Manuel Fernández Delgado is an Associate Professor at the University of Santiago de Compostela (manuel.fernandez.delgado@usc.es). His research spans Machine Learning , Pattern Recognition , and Computer Vision with applications in medical diagnostics, agricultural monitoring, and gender-inclusive education. PhD in Computer Science (1999) Developed software tools: Govocitos , CystAnalyser , STERapp Research highlights: Medical Imaging : Breast cancer and oral leukoplakia analysis via image segmentation/classification Agricultural AI : Nutrient deficiency detection in wheat, marbling analysis in ham Gender Equality : Pioneering computational thinking education with gender perspectives Robotics : Fault diagnosis systems for antenna arrays Key methodologies include Extreme Learning Machines, Support Vector Machines, and Deep Learning. He collaborates with teams in TELGalicia and TecAnDaLi networks.
Gianfausto SALVADORI is a University Researcher in the Department of Mathematics and Physics "Ennio De Giorgi" at the University of Salento, specializing in Probability and Mathematical Statistics (SSD MAT06). His office is located on the ground floor, room 325, at the Former Fiorini College - Via per Arnesano - Lecce. Dr. SALVADORI is an applied mathematician with research interests spanning Copulas, Extreme Value Theory, and stochastic modeling applications to environmental phenomena. His work focuses on modeling multivariate dependent random variables and analyzing extreme environmental events including rainfall, floods, droughts, and sea storms. He has been involved in environmental research since 1989, initially working on Chernobyl radioactive pollution and Universal Multifractals modeling, and since 2001 has specialized in Copulas methodology. His research activities include collaboration with hydrological engineers at the Polytechnic of Milan, participation in national and European projects related to extreme environmental phenomena, and academic contributions including co-authoring the book "Extremes and Copulas" published by Springer-Verlag in 2007. He has developed course materials on Extreme Value Theory, indicating active teaching responsibilities in this specialized area of statistics. Office hours are available by prior agreement via email. His contact information includes telephone +39 0832 29 7584 and email gianfausto.salvadori@unisalento.it. His professional activities demonstrate ongoing engagement in both theoretical statistical research and practical applications to environmental challenges.
Dr. Annette Christine Möller is a Professor at Bielefeld University's Faculty of Economics, where she leads the Chair of Data Science and is affiliated with the Department of Empirical Methods. She serves as a key researcher at the Bielefeld Center for Data Science (BiCDaS). Her contact information includes email (annette.moeller@uni-bielefeld.de) and phone (+49 521 106-4877). Her research focuses on: Developing statistical methods for weather forecast post-processing Copula-based modeling of multivariate dependencies Ensemble forecasting for climate risk mitigation Applications in renewable energy prediction Statistical computing and open-source tool development Her publication record (2019-2025) demonstrates strong emphasis on: Probabilistic weather forecasting techniques Vine copula applications in meteorology Statistical calibration of ensemble models Interdisciplinary work spanning climatology, epidemiology and education She leads significant research grants including: DFG project: 'Statistical post-processing of ensemble forecasts' (2018-2024) DFG project: 'Mitigating climate risks via copula-based forecast improvement' (2024-2026) She collaborates with the Technical University of Munich and maintains active research teams focused on statistical meteorology and climate informatics.
Mehmet İshak Yüce is a Professor in the Department of Hydraulics within the Faculty of Engineering at Gaziantep University. His career spans over 30 years, starting as a Research Assistant in 1993 and achieving professorship in 2021. He holds a PhD in Civil Engineering from the University of Manchester (2005), an MSc in Water Engineering from Istanbul Technical University (1995), and a BSc in Civil Engineering from Middle East Technical University (1992). Research Focus: Dr. Yüce specializes in hydrology, fluid mechanics, and climate impacts on water systems. His work integrates computational modeling, statistical hydrology, and sustainable resource management. Key themes include: Drought prediction using copula models and machine learning Hydrokinetic turbine design for renewable energy Hydraulic transients and pollutant transport Climate-driven hydrological variability in Mediterranean basins Awards & Recognition: Gaziantep University 2015 Second Best Doctoral Thesis Award Academic Leadership: He has supervised 6 doctoral and 32 master's students, focusing on hydrology, energy systems, and hydraulic infrastructure. His grants include projects on hydrokinetic turbines (2012-2022) and drought analysis (2015-2019), funded by national agencies. Administrative Roles: Former Dean of Engineering (2021), Institute Director (2020-2023), and multiple terms as Department Deputy Head.
