Prof. Seifedine Kadry, Noroff University College, Norway (IET Fellow; IETE Fellow; IACSIT Fellow)
Experience: Professor Seifedine Kadry has a Bachelor degree in 1999 from Lebanese University, MS degree in 2002 from Reims University (France) and EPFL (Lausanne), PhD in 2007 from Blaise Pascal University (France), HDR degree in 2017 from Rouen University. At present his research focuses on Data Science, education using technology, system prognostics, stochastic systems, and applied mathematics. He is an ABET program evaluator for computing, and ABET program evaluator for Engineering Tech. He is a Fellow of IET, Fellow of IETE, and Fellow of IACSIT. He is a distinguish speaker of IEEE Computer Society.
Prof. Yanchun Zhang，Guangzhou University/Victoria University，China
Experience: Yanchun Zhang is currently Professor at Guangzhou University and Chief Scientist at New Cyber Research Department of Pengcheng Laboratory, China. He is also Emeritus Professor at Victoria University.Dr Zhang obtained a PhD degree in Computer Science from The University of Queensland in 1991. His research interests include databases, data mining, web services and e-health. He has published over 400 research papers in these areas, and supervised over 40 PhDs and post doctors in completion. Dr. Zhang is a founding editor and editor-in-chief of World Wide Web Journal (Springer) and Health Information Science and Systems Journal (Springer). He speaks regularly at international conferences in the areas of data engineering / data science and health informatics. He has served as an expert panel member for various international funding agencies including National Natural Science Foundation of China, Australian Research Council, the Royal Society of New Zealand’s Marsden Fund, Medical Research Council of United Kingdom and NHMRC of Australia on Built Environment and Prevention Research.
Prof. Chun-Wei Lin（Jerry）, Western Norway University of Applied Sciences, Norway (IET Fellow; ACM Distinguished Member)
Experience:Jerry Chun-Wei Lin received his Ph.D. from the Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan in 2010. He is currently a full Professor with the Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, Bergen, Norway. He has published more than 500+ research articles in refereed journals (with 60+ ACM/IEEE transactions journals) and international conferences (IEEE ICDE, IEEE ICDM, PKDD, PAKDD), 16 edited books, as well as 33 patents (held and filed, 3 US patents). His research interests include data mining and analytics, natural language processing (NLP), soft computing, IoTs, bioinformatics, artificial intelligence/machine learning, and privacy preserving and security technologies. He is the Editor-in-Chief of the International Journal of Data Science and Pattern Recognition, the Associate Editor for IEEE TNNLS, IEEE TCYB, IEEE TDSC, INS, JIT, AIHC, IJIMAI, HCIS, IDA, PlosOne, IEEE Access, and the Guest Editor for several IEEE/ACM journals such as IEEE TFS, IEEE TII, IEEE TIST, IEEE JBHI, ACM TMIS, ACM TOIT, ACM TALLIP, and ACM JDIQ. He has recognized as the most cited Chinese Researcher respectively in 2018, 2019, 2020, and 2021 by Scopus/Elsevier. He is the Fellow of IET (FIET), ACM Distinguished Member (Scientist), and IEEE Senior Member.
Title：Utility-Oriented Techniques, Modeling, and Analytics
Abstract: As a large amount of data is collected daily from individuals, businesses, and other organizations or applications, various algorithms have been developed to identify interesting and useful patterns in data that meet a set of requirements specified by a user. The main purpose of data analysis and data mining is to find new, potentially useful patterns that can be used in real-world applications. For example, analyzing customer transactions in a retail store can reveal interesting patterns in customer buying behavior that can then be used for decision-making. In recent years, the demand for utility-oriented pattern mining and analytics has increased because it can discover more useful and interesting information than basic binary-based pattern mining approaches, which have been used in many domains and applications, e.g., cross-marketing, e-commerce, finance, medical and biomedical applications. In this talk, I will first highlight the benefits of using the utility-oriented pattern mining and analytics compared to the past studies (e.g., association rule/frequent itemset mining). I will then provide a general overview of the state of the art in utility-oriented pattern mining and analytic techniques according to three main categories (i.e., data level, constraint level, and application level). Several techniques and modeling on different aspects (levels) of utility-oriented pattern mining will be presented and reviewed.
