affiliate professor at the School of Aquatic and Fishery Sciences (SAFS) at the University of Washington. I work on a wide range of statistical problems – population dynamics, extinction risk, conservation genetics, fisheries stock assessment, reproductive success studies, etc. Most of the species I study are fish, but I also work with data from marine mammals, seabirds, and turtles. Much of my recent modeling interests have been pursuing applications of multivariate state-space time series and spatio-temporal models, isotope mixing models, and Bayesian model selection techniques.