Eugenio José Fernández Vicente is a researcher at the University of Alcalá within the Department of Computer Languages and Systems , focusing on IT governance, tourism technology, and e-learning systems. His work spans practical implementations of ITIL, IT infrastructure management, and predictive analytics for tourism sectors. Research Pillars : IT governance frameworks (ITIL, COBIT, EFQM), tourism demand forecasting, e-learning platforms, and telecom security. Advising : Directed four doctoral theses on topics like hydrocarbon distribution optimization, IT outsourcing, and educational interoperability. Publications : Authored 4 journal articles, 17 collective work contributions, and 4 books, including El gobierno y la gestión de las TIC (2009).
Haim Avron is a Professor in the Department of Applied Mathematics at Tel Aviv University's School of Mathematical Sciences, where he has been employed since 2015. His research focuses on numerical computing, high-performance computing, and their applications in scientific computing and machine learning. Education: Completed PhD in Computer Science at Tel Aviv University under Prof. Sivan Toledo, followed by postdoctoral research at IBM T.J. Watson Research Center. Research interests: Foundations of numerical linear algebra and randomized algorithms Tensor-tensor algebra for multiway data representation High-performance computational methods for machine learning Optimization techniques for large-scale systems Recent publications demonstrate strong focus on tensor algebra, randomized numerical methods, and machine learning optimization, with applications ranging from quantum computing to deep learning architectures. Awards: SIAM Activity Group on Computational Science and Engineering Best Paper Prize 2025 Software contributions include development of numerical libraries such as libSkylark for matrix sketching and Blendenpik for least-squares problems.
Associate Professor RECEP BİNDAK is a faculty member at Gaziantep University's Faculty of Education, Department of Mathematics and Science Education. With a Doctorate in Mathematics from Dicle University (2004), he has built an extensive academic career spanning over two decades, including positions at Mardin Artuklu University and Gaziantep University's Technical Sciences Vocational School. His educational background includes a Doctorate (2000-2004) from Dicle University's Institute of Sciences in Mathematics, a Master's degree (1994-1997) from Yuzuncuyil University's Institute of Sciences in Mathematics, and a Bachelor's degree (1987-1991) in Mathematics Teaching from Dicle University's Faculty of Education. BINDAK's research focuses on mathematics education, statistics, and educational psychology, with particular emphasis on mathematical self-efficacy, problem-solving attitudes, measurement techniques, and quantitative research methods. His work bridges theoretical statistical concepts with practical educational applications, making significant contributions to both fields. He has published extensively on topics ranging from mathematical justification skills in middle school students to psychometric properties of Likert scales and teacher burnout predictors. His recent publications (2018-2024) reveal a strong trend toward interdisciplinary research combining mathematics education with psychological factors affecting student performance. The majority of his work centers on middle school mathematics education, examining variables like self-efficacy, problem-solving attitudes, and mathematical justification skills. He also maintains active research in statistical methodology, particularly in bootstrap methods and nonparametric techniques, demonstrating his dual expertise in both educational theory and quantitative analysis. TUBITAK Publication Incentive Award (2011) TUBITAK Publication Incentive Award (2012) Professor BINDAK has supervised 22 graduate theses (1 doctoral and 21 master's), reflecting his commitment to mentoring the next generation of researchers. His students have explored diverse topics within mathematics education, including mathematical connection self-efficacy, geometric justification skills, and the relationship between school climate and teacher burnout. His teaching portfolio spans doctoral, master's, undergraduate, and associate degree levels, with courses focused on statistics, probability, data analysis, and mathematical research methods. As evidenced by his extensive publication record and thesis supervision, Professor BINDAK maintains an active research laboratory focused on quantitative educational research, with particular attention to measurement theory, statistical methodology in education, and mathematics learning processes. His work continues to influence both educational practice and research methodology in Turkey's academic community.
Mohammadreza MOUSAVI-KALAN is an Assistant Professor of Statistics at CREST-ENSAI. Previously, he was a postdoctoral fellow in the Department of Statistics at Columbia University. He received his Ph.D. in Electrical Engineering from the University of Southern California (USC) and his B.Sc. from Sharif University of Technology. Dr. MOUSAVI-KALAN's research focuses on theoretical foundations at the intersection of statistics and distributed computing. His primary interests include statistical machine learning, transfer learning, optimization theory, and distributed computing systems. He investigates how to design efficient algorithms that can leverage knowledge across related tasks while providing rigorous theoretical guarantees for learning procedures. His work addresses fundamental questions about sample complexity, computational efficiency, and statistical performance in modern machine learning settings. His publication record reveals a clear research trajectory from foundational work on distributed optimization (2018-2019) toward specialized topics in transfer learning and statistical hypothesis testing (2020-2025). A consistent theme across his work is establishing theoretical limits (minimax bounds, rate analyses) for practical machine learning problems. His recent publications focus on outlier detection, Neyman-Pearson classification frameworks, and transfer learning theory, demonstrating evolution toward more specialized statistical learning problems with practical applications. Dr. MOUSAVI-KALAN has established strong collaborative ties with researchers at USC, including Mahdi Soltanolkotabi, Salman Avestimehr, and Songze Li. His most influential work includes the Lagrange coded computing framework for distributed systems, which addresses critical challenges in resiliency, security, and privacy. His research bridges theoretical computer science, statistical learning theory, and practical distributed systems challenges, with implications for secure and efficient large-scale machine learning applications.
