Wolfgang K. Schief is a Professor at the School of Mathematics and Statistics, University of New South Wales (UNSW), Sydney, Australia. He previously served as Head of the Department of Applied Mathematics. His academic history includes positions as Professor at Technische Universität Berlin (Germany), Associate Professor and Senior Lecturer at UNSW, and Queen Elizabeth II Research Fellow. His research spans integrable systems, soliton theory, differential geometry (continuous/discrete), continuum mechanics, and general relativity. Key themes include geometric aspects of nonlinear equations, discretization methods, and applications in physics. Recent work emphasizes discrete differential geometry, integrable discretizations, and connections between geometry and physical models. Professor Schief's publications (2015–2019) predominantly explore discrete integrable geometry, surface theory, and geometric mechanics. Trends include advanced discretization techniques, projective/minimal surfaces, circle complexes, and multi-dimensional consistency in heavenly-type equations.
Rancoita Paola Maria Vittoria is an Associate Professor of Medical Statistics (MED/01) at the Faculty of Medicine and Surgery, Vita-Salute San Raffaele University (UniSR), since 2022. She is actively involved in research activities at the University Center for Statistics for Biomedical Sciences (CUSSB) and serves on the Board of Directors of CUSSB (since 2016) and the Scientific Committee of CeNRI (Center for Nursing Research and Innovation, since 2016). She also participates in academic governance through multiple committees in the International MD Program (since 2020) and the Doctoral School in Public Health at the University of Milan-Bicocca (since 2020). Research Focus : Her work centers on developing and applying statistical methodologies in biostatistics and bioinformatics, including survival analysis with frailty models, repeated measures and clustered data analysis, genomic data integration via Bayesian regression, and stochastic geometry for angiogenesis image analysis. These techniques address clinical challenges like missing data handling and prognostic index identification. Academic Background : PhD in Mathematics and Statistics for Computational Sciences (2010), University of Milan Post-doctoral Researcher (2010-2011), IDSIA/IOSI Switzerland Research Fellow (2012-2013), Statistics SECS-S/01, Faculty of Psychology, UniSR Fixed-Term Researcher (2014-2021), Medical Statistics MED/01, UniSR Scientific Recognition : WHO Statistical Consultant (2022) Task Force Member, Stop TB Partnership (2020) ISBA Travel Award (2010) INdAM Fellowship (2004-2006) Leadership Roles : She served as Treasurer in the Italian Region of the International Biometric Society (2018-2021) and held leadership roles in Società Italiana di Statistica Medica ed Epidemiologia Clinica (2020-2021). Her teaching spans statistics, bioinformatics, and clinical data science across undergraduate and postgraduate programs at UniSR and Milan-Bicocca University.
Ewa Synówka is a researcher at the Institute of Mathematics, University of Zielona Góra , Poland. Her academic work spans interdisciplinary applications of mathematics and computer science. Research focuses on iterative methods for fixed point problems in Hilbert spaces Contributions to graph theory, particularly coloring models and combinatorial geometry Investigations into nonlinear wave propagation and stochastic equations Applications of computer science in secure data transmission and supply chain privacy She teaches advanced statistical methods, econometric modeling, and multivariate data analysis. Her work also emphasizes modern pedagogy using open-source tools for primary/secondary school outreach.
Sima Siami-Namini serves as a Lecturer at Johns Hopkins University, teaching in the MS in Applied Economics program with extensive experience in undergraduate and graduate instruction across economics, statistics, and finance disciplines. Her academic credentials include advanced interdisciplinary training: PhD in Applied Economics (minor: Statistics), Texas Tech University, 2020 Master's in Statistics, Texas Tech University, 2022 Master's in Artificial Intelligence (Machine Learning focus), University of North Texas, 2023 Her research program integrates macroeconomic theory with cutting-edge computational methods, specializing in monetary policy analysis, time series econometrics, and AI-driven forecasting. She bridges traditional economic modeling with machine learning applications, particularly in anomaly detection, data visualization, and large language model implementations for economic forecasting. Analysis of her publication trajectory (2020-2024) reveals three dominant research streams: (1) deep learning architectures (LSTM, TCN) for time series forecasting and anomaly detection, (2) monetary policy impacts on income inequality using FAVAR/SVECM models, and (3) natural language processing applications for Federal Reserve communication analysis. Her recent work increasingly incorporates large language models for domain-specific economic analysis and code generation. No documented scientific awards or major honors appear in the available records. She mentors students in the Applied Economics program with emphasis on quantitative research methods, though specific grant funding details remain undisclosed. Her teaching methodology incorporates experiential learning techniques adapted from digital forensics education frameworks. No dedicated research laboratories or institutional teams are referenced in the source materials.
