Dr. HRMJ (Ron) Wehrens is the Business Unit Manager of Biometris at Wageningen University & Research. He holds a PhD in Chemometrics from Radboud University Nijmegen (1994). Previously, he served as an assistant and associate professor at the Universities of Twente and Nijmegen, and led the Biostatistics and Data Analysis group at Fondazione Edmund Mach in Italy (2010–2014). His expertise spans biometrics, statistical methods, multivariate analysis, algorithms, and software engineering, focusing on quantifying biological processes through advanced data analysis techniques. Expertise Areas: Classification & Cluster Analysis Regression Analysis Multispectral Imagery Algorithm Development Statistical Modeling Professional Availability: Full-time role with availability across all weekdays (see schedule details on official profile).
Karsten Webel is a Research Professor at the Deutsche Bundesbank's Research Centre, part of the Directorate General Data and Statistics. His work focuses on seasonal adjustment methodologies, stochastic processes, machine learning applications, and index theory. He contributes to the development and implementation of JDemetra+, the official software for seasonal adjustment in Europe. Webel's research emphasizes infra-monthly time series analysis, including daily and weekly economic indicators, and has published extensively on topics like random forest-based seasonality tests and data-driven model selection. Education and affiliations are not explicitly detailed in the provided text, but Webel's professional activities include presentations at conferences such as the International Conference on Establishment Statistics and the Joint Statistical Meetings. His research has been disseminated through peer-reviewed journals, conference proceedings, and book chapters, with a strong emphasis on methodological advancements in time series analysis and econometrics. Key research interests include seasonal adjustment techniques for non-traditional time frequencies, stochastic processes, and the application of machine learning algorithms to economic data. Webel collaborates with institutions like Insee and the National Bank of Belgium, contributing to the Eurostat-recommended seasonal adjustment software framework. His work bridges theoretical statistical methods with practical applications in central banking and official statistics.
Paula Brito is an Associate Professor at the School of Economics of the University of Porto, where she teaches Statistics and Multivariate Data Analysis at undergraduate and post-graduate levels. She is a member of the Artificial Intelligence and Decision Support Lab (LIAAD) at INESC-TEC. She holds a PhD in Applied Mathematics from the University of Paris Dauphine (1991). Her research focuses on symbolic data analysis, including methodologies for multidimensional complex data (e.g., distributional data), clustering, and statistical modeling. She has contributed to applications in environmental monitoring, social networks, labor markets, and anomaly detection. Key projects include developing parametric models for distributional data, symbolic principal component analysis for air quality studies, and community detection in interval-weighted networks. Her work on Luxembourg’s labor market highlights immigrant group dynamics using symbolic data techniques. She has supervised multiple theses on topics such as anomaly detection in financial markets, symbolic pattern mining in networks, and multiclass classification of distributional data. Paula is affiliated with LIAAD, fostering interdisciplinary research in artificial intelligence and decision support systems.
Charles R Doss is an Associate Professor in the Department of Statistics at the University of Minnesota, Twin Cities, within the College of Science and Engineering. His research is centered on nonparametric inference, shape-constrained estimation, and continuous treatment effects, with applications in public health and precision medicine. He has led multiple funded research projects from the National Science Foundation and the Department of Health. Research Interests: Dr. Doss specializes in nonparametric methods, particularly under shape constraints such as convexity and monotonicity. His work includes developing theory and methodology for optimal treatment regimes, continuous treatment effects, and robust inference. Applications span public health, including wastewater surveillance for pathogens like SARS-CoV-2, and statistical learning in high-dimensional settings. The recent publications show a strong trend in causal inference, robust statistical methods, and interdisciplinary applications, particularly in public health and machine learning. His work often involves theoretical development with practical implementation, as seen in projects on wastewater surveillance and multivariate regression models. Scientific Awards: No specific awards listed in the provided text. Advising and Grants: Dr. Doss has been the Principal Investigator on three National Science Foundation grants and a Co-Investigator on a Department of Health project related to wastewater surveillance. His funding history from 2017 to 2026 reflects sustained support for methodological and applied statistical research. While no students are listed, his role as PI suggests active mentorship and research leadership. Labs and Teams: He collaborates with interdisciplinary teams, including public health officials and researchers in virology and environmental science, particularly through the wastewater surveillance project. His network includes researchers from various institutions and disciplines, indicating a collaborative research environment.
