Joseph K Bump is a Professor in the Department of Fisheries, Wildlife, and Conservation Biology at the University of Minnesota. His research focuses on predator-prey dynamics, wetland ecology, and carnivore conservation, particularly examining interactions between wolves, beavers, and ungulates in Minnesota and other regions. His work includes evaluating wildlife monitoring technologies such as GPS collars for neonates and non-invasive hair snares for beavers, as well as modeling territorial behavior in wolf populations. Current projects address floodplain forest resilience, fawn dispersal, and the ecological impacts of megaherbivores in East Africa. Recent publications highlight wolf predation on beavers, GPS tracking methodologies, and social cohesion in Canis groups. His research outputs have been featured in media discussions about Minnesota environmental policy and wetland management challenges.
Tiziana Di Matteo is a Professor of Econophysics in the Department of Mathematics at King's College London, part of the Faculty of Natural, Mathematical & Engineering Sciences. She is also External Faculty at the Complexity Science Hub and affiliated with the UCL Centre for Blockchain Technologies, the Complex System Laboratory, the Complex Systems Society, and the Museo Storico della Fisica e Centro Studi e Ricerche “E. Fermi”. Her research focuses on econophysics, complex systems, complex networks, and data science , with applications in financial modeling, systemic risk, and multiscaling analysis. She integrates methods from statistical physics and network science to analyze financial markets and economic interactions. The most recent publications reveal a strong trend in financial network modeling, multiscaling volatility, tensor-based learning for multidimensional data, and early warning systems using scaling properties . Her work bridges econophysics and traditional financial economics, emphasizing empirical validation and interdisciplinary approaches. Scientific Leadership and Editorial Roles: Editor-in-Chief, Journal of Advances in Mathematical Physics Main Editor, Physica A Editor, European Physical Journal B Editor, Artificial Intelligence in Finance Former Editor-in-Chief, Journal of Network Theory in Finance Guest Editor for multiple special issues Tiziana is the co-founder of the Econophysics Network and has served as a consultant for the Financial Services Authority, hedge funds, and financial companies. She has authored over 100 papers and delivered keynote talks globally. She supervises PhD students and leads collaborative research in complex financial systems. She is actively involved in interdisciplinary research collaborations with institutions like the Francis Crick Institute and industrial partners including EDF Research and Unilever.
Dr. Victor Emilio Troster is an Associate Professor in the Department of Applied Economics at the Universitat de les Illes Balears (UIB), where he has been teaching since 2015. His academic focus spans financial econometrics, time series analysis, and econometric theory, with particular expertise in causality tests, quantile regression models, and financial asset forecasting. He teaches undergraduate and graduate courses including Econometrics, Survey Analysis and Multivariate Techniques, and Econometrics for Big Data across various business administration and economics programs. Ph.D. in Economics, Universidad Carlos III de Madrid, Spain (2015) Dr. Troster's research interests center on advanced econometric methods applied to financial markets and economic phenomena. His work focuses on developing and applying statistical techniques to analyze financial time series data, with particular attention to causality testing in quantile frameworks, specification testing for regression models, and forecasting of financial assets. His research bridges theoretical econometrics with practical applications in finance and economics, addressing complex questions in market behavior, risk analysis, and economic policy evaluation. His methodological contributions have significant implications for both academic research and practical financial decision-making. His publication record demonstrates consistent contributions to high-impact econometrics and finance journals, with a clear trajectory of increasingly sophisticated methodological work. His research spans both theoretical developments in econometric methodology and applied studies addressing contemporary financial and economic issues. The publications show a strong focus on time series analysis, with particular emphasis on nonlinear relationships, quantile-based approaches, and cross-market spillovers. His work frequently addresses methodological challenges in financial econometrics while providing insights relevant to practitioners and policymakers. Dr. Troster serves as a thesis advisor for the PhD in Applied Economics program at UIB. He has participated in competitive research projects funded by Spain's Ministry of Science and Innovation. His teaching portfolio spans multiple degree programs including Business Administration, Economics, and specialized Master's programs in Big Data Analysis and Tourism Economics. He is an active member of the Econometrics and Data Science (ECD) Consolidated R+D+I Group at UIB, contributing to the department's research infrastructure and collaborative projects. His work integrates traditional econometric approaches with emerging data science methodologies, positioning him at the intersection of established economic theory and contemporary analytical techniques.