Panagiotis Xenos is an Assistant Professor of Actuarial Science at the Department of Statistics and Actuarial Science, School of Economics, Business & International Studies, University of Piraeus. His academic career spans teaching, research, and participation in significant national and international research programs focused on insurance, health economics, and organizational efficiency. PhD from University of Piraeus Postgraduate studies at Brandeis University (USA) Postgraduate studies at Boston University (USA) Specialized workshop at York University (UK) Dr. Xenos specializes in Actuarial Science with a particular focus on Insurance Economics, Health Economics, and Productivity Analysis. His research examines the efficiency and productivity of public and private insurance and health institutions, methods of compensation for medical professions, morbidity risk management, social security system sustainability, and insurance portfolio solvency. His work bridges theoretical actuarial science with practical applications in healthcare and social security systems, particularly in the context of economic crises and austerity measures. His publication record demonstrates consistent scholarly output from 2013 through 2023, with a concentration on Greek healthcare and insurance systems but with broader implications for international contexts. The research shows a clear progression from technical actuarial methods toward more comprehensive analyses of healthcare systems, social security sustainability, and economic policy implications. His work frequently employs advanced quantitative methods including Data Envelopment Analysis, Stochastic Frontier Analysis, and Malmquist productivity indices to assess organizational efficiency in healthcare and insurance sectors. Dr. Xenos actively participates in international academic discourse, having presented his research at conferences across Europe, Asia, and Australia, including the World Health Organization conference in Barcelona (2015) on health system financing. He serves as a reviewer for scientific journals and is a member of relevant scientific societies. In addition to his research activities, Dr. Xenos maintains an active teaching role across multiple academic programs. At the undergraduate level, he teaches Introduction to Insurance, Business Insurance, and Personal Insurance. At the postgraduate level, he teaches Health Insurance in the Master's Degree Program in Actuarial Science and Risk Management, Contemporary Issues in Commercial and Insurance Law in the Interdepartmental Master's Program in Law & Economics, and Economic and Financial Management of Health Services in the Postgraduate Program in Healthcare Unit Management at the Hellenic Open University. His teaching reflects his research expertise, connecting theoretical frameworks with practical applications in insurance and healthcare management.
Patrick Kürschner is an Associate Professor for Numerical Mathematics and Linear Algebra at Leipzig University of Applied Sciences (HTWK Leipzig) . He previously held postdoctoral positions at the Max Planck Institute for Dynamics of Complex Technical Systems and KU Leuven's Kulak Kortrijk Campus. His work focuses on computational methods in systems and control theory. PhD in Mathematics (Otto-von-Guericke University, 2015) MSc in Mathematics (Chemnitz University of Technology, 2010) BSc in Financial Mathematics (Chemnitz University of Technology, 2008) His research interests span numerical linear algebra, matrix equations and functions, eigenvalue problems, preconditioning, model order reduction, tensor methods, and numerical algorithms for digital signal processing. He develops efficient computational techniques for large-scale systems in control theory and data science applications. Recent publications include advancements in low-rank ADI iteration, time-limited balanced truncation, inexact linear solvers, and tensor decompositions for polynomial systems. His work addresses challenges in computational neuroscience, mechanical simulations, and optimal control.
Alessandro Recla is a Research Fellow in the Decision Sciences Department at Bocconi University , affiliated with the School of Management through his role as SDA Fellow in Decision Sciences and Business Analytics. He teaches Statistics and Business Analytics courses across undergraduate, graduate, and Master's programs at Bocconi University (since 2006), Università Cattolica del Sacro Cuore (since 2021), Università dell’Insubria (since 2014), and Franklin University Switzerland (since 2022). Education : Economics, Statistics, and Social Sciences (Bocconi University, 2004) Research Focus : His work specializes in data analytics , marketing research , and quantitative methods for corporate decision-making, with applications in banking (churn prediction), tourism demand analysis, and dashboard design. Publications emphasize descriptive analytics , multivariate models , and time series analysis . Teaching Excellence : Recipient of Bocconi University's Excellence in Teaching Awards for both undergraduate (2018) and graduate (2020) programs. Courses include Data Visualization, Dynamic Forecasting, and Applied Statistics. Consulting & Tools : Applies statistical modeling to real-world business challenges, particularly in marketing decision processes and tourism analytics.
