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Michael SiegelView profile

Professor

• Firearm violence prevention
• Alcohol policy
• Tobacco control
• +4 more

Michael Siegel is a Professor in the Department of Public Health and Community Medicine at Tufts University School of Medicine. Previously, he spent 26 years at Boston University's School of Public Health. His research focuses on firearm violence, alcohol, tobacco, and racial inequities, with an emphasis on corporate influences on health through advertising and marketing. He also examines structural racism's role in health disparities. Teaching areas include public health advocacy, social and behavioral sciences, health communication, and social marketing. He has served as an expert witness in litigation against tobacco, chemical, and pharmaceutical industries. Education: Master of Public Health (1992), UC Berkeley School of Public Health Doctor of Medicine (1990), Yale University School of Medicine Research Interests: Firearm violence prevention, alcohol and tobacco policy, structural racism analysis, health communication strategies. His work bridges corporate influence analysis and public health advocacy, with recent emphasis on racial inequities' systemic roots. Grants: Funded by 97 Percent Org Inc. (2022-2022) for gun safety policy research Robert Wood Johnson Foundation grant (2021-2021) on firearm policy communication Multiple NIH grants analyzing firearm policy efficacy and racial disparities Teaching: Courses include Public Health Advocacy, Social Marketing, Health Communication Theory, and Strategies for Public Health Advocacy. Recent teaching spans 2022–2025 with focus on practical public health interventions and advocacy.

Andreas AlfonsView profile

Associate Professor

• Statistics
• Machine Learning
• Psychometrics
• +3 more

Dr. Andreas Alfons is an Associate Professor in the Department of Econometrics at Erasmus School of Economics, Erasmus University Rotterdam. His research focuses on robust statistical methods, machine learning, psychometrics, and software development for high-dimensional data. He leads the NWO Vidi project on robust analysis of rating-scale data and contributes to the interdisciplinary project on digital decision support. He is an editor for the Journal of Statistical Software and Journal of Data Science, Statistics, and Visualization. His research interests include robust statistical learning, high-dimensional data analysis, and open science practices. Key contributions include R packages like robmed , robustHD , and simFrame . Recent work addresses careless responding in surveys and robust mediation analysis. Publications span journals such as Computational Statistics & Data Analysis , Econometrics and Statistics , and Journal of Statistical Software . His work emphasizes reproducibility and software tools for statistical analysis.

Thomas WahlView profile

Associate Professor

• Coastal Flood Risk
• Sea Level Rise
• Storm Surges
• +7 more

Thomas Wahl is an Associate Professor in the Department of Civil, Environmental, and Construction Engineering at the University of Central Florida (UCF), within the College of Engineering and Computer Science. He leads the Coastal Risks and Engineering (CoRE) Lab and is a key member of the UCF Coastal Systems Faculty Cluster, focusing on climate-driven coastal hazards. Education: Diploma in Civil Engineering, University of Siegen, Germany Ph.D. in Civil Engineering, University of Siegen, Germany (2012) His research centers on coastal flood risk , sea level rise , storm surges , and compound climate extremes . He investigates how multiple hazards—such as high tides, storm surges, rainfall, and river discharge—interact to create extreme flooding, especially under climate change. His work aims to develop actionable science for coastal resilience and adaptation planning. The recent publications (2022–2023) highlight a strong trend in analyzing compound flooding, sea-level extremes, and their socio-environmental impacts across the U.S., China, Europe, and global coasts. His research integrates observational data, climate modeling, and multivariate statistics to assess current and future risks. Scientific Awards: Walter L. Huber Civil Engineering Research Prize, ASCE (2023) NSF CAREER Award (2022) Early Career Scientist Medal, IAPSO (2021) Outstanding Early Career Scientist Award, EGU (2018) NASA Early Career Investigator Award (2018) Wahl advises graduate students and leads a dynamic research group in the Coastal Risks and Engineering (CoRE) Lab , which studies extreme sea levels, compound events, and coastal risk modeling. His research is supported by major grants from NSF, NASA, and other federal agencies. He has collaborated extensively with institutions including the University of South Florida, University of Southampton, and international climate research centers.

