Advisory Board and Editors Data Mining & Machine Learning
Chun Liang
Professor in Bioinformatics, Biology Department, Miami University, Ohio, USA
Pengcheng Liu
Pengcheng Liu is a member of IEEE, IEEE Robotics and Automation Society (RAS), IEEE Control Systems Society (CSS) and International Federation of Automatic Control (IFAC). He is also a member of the IEEE Technical Committee on Bio Robotics, Soft Robotics, Robot Learning, and Safety, Security and Rescue Robotics. Dr Liu is an Associate Editor of IEEE Access, PeerJ Computer Science, and he received the Global Peer Review Awards from Web of Science in 2019, and the Outstanding Contribution Awards from Elsevier in 2017. He has published over 70 papers on flagship journals and conferences. He was nominated as a regular Funding/Grants reviewer for EPSRC, NIHR and NSFC and he has been leading and involving in several research projects and grants, including EPSRC, Newton Fund, Innovate UK, Horizon 2020, Erasmus Mundus, FP7-PEOPLE, NSFC, etc. He serves as reviewers for over 30 flagship journals and conferences in robotics, AI and control. His research interests include robotics, machine learning, automatic control and optimization.
Gerton Lunter
My group is interested in investigating the processes of evolution and biology using computational methods. We apply machine learning methods (HMMs, Bayesian statistics, particle filters, deep learning) to large data sets to study for example human demographic history or non-coding functional elements in the genome.
Ana G Maguitman
Ana Gabriela Maguitman is a Principal Researcher at the National Council for Science and Technology (CONICET) of Argentina and an Associate Professor at the Department of Computer Science and Engineering of the Universidad Nacional del Sur (Argentina). She obtained her PhD in Computer Science at Indiana University (USA). Dr. Maguitman leads the Knowledge Management and Information Retrieval Research Group at Universidad Nacional del Sur. Her main research areas include Natural Language Processing, Machine Learning, and Information Retrieval.
Brendan P Malone
My research focus is in using quantitative methods to precisely understand how soils function and change- spatially, and through time.
I research methods for comprehensive digital soil mapping aiming to characterize soil both in the lateral and vertical dimensions.
I research methods for quantifying (and validating) measures of uncertainty for these comprehensive soil information systems.
I investigate innovative systems for soil measurement, which includes that associated with remote and proximal and soil sensing instrumentation. I have particular interest in infrared and x-ray spectroscopy.
Elena Marchiori
Elena Marchiori received a MSc in mathematics and a PhD in computer science from the University of Padua, Italy. She was employed at the Centre for Mathematics and Computer Science, Amsterdam and at the Leiden Institute of Advanced Computer Science. Since 2008 she is associate professor at the Radboud University Nijmegen. She published 100+ scientific papers on methods and applications in computer science. Her current research interests include machine learning methods and applications.
Johannes T Margraf
Johannes Margraf is a professor of theory and machine learning in physical chemistry at the University of Bayreuth. His group focuses on using and developing machine-learning and electronic structure methods to study chemical reactions and discover new functional materials. He obtained his PhD at the University of Erlangen, working with Timothy Clark and Dirk Guldi on the theoretical and experimental characterization of quantum dot solar cells. Subsequently he joined the group of Rodney Bartlett at the University of Florida working on method development in coupled cluster theory and single particle methods, and worked as a group leader at TU Munich and the Fritz Haber Institute in Berlin, in the theory department lead by Karsten Reuter.
Alessio Martino
Alessio Martino graduated summa cum laude in Communications Engineering at University of Rome "La Sapienza", Italy, 2016. From 2016 to 2019, he served as PhD Research Fellow in Information and Communications Technologies at the same University (Department of Information Engineering, Electronics and Telecommunications), with a final dissertation on pattern recognition techniques in non-metric domains. During his PhD, he also served as scientific collaborator with Consortium for Research in Automation and Telecommunication, Rome, Italy.