Professor David B. Stephenson is a leading academic at the University of Exeter, serving as the founding director of the Exeter Climate Systems (XCS) research center since 2008. He holds a professorship in the Department of Mathematics and Statistics and combines expertise in statistical modeling with climate science to advance understanding of weather and climate processes. Education: 1st class BA in Physics (Oxford, 1985), PhD in Theoretical Particle Physics (Edinburgh, 1988) His research focuses on statistical modeling of climate systems, including forecast verification, extreme weather trends, and climate risk assessment. With over 180 publications and an H-index of 75, he co-authored the IPCC AR5 Chapter 14 and developed the Forecast Verification: A Practitioner's Guide . Recent research addresses critical climate questions: quantifying proximity to 1.5°C warming, analyzing climate modes of variability, and investigating drivers of extreme storm risks. His work bridges climate science and statistics to improve decision-making and reduce climate-related risks. As a Royal Meteorological Society Adrian Gill Prize winner (2012) and Royal Society Wolfson Research Merit Award holder (2015), Stephenson has fostered academic-industry partnerships like the Willis Research Network and Met Office Academic Partnership. He actively supervises PhD students and welcomes collaborations in climate modeling and risk analysis.
Prof. Dr. Bihrat Önöz serves as Head of the Civil Engineering Department and full-time faculty at Işık University's Faculty of Engineering and Natural Sciences. She specializes in water resources engineering with extensive contributions to hydrological modeling and drought analysis across Turkish river basins. Her academic credentials include: 1974-1977: B.Sc. in Civil Engineering, Ege University 1984-1986: M.Sc. in Hydraulics, Istanbul Technical University 1988-1992: Ph.D. in Water Engineering, Istanbul Technical University Research focuses on hydrological extremes, featuring pioneering work in statistical streamflow estimation for ungauged basins, multivariate drought indexing, and climate change impacts on water resources. Her methodologies integrate wavelet analysis, copula-based modeling, and machine learning to address water scarcity challenges in Mediterranean ecosystems. She actively supervises graduate research and leads hydrological projects, with recent work emphasizing seasonal drought prediction and reservoir management under changing climate conditions. Notable supervision includes 58 graduate students across doctoral and master's programs, with thesis topics spanning low-flow analysis, flood frequency modeling, and renewable energy-water nexus studies. Current projects include hydrostatistical flow estimation models for ungauged basins and drought characterization in Eastern Black Sea watersheds.