• Artificial Intelligence in Radiology
• Machine Learning
• Health Informatics
• +2 more

Dr. Axel W. E. Wismüller is a Professor of Radiology and Biomedical Engineering at the University of Rochester Medical Center . As Director of the AI Radiology Laboratory , he uniquely combines clinical practice in cardiothoracic/body imaging with pioneering research in Artificial Intelligence (AI) for medical imaging . Holding MD, MSc (Physics), and PhD (Electrical Engineering) degrees from German institutions, he maintains dual US and German medical licenses and board certifications. Ludwig-Maximilians-Universitaet München (1990-1996) Technical University of Munich (MD 1992, PhD 2006) Ludwig Maximilians University Munich (Residency 1997-2005) His research focuses on machine learning and network connectivity analysis in neuroimaging, particularly for HIV-associated neurocognitive disorders and multiple sclerosis . He invented the XOM algorithm (patented in US/EU) and developed AI systems for chest radiograph analysis , breast cancer imaging , and osteoporosis diagnostics . His work on large-scale Granger causality has transformed brain network analysis methodology. Scientific awards include SPIE Senior Membership (2016), NIH/NIDA R01 funding (2012-2015), and NYSTAR public-private partnerships . Supervising over 30 PhD/MSc students across engineering, computer science, and medicine , he bridges clinical and computational disciplines through active roles in ACR, RSNA, and SPIE .

Eldad DavidovView profile

Professor

• Quantitative Methods
• Empirical Social Research
• Cross-Cultural Comparisons
• +5 more

Eldad Davidov is a Professor of Methods of Comparative Cross-National Social Research at the Institute of Sociology and Social Psychology, University of Cologne. He previously held professorships at the University of Zurich (2009-2016) and served as President of the European Survey Research Association (ESRA, 2015-2017). His research focuses on quantitative methods, cross-cultural comparisons, structural equation modeling, and attitudes/values analysis. Davidov has held visiting professorships at Mannheim and Cologne, and his work emphasizes methodological rigor in social research. PhD: Social Sciences, University of Giessen (2004) MA: Economics, Tel Aviv University (1996) BA: Economics, Tel Aviv University (1995) His research interests include: Measurement equivalence in cross-national surveys Value orientations and their societal implications Immigration attitudes and prejudice mechanisms Longitudinal survey data analysis Key publication themes involve analyzing immigration opposition, human values measurement, and methodological advancements in cross-cultural research. His work bridges theory and practice, contributing to the European Social Survey and policy-relevant studies on integration policies. Davidov is affiliated with the Center for Social and Economic Behavior (C-SEB) and contributes to the ECONtribute Cluster of Excellence. His research addresses societal challenges through interdisciplinary collaboration, emphasizing evidence-based approaches to public policy.

Youngsoo ShinView profile

Assistant Professor

• Human-Centered Artificial Intelligence
• User Experience (UX) Design
• Cognitive Science
• +5 more

Dr. Youngsoo Shin is an Assistant Professor at the Seidenberg School of Computer Science and Information Systems at Pace University. His academic foundation spans psychology, integrated design, cognitive science, and human behavior, with degrees from Yonsei University and Cornell University. PhD in Human Behavior & Design, Cornell University (2023) MS in Cognitive Science & Engineering, Yonsei University (2015) BA in Psychology and BS in Human Environment & Design, Yonsei University (2013) Dr. Shin's research focuses on human-centered AI , developing adaptive systems to enhance user experience and AI performance. His work examines emotional, social, and cultural impacts of human-AI interactions, with an emphasis on ethical alignment and prosocial behavior design . Recent publications highlight trends in hyper-personalized recommender systems , behavioral intervention technologies , and UX design guidelines for time management systems. Dr. Shin has taught courses such as Human-AI Interaction, Cognitive Science & Technology, and Product Development Project, reflecting his interdisciplinary expertise in user experience , data science , and cognitive modeling .