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).
doc. dr Zoran Gazibarić is an Associate Professor at the Faculty of Technology, University of Banja Luka, specializing in Graphic Technologies. His research focuses on printing processes, color science, and quality control in graphic industries. He actively contributes to projects like 'Critical evaluation of color quality control in the printing industry' and 'Procurement of professional digital printing equipment for graphic technology laboratories.' His work spans inkjet printing techniques, textile printing analysis, and human perception of color differences. He collaborates on national research projects addressing printing process automation and material science challenges in corrugated cardboard design. Recent articles highlight innovations in UV inkjet printing, simulation modeling for packaging materials, and gender-based studies on color perception. He maintains a lab focused on advanced graphic technologies and has served as a key participant in projects funded by the Republic of Srpska's Ministry of Science and Technology.
Markus Leippold is a Professor of Financial Engineering at the University of Zurich and Director of the Master of Advanced Studies in Finance program. His research focuses on Climate Finance, Natural Language Processing, Asset Pricing, and Machine Learning applications in finance. Before academia, he worked in banking and corporate consulting, bringing practical insights to his academic work. Leippold emphasizes integrating modern technologies like AI into finance while cautioning against over-reliance on E-Learning for specialized education. His work bridges theoretical advancements with practical implications, such as developing models for climate risk assessment and corporate disclosures. Key contributions include studies on operational risk quantification, variance swaps, and the impact of climate policies on financial markets. He has published extensively in top journals and collaborates with institutions like the Swiss Finance Institute.
Mats Julius Stensrud is an associate professor of statistics at École Polytechnique Fédérale de Lausanne (EPFL), specifically within the Institute of Mathematics at the School of Basic Sciences. Starting January 1, 2025, he will also serve as the director of the doctoral program in mathematics (EDMA) at EPFL. His academic journey includes positions at Harvard School of Public Health as a Fulbright scholar and Kolokotrones fellow. Stensrud holds an MD, Dr. Philos in Neuroscience, and BSc in Mathematics from the University of Oslo, along with an MSc in Statistics from the University of Oxford. Prior to his academic career, he worked as a clinical medical doctor specializing in internal medicine. His research focuses on developing causal inference methods, particularly for longitudinal data where time-dependent exposures and outcomes are involved. He emphasizes creating methods with transparent, scientifically falsifiable assumptions that answer practically meaningful questions about causal effects and mechanisms. His work bridges theoretical statistics with real-world applications in biomedicine and epidemiology. Analysis of his recent publications shows a strong focus on causal inference methodology, particularly addressing challenges in survival analysis, vaccine effectiveness studies, treatment effect heterogeneity, and applications in oncology. His work frequently examines interpretation, identification, and estimation of causal parameters while developing robust statistical approaches for complex data structures. Fulbright Scholar Kolokotrones Fellow Stensrud actively mentors the next generation of statisticians, currently supervising five PhD students: Amit Sawant, Lorenzo Gasparollo, Julien Laurendeau, Gellert Perenyi, and Ignacio Gonzalez Perez. His previous PhD student, Matias Janvin, successfully defended his thesis in November 2023. He also mentors postdoctoral researchers including Elise Dumas, with previous fellows including Aaron Sarvet (now Assistant Professor at University of Massachusetts Amherst), Pal Ryalen (Senior Scientist at University of Oslo), and Anders Huitfeldt (MD at Oslo University Hospital). He leads the Chair of Biostatistics research group at EPFL, which focuses on developing and applying advanced statistical methods for causal inference. The group includes PhD students, postdoctoral researchers, and administrative support, working collaboratively on both theoretical methodology development and substantive applications in medicine and epidemiology.
