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Martin SpindlerView profile

Professor

• Econometrics
• Statistics with Applications to Finance, Insurance, and Health Economics
• Nonparametric Statistics
• +3 more

Prof. Dr. Martin Spindler is a Professor for Statistics at the Department of Statistics with Application in Business Administration, University of Hamburg Business School. His research bridges Econometrics, Statistics, and Machine Learning, focusing on high-dimensional methods, causal inference, and applications in finance, insurance, and health economics. Current position since 2016 Visiting Professor at University Mannheim (2016), Boston College (2015), and MIT (2015, 2013-2014) Senior Researcher at Max Planck Society (2012-2016) Education: PhD in Economics, University of Munich (2012) Master in Mathematics and Economics, University of Munich (2008) and Regensburg (2003) B.A. in Mathematics, University of Regensburg (2005) His methodological work includes L2Boosting for treatment effect estimation, double machine learning frameworks, and nonparametric approaches for asymmetric information. Applications span from fraud detection in claims management to pandemic shielding strategies and financial forecasting. Research Trends: Recent publications emphasize high-dimensional statistical methods, causal machine learning, and interdisciplinary applications. Key tools include double machine learning, attention networks, and transformation models. Collaborations: Active partnerships with institutions like MIT, Boston College, and Max Planck Society, alongside contributions to open-source software (e.g., DoubleML, hdm package).

Fiona SloothaakView profile

Assistant Professor

• Stochastic Operations Research
• Network Theory
• Cascading Failures
• +6 more

Fiona Sloothaak is an Assistant Professor at Eindhoven University of Technology (TU/e), affiliated with the Department of Mathematics and Computer Science, specializing in Stochastic Operations Research. She holds the Eurandom Assistant Professor title and contributes to the Stochastic Operations Research group. Her research focuses on cascading failures in complex systems, power grids, battery swapping networks, and stochastic processes. Education: Fiona earned a PhD in Stochastic Operations Research from TU/e in 2020 and completed a Master's in Mathematics (2015). Her doctoral thesis, Criticality in power networks: a probabilistic approach , explores cascading failures in power systems. Research Interests: Fiona’s work addresses systemic risk in infrastructure systems, including power grids and transportation networks. She investigates cascading failure dynamics, load balancing in battery swapping stations, and resource pooling strategies. Her studies often employ probabilistic modeling and queueing theory. Key Projects: She co-authored the MARCONI project (2019–2022), focusing on integrated planning for maritime logistics and service systems. This involved optimizing supply chain resilience and remote control tower operations. Scientific Award: Applied Probability Trust Prize (2020) Teaching: Courses include Stochastic Networks, Insurance and Credit Risk, and Probability and Stochastics 1. Grants: MARCONI project funding for maritime logistics optimization. Labs/Teams: Active in TU/e’s Stochastic Operations Research group and Eurandom institute. Collaborations span network topology analysis and interdisciplinary systems research.

Fred Espen BenthView profile

Professor

• Mathematical Finance
• Energy Markets
• Insurance
• +4 more

Roles and Affiliations: Fred Espen Benth is a Professor in the Department of Mathematics at the University of Oslo, affiliated with the Risk and Stochastics research group. He holds a Dr. scient (PhD equivalent) in mathematics from the University of Oslo (1995). His academic journey includes roles as a researcher at the Norwegian Computing Center, a postdoc at the Universities of Aarhus and Oslo, and an Associate Professor at the University of Trondheim before becoming a full professor in 2002. Research Interests: Benth’s research focuses on mathematical finance, particularly energy and weather markets, commodity derivatives, and stochastic analysis. He explores modeling, estimation, and simulation of spot and forward prices, as well as pricing options and portfolio optimization. Recent work extends to climate systems, energy transition dynamics, and machine learning applications in financial and environmental modeling. Publications and Projects: His extensive publication record includes over 150 journal articles and book chapters, with a focus on energy markets, stochastic processes, and climate-related financial instruments. Notable projects include ‘Spatial-Temporal Uncertainty in Energy Systems (SPATUS)’ and contributions to interdisciplinary energy informatics. His work bridges theoretical stochastic analysis with practical applications in energy systems and risk management. Labs and Collaborations: Benth collaborates with the Stochastics of Renewable Energy Markets (STORE) group and contributes to initiatives like the ‘Computational Modelling and Machine Learning for Applications in Hydropower’ project. His research emphasizes the integration of stochastic methods with real-world energy and climate challenges.