Benedikt Ehinger is a Tenure-Track Professor for Computational Cognitive Science at the Stuttgart Center for Simulation Science (SC SimTech) and the Institute for Visualization and Interactive Systems (VIS) at the University of Stuttgart. His research bridges cognitive neuroscience, computational modeling, and visualization techniques to understand visual perception and decision-making processes. Education 2018: PhD in Cognitive Science from University of Osnabrück with thesis "Predictions, Decisions and Learning in the visual sense" 2013: Master of Science in Cognitive Science from University of Osnabrück with thesis "Filling in Blind-Spots: A psychophysical and an EEG study" 2011: Bachelor of Science in Cognitive Science from University of Osnabrück with thesis "Electrophysiological Correlates of Category Learning" Research Interests Ehinger's research focuses on the intersection of visual cognitive science, computational modeling, and neuroimaging techniques. His work primarily investigates predictive coding mechanisms in visual perception, statistical learning in visual scenes, eye movement control, method development for combined EEG and eye-tracking analyses, visual completion phenomena like the blind spot, and category learning and neural plasticity. His approach combines behavioral experiments, EEG recordings, eye-tracking, and advanced statistical modeling to uncover the computational principles underlying human visual cognition. Publication Trends Ehinger's publication record shows a clear evolution from foundational work on visual perception and category learning toward methodological innovations in neuroimaging analysis. His early work focused on visual completion phenomena, category learning, and melanopsin modeling. More recently, he has pioneered techniques for analyzing combined EEG and eye-tracking data, developing toolboxes like "unfold" that address critical challenges in temporal overlap correction and regression-based analysis. His research demonstrates a consistent thread of applying computational approaches to understand visual cognition while simultaneously advancing the methodological toolkit of cognitive neuroscience. Scientific Contributions Development of the "unfold" toolbox for overlap correction and regression-based EEG analysis Creation of the EEGVIS toolbox for EEG visualization Establishment of comprehensive eye-tracking test batteries for validating mobile eye-tracking devices Innovative approaches to modeling fixation durations and eye movement patterns Research Environment Ehinger leads the Computational Cognitive Science group within the Institute for Visualization and Interactive Systems at the University of Stuttgart. His work is situated at the intersection of cognitive science, neuroscience, and computer science, collaborating with researchers across these disciplines. His lab utilizes behavioral experiments, EEG, eye-tracking, and computational modeling to investigate visual cognition, with emphasis on open science practices and methodological transparency.
Yuliana Yu. Linke is an Associate Professor at the Chair of Probability Theory and Mathematical Statistics of Novosibirsk State University and a Senior Researcher at the Laboratory of Applied Inverse Problems of the Sobolev Institute of Mathematics. She holds a Candidate of Science (Ph.D.) from the Sobolev Institute of Mathematics (2000) and a Doctor Habilitatus from Lomonosov Moscow State University (2024). Education: 1992-1998, Novosibirsk State University, Mathematical Department 1998-2000, Postgraduate Course, Chair of Probability Theory and Math. Statistics, NSU 2000, Candidate of Science (Ph.D.), Sobolev Institute of Mathematics 2024, Doctor Habilitatus (Dr.Habil), Lomonosov Moscow State University Her research focuses on regression analysis and the change-point problem. She has extensively contributed to nonparametric estimation techniques, particularly kernel methods and asymptotic statistics, addressing challenges in stochastic processes and heteroscedastic models. Her publications over the past 15 years highlight advancements in kernel-type estimators for regression models, emphasizing uniform consistency, insensitivity to design correlation, and asymptotic normality under non-identical distributions. These works span theoretical developments and applications to stochastic processes and random fields. Linke has also taught courses for the Department of Natural Sciences at Novosibirsk State University, with teaching materials available in Russian.
