Educational Datamining Professors and Supervisors

Renáta Benda ProkeinováView profile

Associate Professor

Slovak University of Agriculture in NitraSlovakia

Renáta Benda Prokeinová serves as Associate Professor and Head of the Department of Statistics and Operations Research at the Faculty of Economics and Management, Slovak University of Agriculture. She maintains an active role in academic governance, currently serving on the Scientific Council of the Faculty of Economics and Management and the Study Programme Committee for Applied Economy and Data Analysis. Her educational background includes a Doctoral degree in Economics and Business Management with focus on Management in Agriculture (2008) and an Engineering degree (2005). Her research centers on consumer behavior modeling, particularly related to food consumption patterns in Slovakia, sustainable consumption practices, and econometric analysis of demand systems. Dr. Benda Prokeinová's scholarly work demonstrates consistent focus on quantifying sustainable consumption through statistical modeling and analyzing food purchasing behaviors. Her recent publication examines factors influencing organic food purchases among Slovak consumers, highlighting the interplay between quality perceptions and promotional activities. She actively supervises numerous graduate students across various research topics related to consumer behavior and economic modeling, with 68 theses supervised to date. Her grant portfolio includes significant projects such as 'Datamining analysis of public procurement in the public sector' as principal investigator and 'Big Data Mining & Analytics for Academic Operational Excellence' as coordinator. Dr. Benda Prokeinová teaches advanced courses including Data Mining, Econometric Modelling, Forecasting Methods, and Support Systems for Statistical Analyses, contributing to both undergraduate and postgraduate education at the university.

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Saulius PreidysView profile

Assistant Professor

Vilnius UniversityLithuania

Roles & Affiliations: Assistant Professor at the Department of Organizational Information and Communication Research, Faculty of Communication, Vilnius University. Active in research projects, academic conferences, and editorial roles. Education: PhD in Mathematics and Informatics (2012), Vilnius University, Thesis: 'The applications of datamining methods to personalized learning environments.' MA in Informatics (1995), Vilnius Pedagogical University. Research Interests: Focus on e-learning methodologies, big data processing, educational technology, and business intelligence. Explores applications of machine learning in finance and personalized learning systems. Publications & Projects: Over 15 articles in peer-reviewed journals and conferences (e.g., Applied Sciences, Mykolas Romeris University Proceedings). Led the EU-funded project 'Development and Introduction of Multilingual Teacher Education Programmes in Georgia and Ukraine (DIMTEGU)' (2012–2016). Active in conference organizing and editorial work. Grants & Memberships: Participated in EU-funded projects. Member of the National Association of Distance Education (NADE), Lithuanian Association of Distance and e-Learning (LieDM), and Lithuanian Computer Society (LIKS). Labs & Teams: Involved in research groups focused on educational technology, data analytics, and digital transformation initiatives within the Faculty of Communication.

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Victoria Blanes-VidalView profile

Professor

University of Southern DenmarkDenmark

Victoria Blanes-Vidal is a Professor at The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, specializing in Applied AI and Data Science. Her research bridges artificial intelligence with medical applications, particularly in cancer screening, diabetes complications, and environmental epidemiology. With over 80 research outputs and significant research metrics (3320 citations, h-index 30), she maintains an active presence in both academic and clinical research domains. Her research interests span Data Science, Biostatistics, Epidemiology, Machine Learning, and AI applications in healthcare, with specific focus on Cardiovascular diseases, Diabetes, Cancer screening (prostate, breast, colorectal, cervical), Emotions recognition, Liver diseases, Preeclampsia, and Respiratory health. She investigates Screening methodologies, Early detection techniques, and Prediction models within Medical Informatics and Mobile Health frameworks, extending to Environmental Epidemiology and Health Risk Assessment. Analysis of her recent publications reveals a strong trend toward applying AI in medical diagnostics, particularly in gastrointestinal cancer screening using capsule endoscopy, cervical cancer detection through colposcopic image analysis, and non-invasive diabetes complication detection through facial analysis and eye tracking. Her work consistently focuses on developing explainable AI models that can be integrated into clinical pathways while maintaining diagnostic accuracy. Professor Blanes-Vidal serves as a peer reviewer for prestigious journals including PLOS ONE and Scientific Reports, demonstrating her standing in the academic community. She has been involved in editorial work since 2022 and maintains membership in research networks such as the Polytechnical University of Valencia since 2008. Her teaching portfolio includes courses in Statistical Data Analysis, Data Science, and Datamining, reflecting her commitment to educating the next generation of data scientists. She has supervised research on topics including "Public perception of environmental quality: Improving quantitative analysis of statistical data in epidemiological studies." Her work has received significant media attention, with 11 press/media mentions including coverage in Danish media about AI applications for detecting life-threatening liver disease.