David SaundersView profile

Associate Professor

• Quantitative Risk Management
• Mathematical Finance
• Stochastic Optimization
• +3 more

David Saunders is an Associate Professor in the Department of Statistics and Actuarial Science at the University of Waterloo, affiliated with the David R. Cheriton School of Computer Science. His research focuses on quantitative risk management, mathematical finance, and stochastic optimization with applications to credit risk, portfolio optimization, and derivatives pricing. Professor Saunders has held academic positions at the University of Pittsburgh and Cyprus International Institute of Management, and has consulted for institutions like RiskMetrics, TD Bank, and Central Bank of Cyprus. Education: PhD studies at the University of Toronto with industry collaboration at Algorithmics Inc., followed by roles at Cyprus International Institute of Management and University of Cyprus's HERMES Center. His work bridges theoretical stochastic analysis and practical financial risk management challenges. Research interests prominently feature credit risk management, optimal stopping problems, and analytical techniques in finance. Recent work addresses inverse default boundary problems, wrong-way risk in derivatives, and efficient algorithms for portfolio optimization under complex return distributions. Collaborations with financial institutions drive applied research in capital allocation, operational risk modeling, and regulatory compliance. Advising and grants include directing the Professional Science Master's in Mathematical Finance at Pitt, supervising student-industry projects on credit risk and CDO pricing, and securing funding for computational finance initiatives. Active in RiskLab Cyprus and HERMES European Center, his research extends to equity risk management and market risk frameworks.

Alfred KumeView profile

Senior Lecturer

• Shape analysis
• Directional statistics
• Image analysis
• +1 more

Dr. Alfred Kume is a Senior Lecturer in Statistics at the University of Kent, affiliated with the School of Mathematics, Statistics and Actuarial Science. He has held this position since 2004 and has been involved in examining processes for the Institute of Actuaries. His research focuses on shape analysis, directional statistics, image analysis, and stochastic geometry. Kume obtained his PhD and postdoctoral training at the University of Nottingham after working as an actuary. He has supervised students including Theodoros Gkolias and Justyn Campbell-White. His work spans statistical methodology applied to astronomy (e.g., stellar light observations, HII regions) and computational statistics (e.g., holonomic gradient methods, clustering algorithms). His publications reflect expertise in probability distributions, algorithm development, and interdisciplinary applications. His office is located in Cornwallis South, Canterbury Campus. Research interests emphasize statistical techniques for shape and directional data, with applications in astronomy and biology. Key contributions include saddlepoint approximations for normalizing constants and statistical clustering methods. His work bridges theoretical statistics with practical problems in astrophysics and actuarial science. Publications highlight trends in statistical methodology (e.g., Bingham/Fisher-Bingham distributions), computational algorithms, and interdisciplinary collaborations. While no specific awards are listed, his extensive publication record and academic roles reflect scholarly recognition. Advising focuses on statistical shape analysis and Bayesian methods, with grants possibly tied to collaborative projects. He is part of research teams analyzing molecular clouds and astronomical phenomena. His lab or team activities are integral to interdisciplinary projects, though specific lab names are not mentioned.

• Financial Mathematics
• Actuarial Mathematics
• Stochastic Processes
• +7 more

Thorsten Schmidt is Professor of Mathematical Stochastics at the University of Freiburg, succeeding Prof. Ernst Eberlein in the summer semester of 2015. He also serves as Senior Financial Engineer at MathFinance. Previously, he held professorships at Chemnitz University of Technology (2008-2015), Technical University Munich (2008), and University of Leipzig (2004 onwards). From 2017-2019, he was a Research Fellow at the Freiburg Institute for Advanced Studies (FRIAS) in a joint research group with the University of Strasbourg and USIAS on the topic of Linking Finance and Insurance. His research focuses primarily on financial and actuarial mathematics, stochastic processes, and statistics, with recent work on machine learning methods and their applications in financial mathematics and AI regulation. In Freiburg, his goal with his young team is to tackle complex challenges with improved mathematical models and apply these methodologies to various fields. Key Research Areas: Financial mathematics and credit risks Pricing and hedging of derivative financial products Statistics of stochastic processes Energy markets and nonlinear filter theory Machine learning applications in finance and insurance His recent publications show a strong trend toward integrating machine learning with traditional mathematical finance, particularly in risk management, insurance-finance arbitrage, and robust financial modeling. His work increasingly addresses ethical considerations in AI applications within finance, reflecting his broader interest in responsible AI development. Notable Awards: IDA Award Finance (2015) FRIAS-USIAS Research Fellow (2017/2018) IDA Award Machine Learning and AI (2020) MAPFRE Research Grant (2020) Luis Bachelier Fellow (2021) As Editor-in-Chief of Statistics and Risk Modeling and Associate Editor for Mathematical Finance and International Journal of Theoretical and Applied Finance, Schmidt plays a significant role in academic publishing. He leads the CRC 'Small Data' research center with Harald Binder, focusing on medical problems where disease progression must be estimated with few data points per patient. His LeanAI project, funded by the Vector Foundation, explores the connection between machine learning and theorem-proving software LEAN, aiming to develop AI that can translate between mathematics and formal proof systems. His laboratory work centers around the application of stochastic methods combined with machine learning to solve problems in finance and insurance where data is limited ('Small Data' initiative), with significant funding from DFG (€12 million for CRC Small Data) and the Carl Zeiss Foundation.