Andrea Tancredi is a Professor at the Department of Methods and Models for Economy, Territory, and Finance, Sapienza University of Rome. His research focuses on Bayesian inference, computational statistics, and their applications in biostatistics, population size estimation, and health economics. He has coordinated multiple research projects funded by the University, including studies on record linkage, approximate Bayesian computation (ABC), and stochastic frontier models. Research Interests: Bayesian Inference Computational Methods (MCMC, ABC) Population Size Estimation Record Linkage and Data Integration Extreme Value Theory Biostatistics and Health Economics Teaching: He teaches courses such as Basic Statistics, Advanced Statistical Methods, and Extreme Value Theory at the Faculty of Economics and doctoral programs in Economics. Publications and Citations: With 47 publications (23 journal articles), his work spans Bayesian modeling for microbial diversity, climate-conflict risk forecasting, and multi-state health models. His research is cited 682 times on Google Scholar (h-index 12) and 401 times in Scopus (h-index 9).
Ralf Engbert is a Professor of Experimental and Biological Psychology at the University of Potsdam's Faculty of Human Sciences, where he leads research in eye movement control, attention, and dynamical modeling of cognitive processes. He serves as a member of the Executive Committee of the Research Focus 'Cognitive Sciences' at the university, demonstrating his leadership role in interdisciplinary research. His work bridges psychology, neuroscience, and mathematical modeling to understand fundamental cognitive mechanisms through rigorous empirical and computational approaches. Engbert received his Dr. rer. nat. in 1998 from the Department of Physics at the University of Potsdam and completed his Dipl.-Physiker in 1994 at RWTH Aachen University. His physics background has profoundly shaped his research methodology, emphasizing quantitative and mathematical approaches to psychological phenomena. This interdisciplinary foundation enables him to develop sophisticated computational models that capture the dynamic nature of cognitive processes. Professor Engbert's research program centers on the dynamical systems approach to cognition, with particular emphasis on attention and eye movements. His work integrates biological and mathematical psychology to create computational models explaining visual information processing during reading and scene viewing. Key contributions include advancing understanding of microsaccades, peripheral vision in scene perception, and Bayesian approaches to modeling eye movement control. His research frequently involves cross-disciplinary collaborations with mathematicians, computer scientists, and linguists, reflecting the integrative nature of modern cognitive science. Analysis of his recent publications reveals a clear progression toward more sophisticated dynamical modeling of eye movements, with increasing incorporation of Bayesian statistical methods. His work spans multiple domains including reading research, scene perception, and infant cognition, with a notable trend toward integrating computational modeling with empirical eye-tracking data to develop comprehensive theories of cognitive processing. This evolution reflects both methodological advances and expanding theoretical scope in his research program. Project B03 of CRC 1294: Parameter inference and model comparison in dynamical cognitive models (with Prof. Sebastian Reich) Project B05 of CRC 1294: Attention selection and recognition in scene viewing (with Prof. Tobias Scheffer) Project B03 of CRC 1287: Modeling the interaction between eye-movement control and parsing processes (with Prof. Shravan Vasishth) Mobile eye tracking: Generalizability and limitations of the static scene viewing paradigm (Lead PI: Dr. Hans A. Trukenbrod) Professor Engbert maintains an active research group within the Department of Experimental and Biological Psychology, contributing to the Potsdam Eye-Movement Corpus and other shared research resources. His laboratory combines experimental eye-tracking facilities with computational modeling expertise, supporting a pipeline from data collection to theoretical development. The group's work has significant implications for understanding fundamental cognitive mechanisms and developing applications in human-computer interaction, educational technology, and clinical assessment.
