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Hato SchmeiserView profile

Professor

• Risk Management
• Insurance Economics
• Normative Decision Theory
• +4 more

Prof. Hato Schmeiser is a Full Professor of Risk Management and Insurance Economics at the University of St. Gallen, serving as Chairholder of the Chair for Risk Management and Insurance since 2005. He also serves as Managing Director of the Institute of Insurance Economics (IVW-HSG) and was Dean of the School of Management (2017-2023). His academic journey includes a doctoral degree from the University of Passau (1997), a postdoctoral qualification (Habilitation) from Humboldt University Berlin (2003), and professorships at the Universities of Münster and Frankfurt. Education: Doctorate in Business Administration (Dr. rerum politicarum), University of Passau (1997) Habilitation in Business Administration, Humboldt University Berlin (2003) Studies in Business Administration at the University of Mannheim (1987-1993) Research focuses on Risk Management, Insurance Regulation, Behavioral Insurance, and Strategic Management of Insurance Companies. Key themes include optimal risk pooling, capital allocation under Solvency II/SST, and consumer preferences for insurance products. His work bridges theoretical models and practical regulatory challenges. Publications emphasize innovative reinsurance solutions, pandemic risk insurability, and digital monitoring in insurance. Recent work explores big data's impact on risk classification and privacy. Awards highlight contributions to insurance economics, including multiple recognitions from the American Risk and Insurance Association and Emerald Literati Network. He has held leadership roles in professional organizations like the European Group of Risk and Insurance Economists (EGRIE, President 2015-2016). Grants include Swiss National Science Foundation funding for studies on investment guarantees and regulatory impacts. Active in editorial roles for journals like Geneva Risk and Insurance Review and The Journal of Insurance Issues. Oversees the IVW's research on insurance trends and policy implications. Labs/Teams: Leads the IVW-HSG, a leading research hub in insurance economics, collaborating with industry and academia on projects like pandemic risk modeling and digital insurance transformation.

Mario Valentin WüthrichView profile

Professor

• Actuarial Science
• Insurance Mathematics
• Stochastic Finance
• +6 more

Mario V. Wüthrich is a Professor for Actuarial Science in the Department of Mathematics at ETH Zurich. Born in 1969, he earned a diploma (1995) and PhD (1999) in mathematics from ETH Zurich. He held a postdoc position at Nijmegen University (1999–2000), worked at Winterthur Insurance (2000–2005), and became a Senior Scientist at ETH Zurich's Mathematics Department (2005). Since 2011, he has been an Adjunct Professor and currently serves as a full Professor. His research focuses on actuarial models, insurance liabilities, and risk management. He is Editor-in-Chief of the ASTIN Bulletin and a Senior Scientific Advisor at InsureAI. He has received numerous awards, including the Brian Hey Prize (2021) and JoCo Best Paper Award (2025). His work spans machine learning applications in insurance, claims reserving, and data analytics. Upcoming engagements include the International Congress on Insurance: Mathematics and Economics (2025–2028). Educations: ETH Zurich (PhD 1999, Diploma 1995) Affiliations: RiskLab, ASTIN Bulletin Editorial Board Research interests combine theoretical actuarial science with practical applications in insurance pricing, risk modeling, and AI-driven solutions. Recent publications include advancements in neural networks for actuarial problems and credibility theory.

Marco ZaffalonView profile

Professor

• Machine Learning
• Causal AI
• Probabilistic Graphical Models
• +3 more

Marco Zaffalon is a Professor at the University of Applied Sciences and Arts of Southern Switzerland (SUPSI) and Scientific Director of the Dalle Molle Institute for Artificial Intelligence (IDSIA). He holds an MSc in Computer Science (cum laude) and a PhD in Applied Mathematics. His research focuses on causal AI, machine learning, and probabilistic models under uncertainty, with applications in defense, environment, medicine, engineering, and business. He leads a 30-member research group in probabilistic machine learning and has secured over 15 million Swiss Francs in competitive grants. Education: MSc in Computer Science (cum laude), PhD in Applied Mathematics. Professional milestones include roles as a software engineer at an Italian insurance group, consultant for SGS-Thomson, post-doctoral fellow at IDSIA, and tenure as a researcher since 2001. Research interests emphasize foundational and applied aspects of probability theory, particularly imprecise probability, and causal reasoning. His work integrates knowledge-based systems, probabilistic graphical models, and robust algorithms for real-world problems. Notable projects include OmniProfiler , Credal Model Averaging , and Learning under Near-Ignorance . Awards & Recognition: President of SIPTA (2019–present) Senior Area Editor for International Journal of Approximate Reasoning (IJAR) Top 2% Scientist (Stanford’s ranking since 2019) Grants & Leadership: Oversaw 15+ million CHF in competitive research funding Founded and directs a 30-member research group He collaborates on interdisciplinary projects, including environmental modeling, medical applications, and defense-related AI systems through IDSIA’s labs and partnerships.

• Statistics
• Econometrics
• Risk Management
• +5 more

Ruben Seiberlich is a Lecturer at the Zurich University of Applied Sciences (ZHAW), School of Management and Law, Department of Banking, Finance, and Insurance. He holds a PhD in Econometrics from the University of Konstanz (2013) and has taught courses such as Advanced Quantitative Methods, Risk Management, and Finanzinstrumente & Portfoliotheorie. His research focuses on statistics, econometrics, and risk management, with notable work on real estate bubble risk, gender discrimination in housing, and partisan bias in inflation expectations. Education: Diplom Volkswirt (2009), Dr. rer. pol. Ökonometrie (2013) Current roles: Study Program Leader for MSc Banking and Finance, Model Performance and Backtesting Leader at Credit Suisse AG Research Interests span empirical economics, risk analysis, and applied econometric methods. His recent articles analyze real estate and gold market bubbles, gender bias in rental housing, and political bias in economic expectations. Key methodologies include semi-parametric decomposition, propensity score matching, and shrinkage estimation. Scientific Networks: Member of the German Statistical Society’s committee for Empirical Economics and Applied Econometrics; ORCID ID 0000-0002-5258-5499.

