Eric Järpe is a Senior Lecturer at the School of Information Technology, Halmstad University, Sweden, specializing in the convergence of cryptography, steganography, and machine learning for real-world applications. His research focuses on: Advanced steganography techniques across unconventional domains (music, vehicles, smart homes) Machine learning applications for healthcare monitoring and smart sensing Statistical modeling for anomaly detection and sensor data analysis Synthetic data generation to enhance real-world dataset limitations Järpe's work bridges theoretical statistics with practical security and health informatics, particularly addressing challenges in elderly care systems and vehicular networks through innovative data-driven approaches. Publication trends reveal a clear evolution from foundational statistical methods (Ising models, change-point detection) toward applied cryptographic solutions and machine learning for smart environments, with recent emphasis on synthetic data generation for dementia care and activity monitoring systems.
Dr. Matthieu Brinkhuis is an Associate Professor at the Faculty of Science , Utrecht University , with a focus on Software Technology for Learning and Teaching . He serves as Director of Education for the Department of Information and Computing Sciences and is a Senior Fellow at the Center for Academic Teaching and Learning. His work bridges Learning Analytics , Computational Psychometrics , and Human-Centered AI . Research Themes : AI Labs, Governing the Digital Society, Human-Centered Artificial Intelligence Technical Expertise : Data Science, Learning Analytics, Adaptive Learning Systems, Process Mining His scholarly output includes pioneering methods like the Urning Algorithm for dynamic ability tracking and Federated Learning Analytics balancing privacy and performance. He leads the Utrecht Platform for Applied Data Science and contributes to Higher Education Research through large-scale online learning systems. Notable collaborations include projects on Privacy in SMEs , E-Assessment Validation , and Cognitive Diagnostic Models for actionable educational feedback. His work has been published in venues such as the British Journal of Mathematical and Statistical Psychology and Frontiers in Education .
Tomislav Šolić serves as an Assistant Professor at the Department of Production Engineering, Faculty of Mechanical Engineering, University of Slavonski Brod, where he has been employed since 2015. Holding a Ph.D. in Technical Sciences with specialization in Modern Production Processes, his academic career is deeply rooted at this institution where he completed all levels of education. Dr. Šolić's academic qualifications include: Doctor of Technical Sciences (2016–present), Postgraduate doctoral study module "Modern Production Processes" Master of Science in Mechanical Engineering (2013–2015), Graduate study in Mechanical Engineering University Bachelor of Mechanical Engineering (2010–2013), Undergraduate study of mechanical engineering His research program centers on welding technology , surface protection , and corrosion resistance , with particular emphasis on process parameter optimization. Dr. Šolić employs advanced methodologies including fuzzy logic modeling and statistical regression analysis to address complex engineering challenges in manufacturing. His scholarly output demonstrates consistent innovation in materials engineering, with recent publications revealing strong industry collaboration in automotive and shipbuilding sectors. Key research threads include computational modeling of plasma cutting processes, optimization of protective coatings, and failure analysis of welded structures. Dr. Šolić's scientific contributions have been recognized through: Academic achievement award from Slavonski Brod Welding Technology Society (2013) CEEPUS mobility fellowship at Obuda University, Budapest (2017) His teaching portfolio includes Production Processes III and Surface Protection courses, while research leadership spans multiple domains: University Research : Ni-alloy welded layer optimization and residual stress studies Institutional Projects : Technological parameter effects on material integrity European Initiatives : RCK-Slavonika 5.1 project leadership Industry Partnerships : Collaborative testing of shopprimers with industrial partners (2017, 2020) As an active member of the Croatian Society for Materials Protection and Welding Technology Society Slavonski Brod, he bridges theoretical research with practical industrial applications through interdisciplinary team collaborations within the university's production engineering facilities.