Tzavelas Georgios is an Associate Professor at the Department of Statistics and Actuarial Science, School of Finance and Statistics, University of Piraeus. With a distinguished academic career spanning several decades, he has established himself as a prominent researcher in statistical theory and methodology, with particular expertise in estimation theory, characterization problems, environmental statistics, and biostatistics. His educational background includes a Ph.D. in Mathematical Statistics (1994), Master of Arts (1991), both from the University of Maryland at College Park, and a Bachelor's Degree in Mathematics (1984) from the University of Patras. His academic journey began with exceptional promise, as he ranked in the top 5% of students throughout his undergraduate studies. Professor Tzavelas' research focuses on advanced statistical methodologies with applications across multiple domains. His work in estimation theory has produced significant contributions to understanding parameter estimation in complex models, particularly with biased and size-biased samples. His research in environmental statistics has addressed critical issues related to environmental monitoring and assessment, while his biostatistics work has contributed to medical research methodology. Recent research trends show a strong emphasis on weighted distributions, characterization theorems, and the application of statistical methods to biomedical and environmental data. His scholarly output demonstrates consistent productivity with numerous publications in prestigious journals including Journal of Applied Statistics, Biometrical Journal, Metrika, and Journal of Statistical Computation and Simulation. His work often bridges theoretical developments with practical applications, particularly in healthcare and environmental contexts. Exemplary Teaching Award, University of Maryland at College Park (1993) Ranked in the top 5% of students at University of Patras (1981-1983) Throughout his career, Professor Tzavelas has participated in numerous research programs funded by various institutions, addressing diverse topics from healthcare quality assessment to environmental statistics. His collaborative approach is evident in his extensive co-authorship with researchers across different disciplines. He has also contributed significantly as a reviewer for international journals including Applied Statistics, Journal of Statistical Computation and Simulation, and Communication in Statistics. His teaching portfolio spans both undergraduate and graduate levels, with courses in sampling methods, statistical estimation, clinical trials, and biostatistics. His research program continues to advance statistical methodology while addressing real-world problems in healthcare, environmental science, and social research.
Assistant Professor Aljaž Zalar is affiliated with the University of Ljubljana at the Faculty for Computer and Information Science . His research focuses on Real Algebraic Geometry , Truncated Moment Problems , and Matrix Polynomials , with applications in Operator Theory and Positive Linear Maps . PhD in Mathematics, University of Ljubljana (2017) MSc and BSc in Mathematics, University of Ljubljana (2013, 2011) Zalar's work bridges theoretical mathematics and computational applications, including copositive matrices , positive semidefinite matrix completions , and noncommutative polynomial positivity . His recent projects address truncated moment problems on curves and algebraic structures in optimization . His publications from 2016–2025 span journals like Linear Algebra and its Applications , SIAM Journal on Applied Algebra and Geometry , and Integrable Equations and Operator Theory , emphasizing polynomial operator analysis and matrix inequalities . Zalar supervises postdoctoral and graduate students, including PhD candidate Rajkamal Nailwal and Igor Zobovič, and mentors undergraduate researchers. He leads the ARIS grant project J1-60011 on real algebraic geometry approaches to moment problems.
Haeran Cho is Professor of Statistical Science in the School of Mathematics at the University of Bristol, holding a BSc and PhD in Statistics. Her research focuses on developing foundational methodologies for detecting structural changes in complex high-dimensional data streams, with applications spanning finance, environmental monitoring, and biomedical engineering. Her primary research interests include changepoint detection in non-sparse regression frameworks, factor model diagnostics for tensor time series, and robust nonparametric segmentation techniques. She pioneers approaches that handle heavy-tailed distributions, temporal dependence, and high-dimensional scaling—addressing critical limitations in classical change point theory through adaptive covariance scanning and multiscale inference frameworks. Professor Cho received the Research Prize in 2013 for her contributions to statistical theory. She currently leads the £1.2M EPSRC-funded project Statistical Foundations for Detecting Anomalous Structure in Stream Settings (DASS, 2024-2029), developing real-time anomaly detection systems for industrial applications. Previous projects include quantile factor modeling for high-dimensional time series (2019). Her software implementations ( CptNonPar , mosum , fnets ) have become standard tools in statistical computing, with over 500 citations. She actively collaborates through Horizon Europe initiatives and supervises postgraduate researchers in statistical methodology development.