Prof. Dong Xu, University of Missouri-Columbia, USA (AAAS Fellow; AIMBE Fellow)
Experience:Dong Xu is Curators’ Distinguished Professor in the Department of Electrical Engineering and Computer Science, with appointments in the Christopher S. Bond Life Sciences Center and the Informatics Institute at the University of Missouri-Columbia. He obtained his Ph.D. from the University of Illinois, Urbana-Champaign in 1995 and did two years of postdoctoral work at the US National Cancer Institute. He was a Staff Scientist at Oak Ridge National Laboratory until 2003 before joining the University of Missouri, where he served as Department Chair of Computer Science during 2007-2016 and Director of Information Technology Program during 2017-2020. Over the past 30 years, he has conducted research in many areas of computational biology and bioinformatics, including single-cell data analysis, protein structure prediction and modeling, protein post-translational modifications, protein localization prediction, computational systems biology, biological information systems, and bioinformatics applications in human, microbes, and plants. His research since 2012 has focused on the interface between bioinformatics and deep learning. He has published more than 400 papers with more than 21,000 citations and an H-index of 74, according to Google Scholar. He was elected to the rank of American Association for the Advancement of Science (AAAS) Fellow in 2015 and American Institute for Medical and Biological Engineering (AIMBE) Fellow in 2020.
Title：Neural relational inference from molecular dynamics simulations of proteins
Abstract: Molecular dynamics (MD) simulation provides a powerful computational approach to studying proteins. However, current MD simulations cannot reach the time scales of most biological processes. The advent of deep learning made it possible to evaluate spatially short and long-range communications from MD trajectories to understand protein dynamics. For this purpose, we adapted a neural relational inference model using an encoder-decoder graph neural network architecture to infer latent interactions between residues. The model can predict free energy changes upon mutations more accurately than other methods. We applied our method to study protein allostery, a biological process facilitated by spatially long-range intra-protein communication, whereby ligand binding or amino acid mutation at a distant site affects the active site remotely. This model successfully learned the long-range interactions and pathways that mediate the allosteric communications between remote sites in the Pin1, SOD1, and MEK1 systems. Furthermore, it can discover allosteric patterns in early MD simulation trajectories before significant conformational changes.
Prof. Xingyuan Wang, Dalian Maritime University, China
Experience: Xingyuan Wang received the B.S. degree in applied physics from Tianjin University, Tianjin, China, in 1987, the M.S. degree in optics from Tianjin University, in 1992, and the Ph.D. degree in computer software and theory from Northeastern University, Shenyang, China, in 1999. From 1999 to 2001, he was a post-doctoral fellow in automation in Northeastern University. Currently he has been a Second-level Professor with the School of Information Science and Technology, Dalian Maritime University, Dalian, China. He has published 6 academic monographs and more than 600 SCI papers, with a total citation 17000 and an H-index 62 (Web of ScienceTM), 6 papers and 28 papers are respectively selected as the hot papers and highly cited papers of the ESI. His research interests include chaos, fractal, and complex network theory and application research. He is ranked 2512 in the world's top 100,000 scientists (ranked 180 in China). Prof. Wang was in the top 2% of scientists 2020 in the world - China (Top 200 for Science Impact 2019), was a highly cited researcher worldwide from 2018 to 2021. He has applied for 27 invention patents, and has authorized 18 invention patents (15 in international and 3 in China). He won one first prize of Natural Science of Liaoning Province (the only complete person), and one second prize of Natural Science of Ministry of Education (the first complete person).
Copyright© EITCE 2022
2022 6th International Conference on Electronic Information Technology and Computer Engineering(EITCE 2022) http://eitce.org/