Jordan D. Klein is a Research Scientist at the Max Planck Institute for Demographic Research (MPIDR), affiliated with the Department of Digital and Computational Demography and conducting research in the Laboratories of Migration and Mobility and Population Dynamics and Sustainable Well-Being. Education: PhD in Demography, Princeton University (2024) MPH in Epidemiology and Biostatistics, Tufts University School of Medicine (2017) BS in International Relations and Biology, Tufts University (2016) Jordan Klein is a demographer and social epidemiologist whose research centers on the spatio-temporal evolution of mortality disparities, particularly in the context of emerging and re-emerging infectious diseases. His work integrates migration, mobility, climate change, and social inequities as key drivers of health outcomes. He employs a wide array of data sources—such as official vital statistics, surveys, digital trace data, and geospatial information—and advanced methodologies including machine learning, mechanistic modeling, causal inference, and demographic measurement. His recent publications reflect a strong focus on the societal impacts of the COVID-19 pandemic, including modeling socioeconomic inequalities in mortality, estimating migration shifts using social media, and developing strategies for epidemic control in informal settlements. He also investigates broader issues like climate change and health transitions in urban Madagascar and data challenges in ancestry categorization in U.S. surveys. Jordan is committed to open science, sharing data and code publicly. His work bridges demography, epidemiology, and computational social science, contributing to policy-relevant insights on health equity and population dynamics.
Nina Deliu is a Tenure-track Assistant Professor in Statistics at Sapienza University of Rome's MEMOTEF Department, with joint appointments as a Visiting Faculty Researcher at Google and Visiting Researcher at the MRC-Biostatistics Unit, University of Cambridge. She holds editorial roles at Trials journal and YoungStatS, and maintains active collaborations with institutions including the University of Toronto, National University of Singapore, ISTAT, NADO Italia, and FAO. Education: PhD in Methodological Statistics, Sapienza University of Rome (2021) MSc in Statistics and Decisions, Sapienza University of Rome (2017) MSc in Mathématiques, Informatique, Décision et Organisation, Université Paris Dauphine (2016) Research spans Bayesian inference, reinforcement learning, multi-armed bandits, adaptive experimental design, copula models, and uncertainty quantification, with applications in healthcare, education, and public health. Her work bridges theoretical statistics with real-world challenges in biostatistics, mobile health interventions, and digital education platforms. Publications focus on adaptive experimentation frameworks, response-adaptive clinical trials, reinforcement learning in healthcare, and copula-based statistical methods. Recent work emphasizes finite-sample error control, zero-inflated count data modeling, and multivariate dependency analysis. Awards: XPRIZE $1M Digital Learning Challenge (2023) for the Adaptive Experimentation Accelerator project Research Projects: The role of self-reported health outcomes in cancer risk prediction using UK Biobank data Contextual Multi-armed Bandits for Developing Personalized Mobile Health Interventions Leads collaborations through the IAI Lab (University of Toronto) and coordinates interdisciplinary teams for projects in statistical methodology, health interventions, and official statistics innovation.
Pedro Simões Coelho is a Full Professor and President of the Scientific Board at NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, where he also serves as an Integrated Researcher in the Information Management Research Center (MagIC). He is a senior expert for the European Commission in statistical methods and sampling techniques, and holds leadership roles in several national and European bodies, including the European Master of Official Statistics (EMOS) board and the Portuguese Health Technologies Commission. His research interests lie at the intersection of statistics, data analysis, and public policy. He specializes in sampling techniques, structural equation modeling, survey methodology, and data quality, with applications in health economics, business intelligence, and official statistics. His work bridges theoretical statistical innovation with real-world policy impact, particularly in health and administrative systems. His recent publications demonstrate a strong trend toward integrating artificial intelligence and advanced modeling techniques into public administration and health policy. Topics include AI-based detection of legislative burdens, economic burden assessments in dermatology, consumer segmentation via social media, and cardiovascular health optimization. These works reflect a multidisciplinary approach combining statistics, machine learning, and domain-specific knowledge to solve complex societal challenges. Senior Expert, European Commission (Statistical Methods & Sampling) Member, EMOS Board Member, Ischools Accreditation Committee Head of Information and Statistics, NOVA Clinical Research Unit (NOVA CRU) Former President, Portuguese Association for Classification and Data Analysis (CLAD) Former Member, Portuguese High Council for Statistics (CSE) Former President, Fiscal Board, CESD-Lisboa He has supervised numerous graduate and undergraduate courses and has been a consultant and trainer for major institutions including Eurostat, the Portuguese Statistical Office, and the Portuguese Central Bank. His work includes over 100 peer-reviewed publications and around 200 research projects resulting in more than 500 reports. He has delivered nearly 100 invited talks and conference presentations worldwide. He is actively involved in research labs and teams such as MagIC and NOVA CRU, where he leads initiatives in data-driven public health and statistical innovation. His ongoing projects focus on AI for policy assessment, sustainable health systems, and advanced statistical modeling for small area estimation and data integration.