Tenichi Cho is an Assistant Professor (without tenure) at the Center for Data Science, Waseda University, Japan. He specializes in sedimentary geochemistry, palaeoclimate reconstruction and compositional-data analysis, with a focus on Mesozoic oceanic anoxic events and chemical weathering proxies. Since 2023 he has led competitive JSPS grants and teaches an extensive portfolio of data-science courses across Waseda’s Global Education Center. Education: Ph.D. 2023, Waseda University, Graduate School of Creative Science and Engineering (Earth Sciences, Resources and Environmental Engineering) B.Sc. 2018, Waseda University, School of Education, Department of Science (Earth Science Major) Research Interests: Sedimentology, palaeoenvironmental change, chemical weathering indices, Cretaceous and Triassic climate extremes, global warming feedbacks, and multivariate statistical techniques applied to sedimentary compositional data. Recent Publications Trend: Cho’s 2022–2025 articles reveal a consistent trajectory in high-resolution chemostratigraphy, combining novel weathering indices (RW index) with redox-sensitive element and isotope analyses to decode Jurassic and Triassic environmental crises such as the Toarcian OAE and the Carnian Pluvial Episode. His work increasingly couples field-based sedimentary records with quantitative data-science tools. Scientific Awards: Best Oral Presentation, 37th IGC (2024) GeoSciAI2024 Award, JpGU (2024) Sedimentological Society of Japan Paper Award (2024) Waseda Early-Bird Best Collaborative Research Award (2022) Best Poster, IGCP 679 (2019) Grants & Projects: Cho currently heads two JSPS KAKENHI projects (2024-2027) targeting universal palaeoclimate proxies and carbon-cycle feedbacks, and has completed Waseda and Fukada Geological Institute grants on Cretaceous Asian weathering and Triassic volcanism–climate coupling. Teaching & Labs: He delivers over 30 undergraduate data-science and statistics courses each year at Waseda’s Global Education Center, spanning introductory literacy to advanced modelling with R and Python, and is affiliated with the university’s Center for Data Science research community.
Sergio Ortobelli Lozza is a full professor in Applied Mathematics at the Department of Management , University of Bergamo (Italy), where he coordinates the Master in Economics and Finance and PhD program in Management Accounting and Finance . With a Ph.D. in Computational Methods for Financial and Economic Forecasting and a Master's in Mathematics, his research focuses on applying probability theory and operational research to economics and finance. His scholarly work includes over 180 refereed publications in prestigious journals such as: European Journal of Operational Research Journal of Banking and Finance Insurance: Mathematics and Economics Mathematical Problems in Engineering His research emphasizes: Portfolio optimization with applications to crisis scenarios Stochastic dominance for sector-based investments De Novo programming in renewable energy location systems Network reputation analysis in financial contexts Scientific achievements include: Best paper award at EBES Athens (2010) Principal Investigator for GACR research projects Editorial board member of Mathematical Problems in Engineering Contributor to PRIN research teams He actively participates in academic conferences and has served on organization committees for events like Managing and Modelling of Financial Risks (2013-2019) and ICSP XIII (2013). Current contact: sergio.ortobelli@unibg.it | ORCID: 0000-0003-4983-8165
Dr. Ralf Weisse is a leading researcher at the Institute of Coastal Systems - Analysis and Modeling within Helmholtz-Zentrum Hereon, Germany. As Department Head for Coastal Climate and Regional Sea Level Changes, his work focuses on Marine climate dynamics Storm surge mechanisms Ocean wave modeling Regional sea level change analysis Climate change adaptation for coastal regions . His research spans three decades with significant contributions to understanding German Bight storm surges , Baltic Sea extreme events , and North Sea coastal dynamics . Recent work combines machine learning with Earth system models for decadal-scale predictions. Key projects include: WAKOS (2020–present): Climate services for coastal adaptation EXTREMENESS (2016–2019): Storm surge impact assessment ALADYN (2021): Tidal dynamics in German Bight His publications address compound flood events , statistical downscaling , and historical storm surge comparisons . Affiliated with Universität Hamburg and DKRZ, he applies neural networks and ensemble climate modeling to improve coastal protection strategies.