Mevlüde Ebrü Angün is an Associate Professor at Galatasaray University's Faculty of Engineering and Technology, Department of Industrial Engineering. Her research focuses on simulation optimization, industrial engineering, operations research, and disaster management. Doctorate in Operations Research, Katholieke Universiteit Brabant, Netherlands (2004) Postgraduate in Industrial Engineering, Boğaziçi University, Turkey (1998) Undergraduate in Industrial Engineering, Istanbul Technical University, Turkey (1995) Her research interests center on simulation optimization, stochastic programming, and disaster management. She applies these to retail operations, unmanned aerial vehicle (UAV) routing, and humanitarian logistics. Her work combines mathematical programming with practical applications in Turkey and international contexts. Scientific contributions include projects on UAV routing in disaster scenarios, retail assortment planning, and internal audit scheduling. She has supervised postgraduate theses on regression clustering and inventory optimization. Stochastic dual dynamic programming for disaster management Mixed-integer linear programming (MILP) for retail and auditing Risk-averse optimization under uncertainty She has served as a TUBITAK grant reviewer and peer reviewer for journals like Engineering with Computers and European Journal of Operational Research .
Don Batory is a Professor in the Department of Computer Science at the University of Texas at Austin, where he has held the David Bruton Centennial Professorship since 1983. He previously served as faculty at the University of Florida (1981) and has been a pivotal figure in advancing Feature-Oriented Software Development (FOSD). Education: B.S. (1975), M.Sc. (1977) from Case Institute of Technology; Ph.D. (1980) from the University of Toronto. His research focuses on Automated Software Design , Model Driven Engineering , and Feature-Based Product Lines , with applications to software refactoring and formal methods. His recent publications span topics like category theory in software engineering (2023), refactoring-aware testing (2018), and graph grammars in design automation (2013). Key trends: Integration of mathematical abstractions into software design, optimization of software maintenance practices, and formalization of product line engineering. He has received the Automated Software Engineering 2013 Most Influential Paper Award (with Lance Tokuda) and co-authored a textbook on FOSD. Since 1993, he and his students have produced 11 award-winning papers in automated program development. Leadership roles: Program Co-Chair (2002 GPCE), Associate Editor (IEEE Transactions on Software Engineering, ACM Transactions on Database Systems), and committee member across major conferences (PEPM, MODELS, SPLASH, GPCE).
Toni Mattis is a Ph.D. researcher and teaching assistant at the Software Architecture Group of the Hasso Plattner Institute (University of Potsdam, Germany). His work integrates artificial intelligence into software engineering practices, focusing on live programming environments , code repositories mining , and test prioritization . His research interests center around: Software modularity and architectural knowledge reification Exploratory programming and live programming environments Machine learning applications on code repositories Test prioritization techniques using AI and lexical analysis Recent publications analyze: AI-generated dynamic context for program comprehension (2024) Parallel version control for exploratory programming (2025) Game-based contexts for example-driven development (2024) Task complexity modeling in software maintenance (2022-2023) Scientific recognition includes: 2018 3rd Prize ACM Student Research Competition 2017 2nd Prize ACM Student Research Competition German Academic Scholarship Foundation (2010-2014) Multiple national computer science contest awards (2009) As educator, he has designed and supervised: Master's seminars on AI for Programming (2024) Code repository mining courses (2017-2020) Software testing modules covering TDD, BDD, and mutation testing
Paul Bürkner is a Full Professor of Computational Statistics at TU Dortmund University , focusing on probabilistic (Bayesian) methods. His research sits at the intersection of statistics and machine learning, with applications across quantitative sciences. Key Roles : Developer of the brms R package, member of the Stan and BayesFlow development teams. Research Pillars : Bayesian inference, uncertainty quantification, amortized workflows, simulation-based inference, and probabilistic programming. His lab advances methods for prior specification, model evaluation, and scalable inference, collaborating on applications from cognitive science to ecology. Recent work emphasizes neural superstatistics and BayesFlow for efficient mixture and multilevel models. Students and researchers are encouraged to reach out for collaboration or thesis opportunities. Key Labs/Teams : BayesFlow Development Team Stan Project ELLIS Network (European Laboratory for Learning and Intelligent Systems)
Andreas Koch is a Research Associate at the Professorship of Simulation for Additive Manufacturing at the Technical University of Munich (TUM), Germany. His research focuses on computational modeling of compressible multiphase flows, CutDG methods, and high-performance computing with software development. He holds a Master of Science (M.Sc.) in Mechanical Engineering from TUM (2024). Research Interests Koch's work bridges computational mechanics and machine learning, with emphasis on cut discontinuous Galerkin methods for simulating complex flows and high-performance computing systems. His publications demonstrate expertise in machine learning applications for aerospace systems, including anomaly detection in spacecraft telemetry and deep learning acceleration for spaceborne hardware. Scientific Awards ERC Starting Grant Publications (2024-2013) His research portfolio includes 15 recent publications spanning edge AI solutions for spacecraft , RF synchronization systems, and machine learning frameworks for aerospace applications. Earlier work explores robot perception and geoinformatics implementations.