Kaitlyn Cook serves as Assistant Professor of Statistical & Data Sciences at Smith College, where she maintains an active research program at the intersection of biostatistics and medical applications. Her office is located in McConnell Hall 210, and she holds regular office hours on Tuesdays 9–11 a.m. during Spring 2025. Her educational background includes advanced degrees from prestigious institutions: Ph.D. and A.M. from Harvard University B.A. from Carleton College Dr. Cook's research program demonstrates dual focus: methodological innovation in statistical techniques and substantive medical collaborations. She develops robust statistical methods for infectious disease treatment and prevention trials, drawing on expertise in missing data literature, clustered and longitudinal data analysis, survival analysis, and computational statistics. Her substantive collaborations span multiple medical domains including pediatric sleep medicine (particularly snoring and sleep apnea in children), pregnancy outcome prediction, cancer diagnostics, and statistical genetics. Analysis of her recent publications reveals consistent engagement with NIH-funded clinical trials like the Pediatric Adenotonsillectomy for Snoring (PATS) study. Her scholarly output shows significant methodological contributions to handling complex data structures (particularly clustered interval-censored data) while maintaining strong ties to clinical applications. The publications demonstrate expertise in translating statistical innovations into practical medical research tools, with particular emphasis on pediatric sleep disorders and cancer diagnostics. Dr. Cook actively engages in interdisciplinary research collaborations that address pressing medical questions through sophisticated statistical approaches. Her work on the PATS clinical trial demonstrates leadership in designing and analyzing studies that evaluate surgical interventions for pediatric sleep disorders. She also contributes to developing novel diagnostic tools for cancer through work on extracellular vesicle profiling from blood plasma.
Thomas Valentin Mikosch is a Professor at the Department of Mathematical Sciences , University of Copenhagen. His research focuses on extreme value theory, stochastic processes, and time series analysis with applications in actuarial mathematics and risk management. Extreme value theory for dependent data Heavy-tailed distributions in financial and insurance contexts Statistical inference for stochastic models Recent work includes publications on power-law tails in time series, cluster inference for extremal events, and distance covariance in spatial models. He has served as an editor for journals like Bernoulli and Stochastic Processes and Their Applications and holds memberships in prestigious organizations such as the Royal Danish Academy for Sciences and Letters.
Xuerong Meggie Wen is a Professor of Statistics in the Department of Mathematics & Statistics at Missouri University of Science and Technology, where she conducts research in advanced statistical methodologies. She is affiliated with the College of Arts, Sciences, and Education and maintains an active research program in dimension reduction, variable selection, survival analysis, and longitudinal data modeling. Ph.D. in Statistics, University of Minnesota M.S. in Operations Research, Chinese Academy of Sciences B.S. in Probability and Statistics, Peking University, China Her research focuses on sufficient dimension reduction , variable and model selection , and survival and recurrent event data analysis . She develops model-free and nonparametric methods for high-dimensional and complex data structures, with applications in science and engineering. Her work emphasizes conditional independence, sparsity, and efficient estimation without restrictive parametric assumptions. The recent publications show a strong trend in sufficient dimension reduction across multi-population and multi-index settings, with increasing emphasis on sparsity , link-free methods , and conditional screening . Her research bridges theoretical statistics with practical applications, particularly in handling censored, recurrent, and high-dimensional data. Dr. Wen has not been explicitly mentioned to have received scientific awards in the provided text. She has advised and collaborated with several researchers, including Lu Li, Zhou Yu, Lei Huo, and Xuejing Liu, indicating an active role in mentoring graduate students and junior researchers. While no grants are listed, her sustained publication record suggests ongoing research support. She teaches courses in statistics, accessible through the university’s Canvas platform. Dr. Wen leads a research group focused on nonparametric and semiparametric statistical methods, particularly in dimension reduction and variable selection for complex data. Her team works on both theoretical development and computational implementation of novel statistical techniques.
Pavel Chernyavskiy is an Assistant Professor in the Department of Public Health Sciences, Division of Biostatistics at the University of Virginia, School of Medicine. He joined the university in January 2021, bringing expertise in statistical methods for analyzing correlated data arising from clustered, spatial, or longitudinal studies. His research interests focus on methodological and applied biostatistics, particularly in the context of public health. Key areas include health disparities , population health , non-stationary spatial correlation , and intervention efficacy . His work addresses complex data structures where observations are correlated due to repeated measures, spatial proximity, or temporal dependencies. Prior to joining the University of Virginia, Dr. Chernyavskiy was an Assistant Professor of Statistics at the University of Wyoming. He completed his PhD in 2015 at the University of Nebraska–Lincoln and was a Postdoctoral Fellow at the National Cancer Institute’s Division of Cancer Epidemiology and Genetics in Rockville, MD. His collaborative research extends across diverse domains including public health, education, psychology, and ecology, demonstrating the broad applicability of his statistical methodologies. PhD, University of Nebraska–Lincoln, 2015 Postdoctoral Fellow, National Cancer Institute – Division of Cancer Epidemiology and Genetics Former Assistant Professor of Statistics, University of Wyoming Dr. Chernyavskiy has not listed any scientific awards or specific publications in the provided text, but his ongoing research program suggests active engagement in grant-funded and collaborative studies. There is no indication of student advising at this time. He is affiliated with the Division of Biostatistics within the Department of Public Health Sciences, contributing to both methodological development and interdisciplinary applications in health sciences.