Mette Brandt Eriksen is a Research Librarian and Associate Professor at the University of Southern Denmark Library , Department of Education and Outreach, with affiliations to KI, OUH , and Cochrane Denmark in Odense. Her work bridges health sciences and information retrieval through methodological research in systematic reviews and evidence synthesis.
Dr.-Ing. Tim Brüdigam is a researcher affiliated with the Chair of Automatic Control Engineering at the Technical University of Munich . His work focuses on stochastic model predictive control (MPC) for systems with uncertainty, particularly in autonomous driving applications. He received the 2nd Prize IEEE ITSS Germany – Best PhD Dissertation Award for his research on safety and efficiency in MPC under uncertainty. 2024: Best PhD Dissertation Award (2nd prize), IEEE ITSS Germany His research bridges control theory , transportation systems , and uncertainty quantification , with recent publications addressing constraint tightening, collision avoidance, and distributed control strategies. Articles demonstrate a focus on stochastic MPC algorithms for autonomous vehicles, integrating machine learning and multi-granularity models to enhance safety and computational efficiency. Key scientific contributions include advancements in probabilistic constraint handling , event-based maneuver planning , and scenario-based uncertainty representation . His work has implications for urban automated driving , vehicle platooning , and high-speed autonomous racing .
Dr Ian Zajac serves as a Breakthrough Mental Health Research Foundation Fellow in Suicide Prevention at Flinders University's College of Education, Psychology and Social Work. With over 15 years of clinical and research experience, he bridges academic, clinical, and public health domains to address male mental health challenges. Education: Master of Psychology (Clinical), The University of Adelaide (2018) PhD (Psychology), The University of Adelaide (2011) Health Science (Honours), The University of Adelaide (2005) His research focuses on the psychological, social, and systemic drivers of male distress, with particular expertise in masculine identity, help-seeking behaviors, and externalizing forms of male depression. Zajac employs clinical trials, behavioral science methodologies, and lived experience-informed approaches to develop gender-responsive interventions. His work spans mental health treatment innovation, suicide prevention strategies, and psychological assessment tools tailored for men across the lifespan. Scientific Recognition: Breakthrough Mental Health Research Foundation Fellowship in Suicide Prevention Zajac actively supervises postgraduate students and early career clinicians while maintaining clinical practice as a psychologist. His research program emphasizes translation of findings into practical applications through national collaborations, with significant contributions to understanding male depression through instruments like the Male Depression Risk Scale. His h-index of 19 reflects substantial scholarly impact with 1186 citations.
Thu Nguyen is a Research Fellow in the Department of Holistic Systems at Simula Metropolitan, a leading Norwegian research institute specializing in data science and computing. Her work focuses on developing robust methodologies for handling missing data across critical domains including healthcare diagnostics and environmental monitoring systems. Her primary research interests include: Advanced missing data imputation techniques Machine learning explainability under incomplete data Healthcare informatics and medical data analysis Environmental data processing for air quality prediction Multimodal data integration challenges Analysis of her publication record reveals a strong trajectory in developing computationally efficient imputation algorithms with real-world applications. Key trends include the application of principal component analysis and generative models to healthcare datasets (sperm tracking, medical imaging), environmental monitoring (air quality prediction), and multimodal systems. Her work consistently bridges theoretical statistical foundations with practical implementation needs, emphasizing model transparency and performance validation. Dr. Nguyen actively collaborates within Simula Metropolitan's data science ecosystem, contributing to interdisciplinary projects that address fundamental challenges in data completeness and usability across scientific domains.
Shunsuke Horii is an Associate Professor at the Center for Data Science, Waseda University. His research spans information theory, coding theory, statistical learning theory, and data science applications. He actively collaborates with industry through initiatives like the Waseda Data Science Consortium. Education: Ph.D. in Science and Engineering from Waseda University (2009), Master's from Waseda University Graduate School of Science and Engineering (2004). Research Focus: Addresses causal effect estimation in data science using Bayesian decision theory, sparse modeling, and optimization techniques like ADMM and variational inference. Develops efficient algorithms for multiuser communication, matrix completion, and privacy-preserving distributed computing. Teaching: Instructs courses on statistics literacy, data science, and programming with Python/R across multiple academic quarters. Grants: Leads projects funded by Japan Society for the Promotion of Science, including causal inference frameworks, product recommendation systems, and business analytics. Publications: 21 papers with 61 Scopus citations, focusing on LP decoding, Bayesian hierarchical models, and statistical causal analysis.