Dr. Szabolcs Takacs is an Associate Professor at Gáspár Károli Reformed University (KRE), where he serves as Deputy Head of the Institute of Psychology within the Faculty of Humanities and Social Sciences. Additionally, he holds the position of Deputy Dean of Education. His academic career combines expertise in mathematics, statistics, and psychology, with a focus on applying quantitative methods to psychological and social research questions. Dr. Takacs has established himself as a prominent figure in statistical methodology within the Hungarian academic community. Dr. Takacs earned his educational qualifications at Eötvös Loránd University (ELTE): PhD in Applied Mathematics (2014) - Dissertation: "Solving some problems of hierarchical money distribution systems using random vectors and multidimensional scaling" MA in Applied Mathematics (2004) Dr. Takacs' research spans multiple domains at the intersection of statistics and psychology. His primary focus areas include statistical methodology development, performance evaluation systems, and adaptive measurement technologies. He applies operations research techniques such as linear programming and stochastic optimization to psychological measurement problems. His work often addresses practical applications in workforce analysis, educational assessment, and risk management. Dr. Takacs has made significant contributions to cluster analysis within person-oriented research frameworks and has developed innovative validation procedures for statistical models. Analysis of Dr. Takacs' 15 most recent publications reveals a strong emphasis on statistical methodology development with applications primarily in psychology and social sciences. His work demonstrates expertise in multidimensional scaling, classification methods, and cluster analysis. A notable trend is the application of mathematical optimization techniques to psychological measurement problems. Many publications address workforce and employment issues, reflecting his interest in practical applications of statistical methods. His research shows increasing sophistication in handling complex data structures while maintaining focus on real-world applicability. Dr. Takacs has received prestigious recognition from KRE: Publication of the Year (2014) - awarded in 2015 Teacher of the Year (2014) - awarded in 2015 Dr. Takacs actively mentors the next generation of researchers, currently supervising three doctoral students across institutions (Tilburg University and KRE). His supervisory approach emphasizes methodological rigor in psychological research. He has served as an opponent in multiple doctoral defenses, most recently in 2024 for research on mathematics-related beliefs and academic performance. His leadership extends to the Competency Assessment Research Group, which he leads, focusing on developing and validating assessment tools for various professional contexts. As Deputy Dean of Education and Deputy Head of Institute, Dr. Takacs plays a significant role in shaping educational policy and research direction within the Faculty of Humanities and Social Sciences. His work bridges theoretical statistical methodology with practical applications in psychology and social sciences, contributing to the university's research profile and educational mission.
Ганна Анатоліївна Шишканова is an Associate Professor at the Department of Applied Mathematics within the Faculty of Computer Science and Technologies at Zaporizhzhia Polytechnic National University. With over two decades of academic experience since 1999, she holds the academic title of Associate Professor and a Candidate of Physical and Mathematical Sciences degree. Her expertise spans applied mathematics, continuum mechanics, and mathematical modeling, with significant contributions to contact mechanics and econometrics. Zaporizhzhia State University (Applied Mathematics, Specialist) Zaporizhia National Technical University (Entrepreneurship, Master's degree, 2019) Dr. Shyshkanova's research focuses on solving complex mathematical problems in engineering and economics. Her work in spatial contact problems with unknown contact regions has practical applications in structural engineering and green building design. She has developed innovative approaches to multidimensional integro-differential equations and applied catastrophe theory to various scientific domains. Her recent research has expanded into econometrics, applying mathematical models to business optimization and economic forecasting. Her 77 scientific publications demonstrate consistent scholarly output across multiple disciplines, with recent work addressing sustainable construction, cylindrical structure deformation, and precision alloy production. The publications reveal a trend toward interdisciplinary research that bridges applied mathematics with practical engineering and economic challenges. Certificate of Merit on the 110th anniversary of ZNTU (2010) Certificate of Merit on the 115th anniversary from Zaporizhzhia City Council (2015) Honorary Certificate for long-term work and contribution to technical education (2021) Appreciation from NU "Zaporizhzhia Polytechnic" for 50th anniversary (2023) Certificate from Zaporizhzhia Regional State Administration (2023) Dr. Shyshkanova teaches Higher Mathematics, Probability Theory and Mathematical Statistics, Optimization Methods and Models, and Econometrics. Her pedagogical approach integrates traditional instruction with innovative distance learning technologies to enhance student engagement with mathematical concepts. She has contributed to educational methodology through research on student evaluation systems and the application of mixed learning approaches to mathematical disciplines.