• Applied Microeconomics
• Microeconometrics
• Labor Economics
• +8 more

Mattia Albertini is a Post-Doctoral Researcher at the Institute for Economic Research (IRE) within the Faculty of Economics at Università della Svizzera italiana (USI). He previously completed his Ph.D. at the Institute of Economics (IdEP), USI, and participated in the Swiss Program for Beginning Doctoral Students in Economics. He holds a Master of Science in Economics with a minor in Data Science from USI and a Bachelor in Economics from the University of Pavia. Bachelor in Economics, University of Pavia Master of Science in Economics (minor in Data Science), Università della Svizzera italiana Ph.D. in Economics, Università della Svizzera italiana Swiss Program for Beginning Doctoral Students in Economics, Swiss National Bank His research focuses on applied microeconometrics with applications in health, labor, public, urban, and environmental economics. A recurring theme in his work is the spatial dimension of economic outcomes, including residential integration, real estate market dynamics, and geographic proximity to borders or hazards. He integrates data science techniques, particularly programming in Python and STATA, to develop tools for empirical analysis and reproducible research. The collection of articles reflects a strong trend in applied econometrics, causal inference, and spatial analysis. His work spans health economics (e.g., benzodiazepine prescribing), labor and urban economics (e.g., migration, job accessibility), and environmental and insurance economics (e.g., natural hazard impacts). Methodologically, he engages with difference-in-differences, event study designs, spatial modeling, and text analysis, often building or applying computational tools to address research questions. He has contributed to teaching at both the Bachelor's and Master's levels, particularly in microeconomics, macroeconomics, and data-intensive courses such as Textual Analysis and Spatial Data for Economists. Teaching Assistant, Microeconomics A (Bachelor) Teaching Assistant, Macroeconomics B (Bachelor) Teaching Assistant, Textual Analysis and Spatial Data for Economists (Master) Mattia Albertini is actively involved in open science, sharing code for econometric methods, data visualization, and application development (e.g., a gluten-checking app) on GitHub. His interdisciplinary approach combines economic theory, statistical modeling, and computational tools to investigate real-world policy-relevant questions.

Markus MeiererView profile

Assistant Professor

• Marketing
• Network Science
• Computational Linguistics
• +7 more

Markus Meierer is an Assistant Professor at the Institute of Management, University of Geneva, and serves as an External Lecturer at the Chair of Marketing for Social Impact, Department of Business Administration, University of Zurich. His interdisciplinary work integrates marketing, network science, and data science to study large-scale consumer networks and behavior. His research focuses on understanding how consumers' social ties influence behavior, using advanced methodological tools such as multilevel modeling to account for contextual effects. His expertise spans machine learning, marketing analytics, and computational linguistics, applied to real-world challenges in telecommunications, insurance, banking, and retail sectors. Markus holds a PhD and has an extensive educational background, including studies at the Kellogg School of Management, University of Zurich, and University of Trier. He is fluent in German, English, and French, and brings both academic rigor and industry experience to his roles. He is actively involved in leveraging data science to build customer-centric organizations, leading research initiatives on social networks and consumer behavior at the University of Zurich. Assistant Professor, Institute of Management, University of Geneva External Lecturer, Chair of Marketing for Social Impact, University of Zurich His professional experience includes roles as a Data Scientist and Research Group Leader at UZH, where he collaborates with major industries to apply data-driven strategies. No scientific awards are listed in the provided text. He advises on data strategy and market development, with a strong focus on translating academic research into practical business applications. His teaching includes Machine Learning at UZH. He leads the research group 'Social Networks & Consumer Behavior' at the University of Zurich, fostering interdisciplinary collaboration between marketing and data science.

Despoina MakariouView profile

Assistant Professor

• Catastrophe Risk Management
• Machine Learning in Insurance
• Insurance-Linked Securitisation
• +2 more

Despoina Makariou serves as Assistant Professor of Risk Management and Insurance at the University of St. Gallen and holds a Visiting Research Fellow position in Statistics at the London School of Economics and Political Science (LSE). Her academic journey includes a PhD in Statistics from LSE (2017-2022), an MSc in Insurance & Risk Management from Cass Business School (2012-2013), and a BA in Economics and International Business from the University of Macedonia (2008-2012). Dr. Makariou's research centers on machine learning in insurance and finance, catastrophe risk management, and insurance-linked securitisation. She develops and applies statistical learning methods to address challenges in insurance pricing, risk modeling, and alternative risk transfer. Her work bridges theoretical statistics with practical applications in the insurance industry. Recent publications demonstrate a trend toward integrating advanced machine learning techniques (such as random forests and finite mixture models) with traditional actuarial science to improve catastrophe bond spread prediction, claim count modeling, and expert opinion aggregation for risk measures. She has been recognized with scholarships for academic excellence and research potential, and holds an Advanced Chartered Insurance Institute Diploma. Her industry experience spans roles at CNA Hardy, Argo Group, and Lancashire Insurance in catastrophe risk modeling and (re)insurance pricing. Dr. Makariou is affiliated with the Institute of Insurance Economics at the University of St. Gallen, contributing to research and education in risk management and insurance.