Tracey Holloway serves as the Jeff Rudd and Jeanne Bissell Professor of Energy Analysis and Policy at the University of Wisconsin-Madison, jointly appointed in the Nelson Institute for Environmental Studies and the Department of Atmospheric and Ocean Sciences. She leads the Holloway Group at the Center for Sustainability and the Global Environment (SAGE), focusing on the intersection of air quality, energy, climate, and public health. Dr. Holloway directs NASA's Health and Air Quality Applied Sciences Team (HAQAST) and chairs the Energy Analysis and Policy graduate certificate program. Her academic foundation includes a Sc.B. with honors in Applied Mathematics from Brown University (1995), a Ph.D. in Atmospheric and Oceanic Sciences with a graduate certificate in Science, Technology, and Environmental Policy from Princeton University (2001), and a post-doctoral fellowship at Columbia University's Earth Institute with the Mailman School of Public Health (2001-2003). Dr. Holloway's research integrates satellite remote sensing with atmospheric modeling to address critical air quality and public health challenges. Her work emphasizes the connections between energy systems, climate change, and pollution impacts, particularly through the application of NASA satellite data for real-world decision support. She develops methods to translate satellite observations into actionable insights for air quality management and health protection, with growing focus on environmental justice implications of emission reduction strategies. Recent publications reveal a strong trajectory toward multi-pollutant analysis, climate-air quality interactions, and quantification of health co-benefits from clean energy transitions. Her research spans urban to national scales, increasingly incorporating geostationary satellite capabilities like TEMPO for high-resolution monitoring, while maintaining emphasis on practical applications for policymakers and public health officials. Major recognitions include: Member of the National Academy of Medicine Ascent Award from the American Geophysical Union Atmospheric Sciences Section Multiple awards for science outreach, diversity, and mentoring As an educator and mentor, Dr. Holloway co-founded the Earth Science Women's Network (ESWN), providing critical support for early-career women in geosciences through structured mentoring programs. Her leadership in NASA HAQAST exemplifies her commitment to connecting scientific research with stakeholder needs, while her role chairing the Energy Analysis and Policy certificate program shapes the next generation of energy policy experts through interdisciplinary training. The Holloway Group operates as a dynamic research hub within SAGE, collaborating with federal agencies, international organizations, and community stakeholders to ensure research relevance. Current initiatives include developing satellite-based air quality indicators for public health applications, analyzing energy transition impacts on pollution disparities, and creating decision-support tools for sustainable urban planning.
Dr. Iñaki Aliende is an Assistant Professor at the Complutense University of Madrid , affiliated with the Faculty of Economics and Business and the Department of Applied Economics, Public Economics, and Economic Policy. He serves as Visiting Lecturer at the University of Portsmouth and is a member of the UCM Research Group No. 940051 on Data Analysis in Social Studies and the Institute of Statistics and Data Science. His academic roles include co-directing UCM's Permanent Training Certificate in Behavioral Economics, editing the Handbook on Behavioral Economics , and teaching in master's programs, summer schools, and postgraduate courses across multiple institutions. Education Bachelor's in Economics, Complutense University of Madrid (UCM) PhD in Data Science (cum laude, International Mention), UCM Dr. Aliende's research focuses on applying Data Science to Economics, Social Studies, and Behavioral Economics . His work explores football refereeing career dynamics, automation's impact on labor markets, corporate reputation strategies, and econometric teaching tools. Publications span journals like Economics Letters , PLOS ONE , and International Journal for Applied Behavioral Economics , emphasizing empirical analysis, survival models, and behavioral policy frameworks. Recent research trends include: Climate change financial disclosures in energy sectors Behavioral economics in sports officiating Automation's long-term labor market effects Corporate reputation analytics Interactive econometric teaching systems (SADMER) Football referee retention strategies He supervises doctoral theses and undergraduate/masters dissertations at UCM while maintaining a popular YouTube channel for economics and data science education. His professional experience as a consultant includes projects with the World Bank and Grupo Cegos, focusing on Business & People Analytics.