Toni Susin is an Associate Professor of Applied Mathematics at UPC-BarcelonaTech. He leads the Dynamic Simulation Lab, part of the ViRVIG research group in Barcelona. His research focuses on numerical methods, physically-based simulation, and applications in computer graphics and biomechanics. Key research areas include Physically-Based Animation Surgical Simulation Biomechanical Applications Image-Based Modeling Fluid Animation Techniques His recent work spans microbiome data analysis, sports performance tracking, and biomedical simulations. While no scientific awards are listed, his career includes founding three tech companies and mentoring numerous PhD/Master's students in computational methods and simulation technologies.
Tasha Beretvas is a Professor of Quantitative Methods in the Department of Educational Psychology and Associate Dean of Research and Graduate Studies at the College of Education, University of Texas at Austin. She holds the John L. and Elizabeth G. Hill Professorship and has led faculty affairs as Senior Vice Provost for Faculty Affairs. Research focuses on psychometric models, multilevel modeling with complex data structures (e.g., student mobility), and meta-analytic techniques for single-case designs. Key contributions include methodological advancements in multilevel modeling, meta-analysis of dependent effect sizes, and synthesis of single-subject experimental data. Her work appears in journals like Multivariate Behavioral Research , Psychological Methods , and Journal of Educational Psychology . Recent articles emphasize meta-analysis of single-case designs, handling missing data in meta-regression, and cluster wild bootstrapping for dependent effect sizes. She has pioneered tools like MultiSCED for multilevel analysis of single-case data. Scientific Awards Regents Award for Excellence in Undergraduate Teaching Grants Principal Investigator (PI) or Co-PI on multiple Institute of Education Sciences (IES) grants Leadership Elected member, Society for Research Synthesis Methodology Past Associate Editor, Journal of School Psychology and Journal of Educational Psychology Member, Design and Analysis Committee for the National Assessment of Educational Progress (NAEP)
Antonio Pietrabissa is an Associate Professor at the Department of Computer, Control, and Management Engineering “Antonio Ruberti” (DIAG) of the University of Rome Sapienza, where he earned his degree in Electronics Engineering (2000) and PhD in Systems Engineering (2004). He has been teaching Automatic Control and Process Automation since 2010 and holds the National Scientific Qualification as Full Professor in Systems and Control Engineering (09/G1). Research interests include networked systems, robust control, Markov decision processes, and deep reinforcement learning. His work spans telecommunications, biomedical applications, and space systems, with a focus on federated learning and decentralized control. He has authored ~70 journal papers (Scopus h-index 22) and co-invented a patent for model predictive control in motor disability assistance. Awards include the 2021 Cybersecurity Award and ETRI Journal Best Paper. Current projects are NANCY (6G networks) and CADUCEO (AI-driven medical diagnostics). He is also CEO of Sapienza startup Automation Intelligence and Control (AICO), commercializing AI solutions for space, telecom, and biomedical sectors.