Violaine Saint-AndréView profile

Researcher

• Computational Biology
• Immunology
• Epigenetics
• +3 more

Violaine Saint-André is a Researcher at the Institut Pasteur's Bioinformatics and Biostatistics Hub, where she leads computational biology research focused on immune response determinants through the Milieu Intérieur project. She holds a PhD in Molecular Biology/Genetics from Université Paris VI and an MSc in Bioinformatics from Université Paris VII. Her research investigates transcriptional regulatory networks, epigenetic mechanisms, and multivariate analysis of immune variability. Key interests include: Gene expression regulation and epigenetic memory of environmental exposures Determinants of human immune response heterogeneity Computational methods for multi-omics data integration Impact of smoking on long-term immune adaptation Her publications demonstrate strong focus on immunology and computational biology, with recent work uncovering smoking's persistent epigenetic effects on immunity and cytokine regulation. Research frequently involves high-dimensional data analysis across tuberculosis, COVID-19, and HIV contexts. She contributes significantly to academic training through courses at Institut Pasteur, Centrale-Supelec, and École Centrale de Nantes, covering ChIP-seq analysis, multivariate statistics, and health applications of AI. Actively participates in conference organization and student evaluation committees.

• Scientometrics
• Bibliometrics
• Higher Education Evaluation
• +3 more

Tanja Yevremov serves as Associate Professor at the Department of Psychology, Faculty of Philosophy, University of Novi Sad, where she has been employed since 2002. She teaches Advanced Statistics and Multivariate Analysis for Psychology students and Fundamentals of Statistics for Social Work students, while coordinating international cooperation through the Erasmus+ program. Her educational background includes: Bachelor's degree in Psychology (1996), Faculty of Philosophy, University of Novi Sad Master's degree in Psychology (2008), Faculty of Philosophy, University of Novi Sad Ph.D. in Psychology and Scientometrics (2013), Faculty of Philosophy, University of Novi Sad Dr. Yevremov's research centers on scientometrics and higher education evaluation, applying bibliometric methods to analyze knowledge structures in psychology and social sciences. Her work examines publication patterns, citation dynamics, and personality psychology frameworks, with significant contributions to understanding the Dark Triad construct and national research systems. She also investigates student satisfaction metrics and teaching quality assessment in academic settings. Her publication record demonstrates consistent interdisciplinary application of bibliometric techniques across psychology, sociology, and educational research, revealing methodological innovations in mapping scientific knowledge development and evaluation practices. She actively participates in the Laboratory for Experimental Psychology and contributes to institutional initiatives including student evaluation systems and international academic exchange programs.

• Statistics
• Econometrics
• Supply Chain Management
• +7 more

Dr. Anna Tatarczak is a Lecturer at the Department of Management Research Methods, Institute of Management and Quality Sciences, Faculty of Economics, Maria Curie-Skłodowska University. She holds a PhD in mathematical sciences from Jagiellonian University (2015) and an MSc in mathematics from Maria Curie-Skłodowska University. Her 13-year career spans research and education in statistics, econometrics, logistics, and decision analysis. PhD: Jagiellonian University (2015) MSc: Maria Curie-Skłodowska University (original text suggests Master of Science) Specialized in data analysis and project management through additional postgraduate studies Her research focuses on quantitative methods in management, particularly game theory approaches for supply chain coalitions, profit allocation models, and horizontal collaboration in logistics. She has led multiple research projects including "Coordination of supply chains: a game theory approach" (2020-2022) and commercial projects for companies like SOLVERIS and Innovative Engineering. Her academic output includes 27 publications with ministerial score 868, focusing on: Collaborative logistics Supply chain optimization Decision support systems Economic disparity analysis Energy consumption modeling Statistical applications in management She has implemented educational projects for children and youth, and served as expert for international entities including the State Education Development Agency (Latvia) and Horizon 2020. Her work combines rigorous mathematical approaches with practical industry applications.