Jelena Plašić serves as a Lecturer in the Department of Information Technologies at the Faculty of Technical Sciences, University of Kragujevac. She holds office number 130 at Saint Sava 65, 32102 Čačak, Serbia, with contact details +381 032/302-734 and email jelena.plasic@ftn.kg.ac.rs. Her academic journey began with a Graduate Computer Scientist degree from the University of Kragujevac's Faculty of Science and Mathematics, followed by a Master of Information Technology Engineer degree from the Faculty of Technical Sciences in Čačak (2020). Her research focuses on data science applications in business intelligence , with particular expertise in cloud-based analytics, CRM systems, and educational technology transformation. She teaches core computing courses including Introduction to Programming, Programming Practicum, Programming Languages, E-Business, and Software Engineering. Her publication record reveals a strong trajectory in applying machine learning and data warehousing to solve business problems, with increasing sophistication from foundational CRM studies (2018) to advanced sustainable supply chain analytics (2025). Recent publications demonstrate a clear evolution toward integrated cloud analytics solutions , with 8 of her 14 works (2020-2025) focusing on Microsoft Fabric, big data modeling, and predictive analytics. Her work bridges theoretical frameworks with practical business applications across diverse sectors including education, healthcare, and manufacturing. She actively contributes to academic discourse through conference organization for ITOP and TIE events, and participates in faculty promotion initiatives.
Mohamed Ndaoud is an Associate Professor of Statistics at ESSEC Business School and a member of the Statistics Department at CREST. Previously, he held a tenure-track position as Assistant Professor in the Department of Mathematics at the University of Southern California (USC) from August 2019. He earned his PhD in theoretical statistics under the supervision of A.B. Tsybakov. His educational background includes: PhD in Theoretical Statistics, supervised by A.B. Tsybakov. Dr. Ndaoud's research centers on high dimensional statistics, with core contributions in variable selection, estimation, and community detection. He also actively explores robust statistics, stochastic processes, harmonic analysis, random matrix theory, and spiked models, often bridging theoretical statistics with machine learning applications. Analysis of his publication record (2018-2024) reveals a consistent focus on developing robust and adaptive methods for high-dimensional data. Key themes include outlier-robust regression, clustering algorithms for mixture models, minimax optimal procedures, and harmonic analysis techniques for Gaussian processes. His work frequently introduces non-convex and tuning-free approaches to address challenges in sparse modeling and statistical learning. Scientific Awards: No awards were listed in the provided information. Research funding includes support from the CY Initiative of Excellence Paris-Seine. There is no information available regarding student advising or additional grants. Dr. Ndaoud is an integral member of CREST's Statistics Department and serves on the organizing committee for the Meeting in Mathematical Statistics (2023-2025) in Luminy, France. He actively participates in the international statistics community through workshops and tutorials, such as the upcoming Heidelberg-Paris workshop on mathematical statistics in January 2025.