Gavin Cawley is a Professor in the School of Computing Sciences at the University of East Anglia (UEA), with additional affiliations to the Data Science and AI group, the Centre for Ocean and Atmospheric Sciences, and the Statistics group. His research spans machine learning, bioinformatics, climate modeling, and environmental data analysis. His primary research interests include Machine Learning , Kernel Methods , Model Selection , Bayesian Regularization , Bioinformatics , and Climate Modeling . He has made significant contributions to understanding overfitting in model selection, sparse logistic regression for gene and cancer classification, and time series classification using ensemble methods. His work bridges theoretical machine learning with practical applications in biology, archaeology, and environmental science. The recent trend in his publications shows a strong interdisciplinary focus, combining machine learning with climate science (e.g., Arctic sea ice prediction) and molecular biology (e.g., protein domain movements). His work often involves developing and evaluating statistical models for complex real-world problems, emphasizing robustness, interpretability, and predictive accuracy. While no specific awards are listed in the provided text, his extensive publication record in top journals such as Journal of Machine Learning Research , Bioinformatics , and Neural Networks , along with high citation counts (e.g., over 1,800 citations for his 2010 paper on overfitting), indicates significant recognition in the academic community. He has also contributed to organizing major machine learning challenges, such as the ChaLearn AutoML and Active Learning challenges. He has supervised or collaborated with numerous researchers across disciplines, though specific student names are not listed. His work involves methodological development in model selection, kernel learning, and survival analysis, often applied to biological and environmental datasets. He has been involved in projects related to predictive uncertainty, ozone forecasting, and microbial growth modeling. While no specific lab or team name is mentioned, his affiliations with the Data Science and AI group and the Centre for Ocean and Atmospheric Sciences suggest active participation in interdisciplinary research teams focused on data-driven environmental and biological modeling.
Chantal van den Berg is an Assistant Professor in the Department of Criminal Law and Criminology at the Faculty of Law, VU University Amsterdam, and a researcher and program leader at the Netherlands Institute for the Study of Crime and Law Enforcement (NSCR). Her work bridges empirical research and normative studies in criminology, with a strong focus on victimology and the social consequences of criminal behavior. VU University Amsterdam – Assistant Professor, Criminology (2017–present) NSCR – Researcher and Program Leader, Victimology (2022–present) Leiden University – Assistant Professor, Criminology (2014–2015) She earned her BSc and MSc in Criminology from VU University Amsterdam and completed her PhD there in 2015, studying the long-term criminal careers of juvenile sex offenders and their life transitions. Her research centers on sexual offending (particularly by juveniles) , sexual victimization , labor market discrimination against ex-offenders , risk assessment , and the collateral consequences of criminal conviction , especially regarding the Certificate of Conduct (VOG). She employs advanced statistical methods and field experiments to investigate how criminal records and personal characteristics affect employment outcomes and social integration. Her recent publications (2023–2025) reveal a strong trend toward studying digital disclosures of sexual victimization (e.g., during the #MeToo movement), employment barriers for cybercrime offenders , and evidence-based rehabilitation in criminal record screening. These works highlight her interdisciplinary approach, combining criminology, sociology, and data science. She co-supervises two PhD candidates: Marleen Gorissen , researching online disclosure of sexual victimization, and Nora van Buitenen , analyzing comorbidity in mentally ill prisoners using network methods. She also contributes to major research projects such as Evidence-based interventions for victims . Member, Examination Board, Faculty of Law, VU University Amsterdam Former Chair, Programme Committee (OLC) Criminology (2017–2021) Co-editor, What works. Effectieve ondersteuning voor slachtoffers (2023) Chantal teaches Victimology at the BSc level and advanced courses in multivariate analysis and research methods for both BSc and MSc Criminology students. Her academic service includes editorial and peer-review roles, as well as frequent academic presentations on juvenile sex offending and rehabilitation. She is actively involved in research collaboration through NSCR and VU, with a growing network in victimology, labor market reintegration, and digital justice. Her work continues to influence both academic discourse and policy development in criminal justice and victim support.
Angelos Alexopoulos is an Assistant Professor at the Department of Economics , Athens University of Economics and Business . He has held Research Associate positions at the University of Cambridge, University College London, and University of Exeter in the UK. PhD: Athens University of Economics and Business Research Focus: Computational Statistics, Econometrics, Bayesian Analysis, Network Modelling Publications span Bayesian inference, epidemic forecasting, machine learning for fraud detection, and econometric methodology. Key journals include Journal of the Royal Statistical Society , Journal of Computational and Graphical Statistics , and Statistics and Computing . 2024: Gaussian invariance in MCMC 2024: Epidemic nowcasting models 2023: VAT fraud detection with ML Awards include certifications in Deep Learning (Coursera), Blockchain (edX), and Object-Oriented R Programming (DataCamp).