• Psychology
• Human Factors
• Data Visualization
• +7 more

Pedro Valero Mora is a Professor at the University of Valencia , affiliated with the Faculty of Psychology and Speech Therapy and the Department of Behavioural Science Methodology . He leads research at the Universitat de València Research Institute of Transit and Road Safety (INTRAS) and contributes to the GIDOP Optimal Development Research Group . PhD in Psychology (Universitat de València, 1996) Specializes in Human-Computer Interaction , Driving Simulation , and Data Visualization Develops tools for Statistical Analysis and Educational Technology Focuses on Transportation Safety , Behavioral Science , and GIS Applications His recent work analyzes driving performance through GIS mapping , fatigue studies , and mindfulness impacts . Publications emphasize automated data visualization and road safety in university campuses. Research spans 26 years, including collaborations on EU COST Action TU1101 and ViSta software development.

• Lifespan Psychology
• Cognitive Aging
• Neural Plasticity
• +2 more

Ulman Lindenberger is Managing Director (2006-2009, 2016-2019, 2025-2027) and Director of the Center for Lifespan Psychology at the Max Planck Institute for Human Development in Berlin. He concurrently serves as Co-Director of the Max Planck UCL Centre for Computational Psychiatry and Ageing Research. He holds honorary professorships at Freie Universität Berlin, Universität des Saarlandes, and Humboldt-Universität zu Berlin. Education includes a Dipl.-Psych. from Technische Universität Berlin (1985), Dr. phil. from Freie Universität Berlin (1990 summa cum laude ), and Habilitation in Psychology (1998). His research examines: Behavioral and neural plasticity across lifespan Brain-behavior relationships Lifespan developmental theory Multivariate developmental methodology Formal models of behavioral change His publications focus on cognitive aging patterns, neural plasticity mechanisms, and methodological innovations in lifespan psychology. Research demonstrates consistent themes: neurocognitive dedifferentiation in aging, dopaminergic modulation of cognition, and environmental influences on brain plasticity. Awards include: Gottfried Wilhelm Leibniz Prize (2010) Fellow of Royal Society (2025) Foreign Member of Royal Swedish Academy of Sciences (2023) Mentoring Award of German Psychological Society (2011) He has supervised over 50 doctoral students and secured major grants including DFG collaborative projects, BMBF initiatives (Berlin Aging Study I/II), EU Horizon 2020 (LIFEBRAIN), and Max Planck Society strategic funds. Leads multidisciplinary teams at Center for Lifespan Psychology and Max Planck UCL Centre, coordinating international projects like COBRA (Cognition, Brain, and Aging) and SYNAPSE.

Michel BesserveView profile

Researcher

• Causal Machine Learning
• Complex Systems
• Neuroscience
• +4 more

Michel Besserve is a Senior Research Scientist in the Empirical Inference department at the Max Planck Institute for Intelligent Systems in Tübingen, Germany. His research bridges machine learning theory with applications in neuroscience and complex systems analysis. He leads a research group focused on developing causal machine learning tools to uncover the internal structure and transformations of complex artificial, physical, and socioeconomic systems. Dr. Besserve's primary research interests center on causal machine learning and its applications to understanding complex systems. His work investigates how causality can provide principled ways to study and improve AI algorithms, particularly focusing on the identifiability of causal models and the principle of Independence of Causal Mechanisms (ICM). He develops theoretical frameworks and practical tools for causal inference in complex equilibrium systems, neural circuits, and socioeconomic contexts. His research has significant implications for building trustworthy and interpretable AI systems that can reliably handle real-world complexity. Analysis of Dr. Besserve's recent publications reveals a strong focus on causal representation learning, with significant contributions to independent mechanism analysis and the identifiability of nonlinear generative models. His work spans both theoretical foundations and practical applications, connecting machine learning with neuroscience to understand brain function through causal inference. The interdisciplinary nature of his research is evident in publications spanning top machine learning conferences (NeurIPS, ICML, ICLR) and leading neuroscience journals (Nature, PLOS Biology). Dr. Besserve has established productive collaborations across multiple institutions, particularly with researchers at the Max Planck Institute and ETH Zurich. His work demonstrates how integrating causal principles with machine learning can address fundamental challenges in AI robustness and interpretability, with applications ranging from brain network analysis to economic modeling. His research group focuses on developing the Causal Computational Model (CCM) framework, which aims to create digital representations of real-world systems that integrate data, domain knowledge, and interpretable causal structure. This work has potential applications in climate modeling, industrial digital twins, and economic simulation.