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Dries BenoitView profile

Associate Professor

Ghent UniversityBelgium

Dries Benoit is an Associate Professor in Data Analytics at Ghent University, specializing in Bayesian Statistics and its applications in business engineering. He teaches advanced courses in Bayesian Statistics, Statistical Modeling & Datamining, and Pricing & Revenue Management. Additionally, he serves as a visiting professor at Université de Namur and IESEG School of Management, where he contributes cross-institutional expertise in statistical modeling. Primary Affiliation : Ghent University Secondary Affiliations : Université de Namur, IESEG School of Management Research Focus : Dries Benoit's work bridges methodological advancements in Bayesian statistics with real-world applications across business domains. Core areas include marketing modeling, customer relationship management (CRM), learning analytics, and operations research. His interdisciplinary collaborations extend to medicine, energy, and education sectors, leveraging data science to solve complex problems in churn prediction, demand forecasting, and multimodal analysis. Publications & Research Trends : His recent publications highlight: Machine learning techniques for question difficulty estimation Uncertainty quantification in industrial AI Transformer-based models for ordinal regression Churn prediction in business-to-business contexts Deep learning applications in cultural analytics Ensemble methods for robust classification These works emphasize interpretable modeling, cross-domain adaptability, and balancing technical precision with business strategy applications.

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Julia MemarView profile

Lecturer

University of Technology SydneyAustralia

Julia Memar is a Lecturer in the School of Mathematical and Physical Sciences at the University of Technology Sydney (UTS). She holds a Master's in Applied Mathematics from Moscow State University (Russian Federation) and a PhD in Operations Research from UTS. Her research focuses on optimization algorithms for scheduling problems, flow shop systems, Lagrangian relaxation, complexity theory, and stochastic scheduling in underground mining. She has been teaching foundational mathematics courses at UTS for over a decade, including developing the Quantitative Skills for Science subject (2017–2020) and running UTS Mathematics Bridging courses. Julia's funded research includes two projects with Datamine Australia Pty Ltd: 'Uncertainty Modelling in Underground Mine Planning and Scheduling' (2022–2023) and its continuation (2023–2024). She leads the Mathematics and Science Study Centre at UTS, providing academic support in introductory quantitative subjects. Her teaching roles include subject coordination and lecturing for courses such as Introduction to Optimization, Foundation Mathematics, and Mathematical Modelling. Her publications span algorithmic optimization, scheduling theory, and education-focused strategies. Notable areas include flow shop scheduling with job-dependent buffers, mastery learning in STEM education, and computational approaches to discrete optimization.

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WONG Lim SoonView profile

Professor

National University of SingaporeSingapore

WONG Lim Soon is the KITHCT Chair Professor at the School of Computing , National University of Singapore (NUS). He also served as Deputy Dean of the NUS Graduate School and currently directs the Integrative Sciences and Engineering Programme . His career spans significant contributions to database theory, computational biology, and knowledge discovery technologies. Education: B.Sc. (Engineering) in Computing Science, First Class Honours, Imperial College London Ph.D. in Computer & Information Science, University of Pennsylvania His research interests intersect computational biology , database theory , and knowledge discovery . He focuses on enhancing proteomic profile analysis, addressing confounders in omics data, predicting transcription factor interactions, and rethinking synchronized iteration in programming languages. His work aims to improve biomarker reproducibility in proteomics and design robust statistical methods for omics analysis. His recent publications highlight advancements in genomic data processing , proteomics , and database algorithms . Key themes include non-equijoin efficiency , batch effect normalization , and protein family prediction in low-similarity regimes. Scientific Awards & Honours: Fellow of the ACM, 2013 ICDT 2014 Test of Time Award (with Peter Buneman and Val Tannen) FEER Asian Innovation Gold Award, 2003 (with Allen Yeoh, Huiqing Liu, and Jinyan Li) Singapore Youth Award Medal of Commendation, 2006 Limsoon co-founded Molecular Connections in India and has chaired the company for over a decade, growing it into a 2,000-person organization. His research is supported by grants from Singapore’s National Research Foundation and Ministry of Education Academic Research Fund (Tier-1 and Tier-2). He also develops open-source tools like Synchrony DBModel and GMQL implementations in Scala/Python.

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Matthijs WarrensView profile

Adjunct Professor

University of GroningenNetherlands

Prof. dr. Matthijs Warrens is an Adjunct Professor of Educational Sciences at the Faculty of Behavioural and Social Sciences, University of Groningen. He serves as Director of Education for the Pedagogical and Educational Sciences department and holds roles such as Associate Editor of the Journal of Classification and member of the IFCS Benchmarking Task Force. His research focuses on educational transitions, teacher expectations, statistical methodologies, and educational datamining. Key expertise areas include school career trajectories, educational choice dynamics, and the role of standardized assessments. He leads projects on shadow education effects, teacher workload, and inclusion strategies for students with special needs. Recent work addresses disparities in educational attainment and policy impacts on school advising systems. Prof. Warrens collaborates with researchers like Hanke Korpershoek and Anke de Boer, advising PhD candidates across multiple educational research topics. His methodological contributions span inter-rater reliability metrics and cluster analysis benchmarking, published widely in statistical and educational journals. Current initiatives include evaluating the Success for All literacy program and investigating symbiosis models in special education. His work bridges educational practice and theory through rigorous quantitative analyses addressing equity, policy effectiveness, and pedagogical innovation.

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