Mohamed-Salem AHMED is a confirmed researcher within the ULR 2694-METRICS team (Public Health: epidemiology and quality of care) at the University of Lille. He also serves as a part-time lecturer at the UFR MIME (Mathematics, Computer Science, Management, Economics) and the Polytech Lille school, while working as a data scientist at the Alicante company. His research focuses on developing advanced statistical methodologies with applications in public health and epidemiological studies. His primary research interests include: Functional data analysis Spatial statistics Epidemiology Dr. AHMED's extensive publication record demonstrates a deep expertise in spatial scan statistics and functional data analysis. His work has evolved from foundational statistical methods to specialized applications in epidemiological cluster detection, with recent focus on multivariate functional data analysis and Bayesian modeling approaches. His development of the R Package HDSpatialScan shows commitment to making advanced statistical methods accessible to researchers. His dual affiliation with academia and industry allows him to bridge theoretical advances with practical implementations in public health surveillance and disease monitoring systems. The consistent output of high-quality research publications since 2014 demonstrates sustained scholarly productivity in his field.
Berta Aznar Martínez is an Assistant Professor in the Department of Psychology and Speech Therapy at the Faculty of Psychology, Educational Sciences and Sports at Blanquerna. She is actively affiliated with the Research Group in Pedagogy, Society and Innovation with the support of ICTs (PSITIC) and the Couple and Family Research Group (GRPF), contributing to cutting-edge research in sexual violence prevention, gender equality education, and child psychology. Her research focuses on critical areas including sexual violence prevention (particularly cyberviolence), gender equality education in schools, child psychology, attachment theory, and the impact of pornography consumption on young people. Dr. Aznar Martínez combines psychological assessment with educational intervention strategies, examining how educational programs can address gender-based violence and promote healthy relationships among youth. She has conducted systematic reviews on neglected topics in sexuality education and developed assessment tools for measuring parenting practices. Her scientific contributions include publications on the validation of psychological assessment tools, efficacy studies of gender equality training programs, and analyses of the relationship between child maltreatment, personality, and attachment styles. She has led significant projects including the EAS-TIC program to prevent sexual cyberviolence (as Principal Investigator), the Co-educational Diagnosis Service in Calafell municipality, and contributed to the European Commission-funded CONSENT project addressing online sexual content and healthy relationship development. Dr. Aznar Martínez collaborates extensively with researchers across Spain and internationally, particularly with colleagues at Blanquerna. Her work has significant implications for educational policy and practice, developing evidence-based interventions for gender equality education and sexual violence prevention that directly influence how schools implement gender equity practices.
Professor Jennifer Brown is a distinguished academic in the School of Mathematics and Statistics at the University of Canterbury, Faculty of Engineering, where she has been Professor since December 1996. She maintains an international reputation in survey design and environmental monitoring, frequently collaborating with both statisticians and biologists across various application areas. Her work spans environmental statistics, human health and wellbeing, and theoretical statistics. Education: PhD, University of Otago, Dunedin (1996) PG Diploma Science, Massey University, Palmerston North (1992) B.For.Sc (hons.), University of Canterbury, Christchurch (1986) Postgraduate Certificate in Strategic Leadership, University of Canterbury (2016) New Zealand Diploma in Health and Wellbeing, CareerForce, Wellington (2020) Professor Brown's research interests primarily focus on environmental statistics, survey design, and environmental monitoring. She has expertise in collaborative research across multiple disciplines, working with agencies in France, Italy, Spain, USA, Australia, and New Zealand. Her work extends to theoretical statistics, including regression trees and sampling theory, while maintaining strong connections to practical applications in environmental science and health. She has hosted international workshops on survey design and environmental monitoring and is committed to developing leadership in mathematical sciences. Her recent publications demonstrate a strong focus on spatial statistics and sampling methodologies, with particular emphasis on spatially balanced sampling techniques, adaptive sampling designs, and environmental monitoring applications. The research trends indicate a consistent progression toward more sophisticated spatial statistical methods with practical applications in environmental monitoring, resource assessment, and ecological studies, while also expanding into health-related statistical applications. Scientific Awards: Senior Fellow of the Higher Education Academy (UK) NZSA Campbell Award New Zealand Statistical Association Lifetime Member International Statistical Institute Elected member Professor Brown has supervised numerous graduate students across PhD, Masters, and undergraduate levels, with research topics spanning statistical learning, spatial statistics, healthcare applications, and sampling methods. She has been actively involved in research projects related to environmental monitoring, health and wellbeing, and statistical methodology development. Her teaching includes specialized courses in sampling methods and generalized linear models. She is Associate Director of the Biomathematics Research Centre and researcher in the NZ Health Ageing Research Group, maintaining strong connections between theoretical statistical development and practical applications in environmental science and health research.