• Visual Analytics
• Temporal Data Analysis
• Data Cleansing
• +3 more

Theresia Gschwandtner is a Researcher at TU Wien's Research Division of Visual Analytics (E193-07). Her work focuses on advancing visual analytics methodologies for temporal data, fraud detection, and uncertainty visualization. She leads the Network Lab and contributes to tools like TimeCleanser for data cleansing and NEVA for fraudulent network identification. Her research emphasizes interactive systems for guidance in data analysis, provenance tracking, and enhancing user-centric visualization frameworks. Key research interests include temporal data preprocessing, multivariate time series analysis, and the integration of automated guidance systems into visual analytics platforms. She has collaborated on projects such as Hermes (economic network exploration) and TBSSvis (temporal blind source separation), which combine algorithmic innovation with intuitive user interfaces. Guidance frameworks and user studies are central to her work, exploring how automated support impacts performance and mental state during complex data analysis tasks. She has advised students on theses addressing data quality, cyclical pattern detection, and lighting design visualization. Notable contributions include the Quantifying Uncertainty in Time Series Processing framework and the LightGuider system for interactive lighting design guidance. Her work bridges theoretical advancements with practical applications in healthcare, finance, and engineering domains.

Silvia MikschView profile

Professor

• Visual Analytics
• Information Visualization
• Interaction Design
• +6 more

Silvia Miksch is a Full University Professor of Visual Analytics at TU Wien's Faculty of Informatics, leading the CVAST Center. She holds a PhD from the University of Vienna and has held roles including Head of the Department of Information and Knowledge Engineering at Danube University Krems. Her research focuses on Visual Analytics, Information Visualization, Temporal Data Analysis, and Medical Informatics. She has supervised numerous PhD and Master’s students, with notable advisees including Ignacio Baltazar Pérez Messina and Davide Ceneda. Her work bridges theory and practice, addressing challenges in Visual Analytics for healthcare, business intelligence, and digital humanities. Awards include the IEEE VGTC Technical Achievement Award (2023) and induction into the IEEE Visualization Academy (2020). She actively contributes to conferences like IEEE VIS and EuroVis as program chair and steering committee member. Her projects, such as 'VisuExplore' and 'DisCo', have received recognition for advancing visualization in medical and cultural domains. Key research areas include guidance-enriched systems, network visualization, and temporal reasoning. She explores applications in fraud detection, cultural heritage analysis, and pandemic data visualization. Her lab's tools, like 'Hermes' and 'COVIs', exemplify task-driven design for real-world data challenges.

Bersimis SotiriosView profile

Associate Professor

• Statistical Process Control
• Public Health Surveillance
• Health Analytics
• +5 more

Sotirios Bersimis is an Associate Professor at the University of Piraeus, Department of Business Administration. He holds additional roles as an elected member of the board of directors of the National Statistical Institute (Greece) and representative for FenSTATs and ECAS. Previously, he served as President of the Hellenic Organization for Health Care Services (EOPYY) and as President of the European Healthcare Fraud & Corruption Network (EHFCN). His education includes a PhD in Statistics from the University of Piraeus, an MSc in Statistics from Athens University of Economics and Business, and a BSc in Statistics and Insurance Science from the University of Piraeus. His research focuses on stochastic models for process monitoring, statistical process control, and health analytics. He has published over 60 peer-reviewed articles in journals like Journal of Quality Technology , Statistics in Medicine , and Annals of the Institute of Statistical Mathematics . His work emphasizes applications in healthcare surveillance, quality management, and fraud detection. Notable contributions include the development of the Process Monitoring Group and the 'Multivariate Statistical Process Control Charts: An Overview' paper, which remains highly cited in the field. Bersimis has received awards such as the 2018 ENBIS Best Manager Award and a public honor from the Greek Prime Minister. He actively collaborates with healthcare institutions, pharmaceutical companies, and international organizations, contributing to projects on health expenditure modeling and anti-fraud initiatives. His teaching spans undergraduate and postgraduate programs in statistics, biostatistics, and data science.