Dr. Emmanuel Fianu is a Lecturer in the Department of Accounting and Finance at De Montfort University, Faculty of Business and Law. He has held academic and research positions at several international institutions, including Mainz University of Applied Sciences, Leuphana University of Lüneburg, University of Freiburg, University of Verona, Goethe University Frankfurt, and University of Naples Parthenope. His work bridges academia and industry through applied research in finance and data science. Education: PhD in Economics and Finance, University of Verona, Italy MSc in Mathematical Economics and Applied Mathematics, Bielefeld University & University of Paris 1 Panthéon-Sorbonne BSc (Hons) in Mathematics, Kwame Nkrumah University of Science and Technology Dr. Fianu’s research is centered on Financial Econometrics, Risk Management, Sustainable Finance, and AI applications in FinTech and Energy Markets. He employs advanced quantitative methods, including stochastic modeling, machine learning, and network analysis, to study systemic risks, ESG integration, and commodity price dynamics. His interdisciplinary approach connects finance, environmental economics, and data science. His recent publications span areas such as contagion in electricity markets, ESG portfolio optimization under solvency constraints, and hybrid forecasting models for commodities. These works reflect strong trends in sustainable finance, energy market risk, and the application of AI and computational methods to financial decision-making. Scientific Awards: Young Investigator Research Grant (€2,674) from ACRI Post-Doctoral Research Grant from German Federal Ministry of Education and Research Ph.D. Scholarship from University of Verona and Italian Ministry of Education (€13,638 p.a.) UseR 2012 Student Travel Scholarship ($500) Erasmus Mundus Scholarship for QEM Master (€42,000) Award winner at Energy Finance Italia Conference 2018 Dr. Fianu has successfully supervised multiple Master’s and DBA students and currently serves as an assessor for a Doctor of Business Administration candidate. He has secured externally funded research grants, including a €40,000 project on sustainable investments in insurance portfolios under Solvency II. He is actively involved in academic service as a reviewer for leading journals and as a member of scientific committees. He also contributes to institutional development as the AI Go-To Person in his department and participates in key university committees related to IT and research symposia. He is affiliated with several research groups, including the Centre for Research in Accountability, Governance and Sustainability (CRAGS) and the Finance and Banking Research Centre (FiBRe), and collaborates internationally on projects related to climate finance and financial regulation.
Alberto Rodriguez Casal is a Professor in the Department of Statistics, Mathematical Analysis and Optimization at the Faculty of Mathematics, University of Santiago de Compostela. He is affiliated with the Galician Mathematical Research and Technology Center (CITMAga) and leads the MODESTYA research group focused on Optimization, decision, statistical models and applications. His research primarily focuses on nonparametric statistics, with special emphasis on: Set and boundary estimation Density level sets and shape analysis Statistical methods for geometric inference Nonparametric estimation under shape constraints Analysis of his publication record spanning nearly two decades shows a consistent focus on geometric and shape-related statistical problems. His work bridges theoretical statistical developments with practical applications in areas such as environmental statistics and spatial analysis. Recent publications demonstrate continued innovation in data-driven methods for shape and boundary estimation, particularly through the application of alpha-shapes and other computational geometry approaches to statistical problems. Professor Rodriguez Casal has established significant collaborative networks, particularly with researchers including Saavedra-Nieves, Paula; Crujeiras, Rosa María; de Uña-Álvarez, Jacobo; Pateiro-López, Beatriz; and Cuevas, Antonio. His work has been published in top statistical journals including The Annals of Statistics, Journal of the American Statistical Association, Test, and Journal of Nonparametric Statistics, with over 25 publications since 2004 that have been cited more than 300 times.