Dr. Beata Sternal is an assistant professor at Adam Mickiewicz University, affiliated with the Geohazards Research Unit. Her research focuses on marine geology, particularly sedimentation processes in coastal and shelf environments across diverse regions including the Vietnamese Shelf of the South China Sea, Thailand Coast of the Indian Ocean, Sendai Plain in Japan, SW Spitsbergen in the Arctic Ocean, and Northern Norway's fjords. She employs sedimentological (grain-size, X-ray imaging), geochemical (radioisotopes, TOC, SEM-EDS), and mineralogical (heavy and clay minerals) methods. Education: BSc and MSc in Geology (Vietnamese Shelf studies), PhD in Earth Sciences (Arctic Ocean/SW Spitsbergen research). Her publications highlight expertise in heavy mineral analysis, mine tailing dispersion, and paleoenvironmental reconstruction. She has contributed to understanding human-induced environmental changes in Arctic fjords and sediment dynamics from catastrophic events like the 2004 Indian Ocean and 2011 Tohoku tsunamis.
Prof. Dr. Sebastian Mentemeier is a permanent Professor (W2) in the Department of Mathematics 2 at the Institute for Mathematics, Mathematics Education and Computer Science Education, University of Hildesheim, Germany, a position he has held since October 2019. He also holds significant administrative roles as Vice Dean and Dean of Studies in Faculty 4 (Mathematics, Natural Sciences, Economics & Computer Science), and is a member of the Institute's Board and the Central Commission for Studies and Teaching. His research is centered on advanced probability theory, with a focus on branching processes, products of random matrices, and extreme value theory for time series. His research interests include: Non-Gaussian limit theorems Branching processes, particularly Multitype Branching Random Walks Products of random matrices Extreme value theory for time series Heavy-tailed random variables Conditional limit theorems His recent publications, spanning from 2012 to 2022, demonstrate a consistent and high-impact research trajectory in theoretical probability. The articles reveal a strong trend in analyzing the asymptotic behavior of complex stochastic systems, particularly those defined by recursive equations and random matrix products. His work frequently intersects with statistical mechanics and time series analysis, often investigating the tail behavior and limit laws of solutions to stochastic fixed-point equations, with a particular emphasis on heavy-tailed and multivariate settings. Prof. Mentemeier has been a Principal Investigator on two major DFG projects: 'Nonlinear stochastic fixed-point equations with applications in statistical mechanics' (2017-2023) and 'Products of Random Matrices, Noncommutative Branching Random Walks, and Multitype Branching Random Walks in Random Environments' (since 2021). He is an active member of the academic community, serving as a referee for journals such as Stochastic Processes and their Applications and Journal of Theoretical Probability , and has co-organized international conferences on recursive stochastic processes and branching models. He supervises PhD and Master's students, although specific names are not listed on his profile. He has led a research group within the Department of Mathematics 2 and is a key member of the Institute's academic board. His work is conducted within the broader context of the Faculty of Mathematics, Natural Sciences, Economics & Computer Science at the University of Hildesheim.
Prof. Bianca Maria Colosimo is a faculty member at Politecnico di Milano in the Department of Mechanical Engineering. Her research focuses on in-situ sensing and monitoring of metal additive manufacturing (AM) processes, particularly Laser Powder Bed Fusion. She develops statistical data mining techniques for defect detection and process control, integrating big data streams into quality assurance systems. Her work aims to advance smart AM technologies through real-time monitoring and digital twin methodologies. Key research interests include: Metal Additive Manufacturing Statistical Process Control Image Data Analysis Laser Powder Bed Fusion In-situ Monitoring Smart Manufacturing Systems Publications highlight her contributions to layerwise imaging , plume signature analysis , and spatial statistical modeling for AM processes. Contact: Department of Mechanical Engineering, Politecnico di Milano.