Tuba Bircan is a Professor at the Faculty of Social Sciences , Department of Sociology at Vrije Universiteit Brussel (VUB). She holds leadership roles including Department Chair and Head of Research Group within the Brussels Institute for Social and Population Studies. Her research focuses on migration, refugees, gender inequalities, AI governance, and Big Data applications. She has over 15 years of experience in quantitative/mixed methods and policy advocacy. Research Interests: Bircan explores migration policies, refugee integration, AI ethics, gender disparities, and multilateral governance. She pioneers interdisciplinary projects like DE-CONSPIRATOR (countering information suppression) and From Camp to Campus (supporting refugee education). Her work bridges theory and practice, emphasizing evidence-based policy. Recent Trends in Publications: Bircan’s 2020s work highlights AI’s societal impacts, climate-driven displacement, and innovative data sources (mobile phone records, remote sensing). She critiques algorithmic bias and advocates for equitable migration policies using Big Data. Grants & Projects: Leads 9 active projects including EU-funded initiatives. Notable: SRP-Onderzoekszwaartepunt (demographic challenges), Brussels Interdisciplinary Research centre on Migration and Minorities (BIRMM). Labs/Teams: Coordinates the Brussels Institute for Social and Population Studies, fostering collaborations on migration, digital governance, and social equity.
Sreekanth Mallikarjun is a Lecturer at the School of Data Science and holds a joint appointment with the McIntire School of Commerce as a Visiting Scholar at the University of Virginia. He also serves as Chief Data Scientist at Reorg, a global provider of credit intelligence, data, and analytics, bridging academia and industry in data science and business applications. Education: Ph.D. in Engineering Technology and Policy and Innovation, Stony Brook University M.S. in Operations Research, Stony Brook University B.S. in Mechanical and Industrial Engineering, Osmania University His research focuses on leveraging data science to solve complex business problems, particularly in finance and operations. Key areas include machine learning, natural language processing, data mining, statistics, and operations research, with an emphasis on extracting insights from both structured and unstructured data. He explores innovative methodologies to improve model execution, scalability, and reliability in enterprise environments. The recent articles highlight a strong trend in applying data science to financial domains, emphasizing model simplicity, data quality, and organizational scalability. His work consistently addresses practical challenges in deploying and maintaining data science models in real-world settings, particularly within financial institutions and large organizations. Scientific Awards and Recognitions: Official Member, Forbes Technology Council Mallikarjun advises on data science strategy and model deployment, though no formal advisees are listed. He has not disclosed specific grants, but his industry role at Reorg and academic position suggest engagement in applied research and innovation. His contributions span thought leadership through Forbes, academic teaching, and high-impact industry applications. He is actively involved in promoting best practices in data science through publications and professional networks, contributing to the broader discourse on effective data science implementation in business contexts.
Glenn Björklund is an Associate Professor and Senior Lecturer at Mid Sweden University, working within the Department of Health Sciences. He is based in Östersund at room 307b, with contact information glenn.bjorklund@miun.se and telephone +46 (0)10-1428149. Dr. Björklund is affiliated with The Swedish Winter Sports Research Centre, which serves as a key resource for Olympic winter sports development and monitoring. Dr. Björklund completed his doctoral thesis "Metabolic and Cardiovascular Responses During Variable Intensity Exercise" in 2010 at Mid Sweden University. His academic journey demonstrates progression from traditional exercise physiology to advanced interdisciplinary sports analytics. His primary research interests focus on biathlon performance analysis, cross-country skiing physiology, training load measurement, and sports biomechanics. Dr. Björklund employs advanced statistical methods including principal component analysis, kernel density estimation, and machine learning to analyze athletic performance across multiple sports. His work has expanded from traditional winter sports to include swimming, ice hockey, and tennis, demonstrating a growing interdisciplinary approach to sports science. Dr. Björklund's recent publication trends show a strong emphasis on data-driven performance prediction across multiple sports. His work increasingly incorporates machine learning techniques and advanced statistical modeling to extract meaningful insights from complex performance data, with applications spanning both winter and summer sports disciplines. Focus on One Swimming Stroke or Compete in Multiple (2025) Kernel Density Estimation for training intensity visualization (2025) Performance indicators in biathlon relay (2025) Discrepancies in training load measurements (2024) PCA for swimming performance prediction (2024) Dr. Björklund has received recognition through numerous collaborative research projects including ASPE-P (asthma, medication and performance), Elite performance in cross-country skiing and biathlon, and Pacing strategies for improved skiing performance. His work demonstrates consistent funding support and collaboration with researchers across Scandinavia and internationally. His research is conducted through The Swedish Winter Sports Research Centre, where he leads projects examining heart rate, oxygen uptake, and immune response in endurance exercise. Dr. Björklund's team utilizes advanced biomechanical analysis, physiological monitoring, and data science techniques to translate scientific findings into practical applications for athletes and coaches.