Kamran Paynabar is the Fouts Family Chair and Professor at the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology . He specializes in Engineering-Driven Statistical Modeling , Statistical Learning Methods for Big Data Analytics , and Quality Engineering , with applications in Manufacturing Systems and Healthcare . B.S. and M.Sc. in Industrial Engineering from Iran University of Science and Technology and Azad University (2002, 2004) M.A. in Statistics and Ph.D. in Operations Engineering from the University of Michigan (2010, 2012) His research focuses on high-dimensional data analysis for system monitoring, diagnostics, and prognostics using semi-parametric and nonparametric approaches . Methodologies he develops address applications in automotive, aerospace, medical device manufacturing , and healthcare , including cardiac and orthopedic surgery analytics. Dr. Paynabar’s publications from 2016–2025 highlight trends in anomaly detection , tensor analysis , Gaussian process modeling , and active learning applied to photovoltaic systems , additive manufacturing , and smart grids . INFORMS Data Mining Best Student Paper Award Best Application Paper Award from IIE Transactions Wilson Prize for Manufacturing Systems Research POMS Best Paper Award Georgia Tech CETL/BP Teaching Excellence Award He has advised students like Xiaolei Fang (University of Florida), Hao Yan (Arizona State University), and Chitta Ranjan , with grants from the National Science Foundation (NSF) . His work emphasizes interdisciplinary collaboration , bridging industrial engineering , machine learning , and system optimization .
Wesley Klewerton Guez Assuncao serves as an Associate Professor in the Department of Computer Science within the College of Engineering at North Carolina State University. Previously, he held positions as a University Assistant/Senior Researcher at Johannes Kepler University Linz in Austria, Postdoctoral Researcher at Pontifical Catholic University of Rio de Janeiro in Brazil, and Assistant/Associate Professor at Federal University of Technology - Paraná in Brazil. His academic journey includes a Ph.D. in Computer Science from the Federal University of Paraná with a visiting period at Johannes Kepler University. Dr. Assuncao's research spans several critical areas in modern software engineering. His primary interests include Software Modernization (reverse engineering, re-engineering, and migration), Variability Management (variability mechanisms, software customization, and software reuse), and Software Quality (technical debt, code smells, and software refactoring). He also investigates Model-Driven Engineering (model inconsistency detection, repair generation, and change propagation), Collaboration in Systems Engineering (change synchronization, tool flexibility, and conflict awareness), Software Testing (regression testing, integration testing, and test case selection/prioritization), and AI4SE (Generative AI, Machine Learning, and Evolutionary Algorithms for Software Engineering). His recent publications reveal a strong focus on software modernization challenges, particularly regarding legacy systems transformation, microservices architecture, and the application of AI techniques in software engineering. The research shows increasing integration of Large Language Models in addressing software evolution problems, with emphasis on empirical validation through industrial collaborations. His work consistently bridges theoretical foundations with practical applications, as evidenced by multiple industry partnerships and real-world case studies. Dr. Assuncao has received numerous prestigious awards including Distinguished Reviewer Awards from FSE and SANER conferences, a Young Researcher Award from Johannes Kepler University, and multiple Best Paper Awards from top software engineering conferences. His research has been recognized with ACM SIGSOFT Distinguished Paper Awards and IEEE Computer Society TCSE Distinguished Paper Awards. In terms of academic service, he serves as Co-editor of the In Practice track at the Journal of Systems and Software and has held various leadership roles in major conferences including ICSE, SANER, MSR, and SPLC. He has successfully secured substantial research funding totaling approximately USD 1.91 million from sources including the Austrian Science Fund, Brazilian National Council for Scientific and Technological Development, and state-level Brazilian research foundations. Dr. Assuncao leads the Wolfpack Innovations in Software Engineering Research (WISER) Lab at NC State University, where he supervises graduate students working on cutting-edge software engineering research problems. His lab maintains strong collaborations with international institutions and industry partners including Dynatrace and ITPRO Consulting & Software GmbH.
Bayram Veli Doyar is a Lecturer in the Department of Economics and Department of Economic Policy at Suleyman Demirel University's Faculty of Economics and Administrative Sciences. He holds a PhD in Economics (2023), an MA in Economics (2015), and a Licence in Economics (2012) from Turkish institutions. His research focuses on Microeconometrics Science and technology studies Fuzzy numbers in economic modeling with publications analyzing ICT development, oil price impacts on GDP/exchange rates, R&D-productivity linkages, and technology-life satisfaction dynamics. Recent work includes studies on Internet use in the Southern Caucasus Innovation in Ukraine during conflict Causality in Turkey-Azerbaijan-Kazakhstan trade using econometric models like Probit, ARDL bounds test, and bootstrap causality.