Mehrdad Naderi is a Lecturer in Statistics at the Department of Mathematics, Physics, and Electrical Engineering , Northumbria University . His academic journey includes a PhD in Mathematical Statistics from Shahid Bahonar University of Kerman (2017) and postdoctoral research at National Chung Hsing University (Taiwan), Ferdowsi University of Mashhad (Iran), and University of Pretoria (South Africa). Education: PhD in Mathematical Statistics, Shahid Bahonar University of Kerman (2017) His research focuses on applied statistical inference with emphasis on classification , cluster analysis , factor analysis , finite mixture models , and EM algorithm for robust estimation. He has contributed to multivariate and matrix-variate analysis, particularly in handling outliers and asymmetrical data structures. Recent work includes three-way data clustering using matrix-variate normal distributions and robust Bayesian inference for censored mixture models. His publications demonstrate expertise in distribution theory, statistical computation, and applications to financial data, environmental modeling, and astrophysics. Current collaborations span multiple institutions, focusing on heavy-tailed distributions and computational methods for complex data structures.
Clara Viseu is a Senior Lecturer at Coimbra Business School, Polytechnic University of Coimbra, with dual research appointments at the Centre of Mathematics and Applications (University of Beira Interior) and CEOS.PP (Polytechnic of Porto). Her academic credentials include: PhD in Mathematics, University of Beira Interior Master in Probability and Statistics, University of Lisbon Her research integrates advanced statistical methodologies with business applications, specializing in Econometrics for economic forecasting, Multivariate Statistical Analysis for complex dataset interpretation, and Extreme Value Theory for modeling rare financial/business events. This triad of expertise positions her at the intersection of theoretical statistics and practical business decision-making. She actively contributes to academic governance through committee memberships: Statistics Committee of IPC Scientific Committee for Accounting and Public Management Scientific Committee for Data Science for Management As an educator, she supervises Master's dissertations with emphasis on quantitative business research, guiding students through rigorous statistical methodology application while maintaining active scholarly publication in her core disciplines.
Marian-Andrei Rizoiu is an Associate Professor leading the Behavioral Data Science lab at the University of Technology Sydney's Data Science Institute, Faculty of Engineering and Information Technology. He holds concurrent appointments as an Honorary Lecturer at Australian National University and Honorary Research Scientist at Data61. Previously, he has held visiting professor positions at Imperial College London, Jean Monnet University, and Max Planck Institute for Software Systems. Associate Professor in Behavioral Data Science, UTS Data Science Institute (Jan 2024 - present) Senior Lecturer in Behavioral Data Science, UTS Data Science Institute (Jul 2021 - Jan 2024) Lecturer in Computer Science, UTS Faculty of Engineering and Information Technology (Feb 2019 - Jul 2021) Dr. Rizoiu's research focuses on interdisciplinary work crossing computer and social sciences, blending psycholinguistics, digital communication, and stochastic modeling to understand human attention dynamics online, the emergence of influence, and opinion polarization. His key contributions include developing theoretical models for online information diffusion that can account for complex social phenomena, and building skill-based real-time occupation transition recommender systems that link social media-predicted personality profiles with occupation skill requirements. His research outputs reveal strong trends in misinformation detection, social influence measurement, and online radicalization pathways. Recent publications demonstrate sophisticated modeling approaches including state space models for early misinformation prediction, multivariate Hawkes processes for analyzing partially interval-censored data, and ideology detection pipelines. His work spans computational social science, machine learning, and practical applications for countering harmful online content. Excellence Award and Academic of the Year at the 2023 Australian Defence Industry Awards ADMA'22 Best Application Paper Dr. Rizoiu has successfully secured over $2.9 million in research funding from selective funders including Meta Research, Defence Science and Technology Group, Department of Home Affairs, and Defence Innovation Network. He has supervised 3 PhD students to completion and more than 10 Honours students, most achieving High Distinction. His research has been applied in real-world contexts including serving as an expert for NSW government's Defamation Law Reform and providing evidence for Australian Federal Senate inquiry into media diversity. He leads the Behavioral Data Science lab which focuses on modeling human behavior in online environments, with particular emphasis on mis- and disinformation detection and labor market analysis. The lab has developed tools like TRACK, UTS OPEN's software for recommending personalized learning pathways, used by over 750 students and professionals.