After obtaining the PhD, he was granted a 1-year Post Doctoral Research Fellowship at University of Rome "La Sapienza" and a 1-year Post Doctoral Research Fellowship at the Italian National Research Council (Institute of Cognitive Sciences and Technologies). Since February 2022, he is Assistant Professor of Computer Science at LUISS University.
His research interests include machine learning, computational intelligence and knowledge discovery. Currently he's focusing on large-scale machine learning, advanced pattern recognition systems, big data analysis, parallel and distributed computing, granular computing and complex systems modelling, in applications including bioinformatics and computational biology, natural language processing and energy distribution networks.
He serves as Editor for several journals and regularly serves as Technical Program Committee member for several international conferences. Alessio Martino is also a member of the IEEE.
Hamid Mcheick
Dr. Hamid Mcheick is a full professor in Computer Science department at the University of Québec at Chicoutimi, Canada. He has more than 25 years of experience in both academic and industrial areas. He has done his Ph.D. in Software Engineering and Distributed System in the University of Montreal, Canada. He is working on designing of adaption distributed, smart and connected software applications; designing healthcare frameworks; and designing smart Internet of Things architecture. He has supervised many post-doctorate, PhD, master, and bachelor students. He has nine book chapters, more than 60 research papers in international journals, and more than 150 research papers in international/national conferences and workshop proceedings to his credit. Dr. Mcheick has given many keynote speeches and tutorials in his research area, particularly in Healthcare systems, Architecture Pervasive and Ubiquitous Computing, Distributed Middleware Architectures, Software Connectors, Service-Oriented Computing, Internet of Things (IoT), Smart Architectural Frameworks, Mobile Edge Computing, Fog Computing, and Cloud Computing. Dr. Mcheick has gotten many grants from governments, industrials and academics. He is a chief in editor, chair, co-chair, reviewer, member in many organizations (such as IEEE, ACM, Springer, MDPI, Elsevier, Inderscience) around the world.
Gang Mei
Dr. Gang Mei is an Associate Professor in Scientific Computing in Engineering at China University of Geosciences (Beijing). He received his Ph.D degree in 2014 from the University of Freiburg in Germany. His main research interests are in the areas of Numerical Simulation and Computational Modeling, GPU Computing, Machine Learning, Data Mining, and Network Science and Applications. He is the IEEE Member, and has served as an Academic Editor for the journals IEEE Access, and PeerJ Computer Science.
Nageswara Rao Moparthi
Dr. M. Nageswara Rao is a Professor within the Dept. of Computer Science and Engineering, K L University, India. He has over 19 years of experience in the S/W industry and academia. Dr. M. Nageswara Rao has published over 20 articles in reputed international journals, written 2 books and filed 2 Indian patents. He is a reviewer for a number of SCI/SCIE journals, including IEEE Access and Journal of Big Data(JBD) Journal of Database Management, Cluster Computing , NHIB and Information Sciences; and Scopus journals, such as IJAIP, IJDS, CIT and IJECE. Dr. M. Nageswara Rao is also an associate TPC member for the following International conferences: ICACII-2019-Springer (India), ICCET-2020-IEEE/WOS (New Zealand), ITIoT/ICCCS 2020-Shanghai (China), JCICE-Sydney (Australia), BDET-2020-ACM Digital Library (Singapore) and ICCMA 2019-IEEE (TU Delft, Netherlands).
Dr. M. Nageswara Rao's research areas are listed below:
1.Data Mining
2. Data Analytics
3.Machine Learning
4. Software Engineering
5. Artificial Intelligence
Alejandro Moreo
Alejandro Moreo received a PhD in Computer Sciences and Information Technologies from the University of Granada in 2013. He is a tenured researcher at Istituto di Scienza e Tecnologie dell’Informazione "A. Faedo", which is part of the National Research Council (CNR). His research interests include learning to quantify, deep learning, and representation learning.