Teemu Hannu Tapani Härkönen is a Postdoctoral Researcher at Aalto University's Department of Electrical Engineering and Automation. Affiliated with the Sensor Informatics and Medical Technology research group, his work focuses on advanced signal processing and statistical modeling in biomedical contexts. Position: Postdoctoral Researcher Email: teemu.h.harkonen@aalto.fi Research Interests: His expertise spans sensor informatics, medical technology, and spectral analysis, with current projects applying Gaussian processes to chemometric challenges in laboratory systems. A recent publication (2025) in Chemometrics and Intelligent Laboratory Systems demonstrates his innovative approach to signal processing. Contact: For collaboration or inquiries, reach him via his official Aalto email.
Glenn Magermann is Associate Professor of Economics (with tenure) at the Solvay Brussels School of Economics and Management, Université Libre de Bruxelles, where he leads research through the ECARES institute. In 2025, he serves as an academic consultant to the European Central Bank Research Network 'Challenges to Monetary Policy'. His core affiliations include CEPR (Trade and IO programs), CESIfo, Complex Adaptive Supply Networks Research Accelerator, Institute of European Studies, I3h, and VIVES KU Leuven. His research agenda centers on production networks, trade dynamics, and micro-to-macro economic linkages , with specific focus areas including global value chains, firm heterogeneity, inequality, and crisis impacts. Recent publications analyze deglobalization effects, COVID-19 policy responses, regional disparities, and supply chain restructuring. He leads or collaborates on major grants: BELSPO HAIOPOLICY (lead promotor, €881K, 2023-2027): Inequality in production networks and consumption heterogeneity AIML4OS (collaborator, €4M, 2024-2028): Machine learning in official statistics BELSPO BE-PIN (collaborator, €2M, 2023-2027): Post-COVID policy collaboration
Dr. Nora Würz serves as an Academic Councillor (Senior Lecturer) at the Chair of Statistics and Econometrics within the Faculty of Social Sciences, Economics and Business Administration at the University of Bamberg since 2022, where she leads teaching and research initiatives in advanced statistical methodologies. Her academic journey includes a Bachelor's degree in Integrated Life Sciences from Friedrich-Alexander University Erlangen-Nuremberg (2015) and a Master's degree in Statistics through the Joint Masters Program of Berlin universities (2017). She completed her PhD at Freie Universität Berlin (2017-2022) under the German Academic Scholarship Foundation, with her dissertation winning the Federal Statistical Office's 2023 'Statistical Science for the Society' prize. Dr. Würz specializes in small area estimation under data constraints, pioneering integrations of machine learning (particularly random forests) with traditional statistical models. Her research addresses critical gaps in poverty mapping, regional unemployment estimation using mobile network data, and construction of consumer price indices, with strong emphasis on real-world applications in official statistics and policy analysis. Her publication trajectory reveals a strategic evolution toward leveraging non-traditional data sources (mobile networks, aggregated census data) and advanced computational techniques to solve persistent challenges in regional statistics, particularly where population microdata is inaccessible. This work has established new methodological standards for income indicator estimation and small area modeling transformations. Her scientific recognition includes: Statistical Science for the Society prize (Federal Statistical Office, 2023) Young Scientist Award (German Statistical Society, 2023) Gerhard Fürst Prize (Federal Statistical Office, 2018) Dr. Würz contributes significantly to statistical infrastructure through R package development (saeTrafo, povmap) and methodological guidelines for European statistical projects. Her research program receives ongoing support from official statistical agencies and focuses on translating methodological innovations into practical tools for national statistical offices. As core faculty within the Chair of Statistics and Econometrics, she collaborates with Prof. Timo Schmid on European projects related to small area estimation, while maintaining active partnerships with the Federal Statistical Office and UK Office for National Statistics through EMOS (European Master in Official Statistics) initiatives.
