Advisory Board and Editors Data Mining & Machine Learning
Joram M Posma
Lecturer in Cancer Informatics at Imperial College London and Fellow at Health Data Research (HDR) UK. Fellow of the Higher Education Academy (FHEA) and Member of the Royal Society of Chemistry (MRSC).
Adam W Potter
Research Physiologist, US Army Research Institute of Environmental Medicine (USARIEM).
Part-time faculty, School of Science, Technology, Engineering, & Math (STEM), American Public University System (APUS).
Research portfolio spans across the applied sciences, from thermal manikin testing, to the cutting-edge of product development (computer-based decision aids, wireless communications, and wearable sensors). Current scientific work areas include: 1) individualized mathematical modeling of thermoregulatory responses to clothing, environment, activities, with the inclusion of components for rest and recovery, 2) studies of metabolic costs over complex terrain, 3) real-time assessments of ground reaction forces and energy demands during locomotion and load carriage, and 4) innovative approaches to data management and the application of mathematics in integrative physiology.
James B Procter
I studied Chemistry at The University of York, Computer Science at The University of Leeds, and obtained a PhD at the Australian National University. I worked on the comparison, classification and prediction of protein structure at ANU and in Germany at the University of Hamburg before joining the Jalview project in Dundee in 2004.
I co-founded the VIZBI conference in 2009, and joined PeerJ CS as Academic Editor in 2014. I serve on a variety of biological and computer science peer review panels and conference program committees. I'm interested in how we can do better science by creating better tools for data analysis and communication.
Michela Quadrini
Current research is focused on Artificial Intelligence, Bioinformatics, Formal methods and Languages for the modelling, analysis and verification of Distributed Systems.
Waqar Shahid Qureshi
Dr. Waqar Shahid Qureshi is an applied AI researcher and engineer with over 22 years of professional experience across academia, research, and industry. His interdisciplinary expertise lies in artificial intelligence, computer vision, robotics, and intelligent sensing systems, with a strong emphasis on real-world deployment in precision agriculture, consumer electronics, and civil infrastructure.
Hossein Rahmani
Hossein Rahmani received his B.Sc. degree in computer software engineering from the Isfahan University of Technology, Isfahan, Iran, in 2004, an M.Sc. degree in software engineering from Shahid Beheshti University, Tehran, Iran, in 2010, and a Ph.D. degree from The University of Western Australia, in 2016.
He has published several papers in top conferences and journals such as CVPR, ICCV, ECCV, and the IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE. He is currently an Associate Professor (Lecturer) with the School of Computing and Communications, Lancaster University. Before that, he was a Research Fellow at the School of Computer Science and Software Engineering, The University of Western Australia. His research interests include computer vision, action recognition, 3D shape analysis, and machine learning.
Sivaramakrishnan Rajaraman
Dr. Sivarama Krishnan Rajaraman is working as a research scientist at the National Library of Medicine (NLM), National Institutes of Health (NIH). Dr. Rajaraman received his Ph.D. in Information and Communication Engineering from Anna University, India. He is involved in projects that aim to apply computational sciences and engineering techniques toward advancing life science applications. These projects involve the use of medical images for aiding healthcare professionals in low-cost decision-making at the POC screening/diagnostics. He is a versatile researcher with expertise in machine learning, data science, biomedical image analysis, and computer vision. He has more than 15 years of experience in academia where he taught core and allied subjects in biomedical engineering. He has authored several national and international journal and conference publications in his area of expertise. Dr. Rajaraman is an Editorial Board member of the PLOS ONE, PeerJ Computer Science, MDPI Knowledge, and MDPI Electronics journals. He is reviewing manuscripts for more than 75 journals including those published by Nature, LANCET, IEEE, MDPI, Elsevier, and other conferences including CVPR, EMBS, CBMS, and MICCAI. Dr. Rajaraman is a Life Member of the Society of Photo-optical Instrumentation Engineers (SPIE), a regular member of the Institute of Electrical and Electronics Engineers (IEEE), IEEE Engineering in Medicine & Biology Society (EMBS), and the Biomedical Engineering Society (BMES).
Rommel T J Ramos
The Rommel Ramos Professor of Bioinformatics of Federal University of Para (Brazil) affiliated member of Brazilian Science Academy and CNPq Researcher (level 1-D). Since 2008 works with genome assembly and RNA-Seq analysis, he is the leader of the bioinformatic development group of the Biologic Engineering Laboratory in Park of Science and Technology (Pará/Brazil).
Ana Reyes-Menendez
PhD Cum-Laude and Extraordinary Award in Business Economics from Rey Juan Carlos University specialized in Neuromarketing and Digital Marketing. Post-doctoral Research stays in Universidade Portucalense, Universidade do Algarve and RCC at Harvard University.
Assistant Professor of the Department of Business at Rey Juan Carlos University. Research focused on Online Consumer Behavior, Information Science, Data Science for Business and Biometrics.
Mauro Rossi
Mauro Rossi is an expert on mapping, modeling and forecasting of landslides, floods and erosion processes in different geo-environmental and anthropic contexts. He has developed (i) new methodologies for statistical and deterministic analysis of the susceptibility and hazard posed by different geo-hydrological phenomena and for the estimation of their impacts, (ii) new statistical approaches to the definition of rainfall thresholds for triggering Landslides, (iii) early warning systems, (iv) approaches to the design optimal models for estimating landslide susceptibility and for the assessment of social risk posed by landslides and floods. He has also developed specific softwares for the landslide susceptibility modelling, for the landslide magnitude modelling and for the joint modeling of landslides and erosion processes in relation to different scenarios of geomorphological, climatic, vegetational and anthropic changes, in order to adequately characterize the hillslopes and the hydrological basins dynamics.
Ismaila Temitayo Sanusi
Ismaila Temitayo Sanusi earned his doctorate from the University of Eastern Finland. He specializes in computer science education, with a focus on democratizing Artificial Intelligence (AI) and Machine Learning (ML) for young learners and beginners. His research spans international contexts and involves co-designing innovative, constructionist technologies and learning materials for K–12 students. He develops AI tools and competency models to support curriculum integration, along with educator training resources that introduce students to AI early; helping build an AI-ready workforce and future technology creators. He also serves on editorial boards and as a special issue editor for journals in computing, technology, and education.
Rossano Schifanella
Rossano Schifanella is an associate professor of computer science at the University of Turin and a researcher at ISI Foundation, where he is a member of the Data Science for Social Impact and Sustainability group. His research embraces the creative energy of a range of disciplines across machine learning, urban science, computational social science, complex systems, and data visualization. He leverages data-driven approaches to model the behavior of (groups of) individuals and their interactions in space and time, aiming at understanding the interplay between online and offline social behavior. He is passionate about understanding the dynamics of complex phenomena in modern cities and building interactive web interfaces to explore urban spaces and access human knowledge through geography.