Santosh AnagolView profile

Associate Professor

• Economic Development
• Household Finance
• Financial Regulation
• +5 more

Santosh Anagol is an Associate Professor in the Business Economics and Public Policy Department at The Wharton School, University of Pennsylvania . His research focuses on financial markets in emerging economies , with emphasis on household finance , regulatory policy , and behavioral economics . Ph.D. in Economics (Yale University, 2009) B.A. in Economics with Minor in Mathematics (Stanford University, 2002) Fulbright Scholar to India (2002-2003) Anagol’s research spans topics like: Indian mutual fund regulation , including commission reforms and investor behavior Life insurance agent incentives and product mis-selling Endowment effects in IPO lotteries Consanguineous marriage impacts on genetic variation Political economy of elections via the 'runner-up effect' Economic returns of livestock in rural India His recent publications analyze: Global genetic patterns linked to marriage practices Investor responses to randomized IPO gains Regulatory impacts on mutual fund flows Scientific awards include: Fulbright Scholarship Wharton Teaching Excellence Award (2018-2019) Teaching focuses on: BEPP2030 - Business in Global Political Environment BEPP2800 - Applied Data Analysis using R Doctoral seminars on research methodology and empirical analysis

Hector Gutierrez RufrancosView profile

Senior Lecturer

• Economics
• Political Economy
• Labour Economics
• +6 more

Dr Hector Gutierrez Rufrancos is a Senior Lecturer in Economics at the University of Stirling School of Management , where he serves as the Undergraduate Economics Programme Director, PhD Convenor, and Summer School Director for the Scottish Graduate Programme in Economics (SGPE). He is also a Research Fellow at the Global Labor Organization . Education: PhD in Economics (2017), University of Sussex MSc in Development Economics (2010), University of Sussex BA in Economics with Development Studies (2009), University of Sussex His research focuses on the interplay between policy, institutions, and individual well-being, particularly in the context of living standards, inequality, and informality. Key areas include: Living Standards & Well-being: Historical analysis of nutrition, poverty, and inequality (19th–20th centuries) in Britain, Europe, and the USA. Institutions & Political Economy: Studies on unions in Mexico, geopolitical risk spillovers, and informal economies in Sub-Saharan Africa and Latin America. Methodological Expertise: Applied econometric techniques such as causal inference, instrumental variables (IV), regression discontinuity design (RDD), and differences-in-differences (DiD). His recent publications address topics like pollution’s effect on labor supply, informality’s impact on welfare, and historical inequality patterns. He emphasizes empirical rigor in both research and teaching, offering workshops in Stata programming, LaTeX, and causal inference methods. Academic Service: Organized SGPE Summer Schools (2022–present), co-directed SGPE PhD programs (2022–2023), and coordinates MSc dissertations. He actively presents at conferences including the Scottish Economic Society, Royal Economic Association, and UNU-WIDER seminars.

Claudia CeciView profile

Professor

• Stochastic models
• Economics
• Finance
• +7 more

Claudia Ceci is a Full Professor at the Department of Methods and Models for Economy, Territory, and Finance (MEMOTEF) at Sapienza University of Rome. Her academic work focuses on stochastic models in economics, finance, and insurance, with a particular emphasis on optimal stochastic control, filtering, asset pricing, credit risk, and self-protection strategies. Coordinates internationalization initiatives within MEMOTEF Leads the Rome Sapienza unit in the 2022 PRIN project "Stochastic control and games and the role of information" Manages the 2023 Grande Sapienza project "Stochastic Optimization Problems in Insurance, Finance and Economics" Member of the UMI-PRISMA group (Probability in Statistics, Mathematics, and Applications) Featured in the "100 Esperte STEM" initiative (Mathematics category) Her research explores risk management, counterparty credit risk, and reinsurance optimization under partial observation. She has contributed extensively to journals in quantitative finance, insurance mathematics, and stochastic control. Recent projects analyze climate-related financial risks and reinsurance strategies under contagion models. Claudia teaches foundational mathematics and risk management courses across multiple campuses. Exam procedures for her mathematics course involve computerized written tests with mandatory oral components under specific conditions, reflecting her analytical approach to assessment.

• Auctions and Mechanism Design
• Microeconomic Theory
• Information Design
• +3 more

Benny Moldovanu is Professor of Economics at the University of Bonn, where he holds the Chair of Microeconomics. He is a principal investigator in the Collaborative Research Center TR 224 (CRC TR 224) and a member of the Cluster of Excellence ECONtribute: Markets & Public Policy, as well as the Hausdorff Center for Mathematics and the Bonn Graduate School of Economics (BGSE). Education: Detailed curriculum vitae available at CV link . Research Interests: His research focuses on auctions and mechanism design , microeconomic theory , information design , voting theory , and dynamic allocation problems . He has made significant contributions to understanding strategic behavior in auctions, optimal voting rules, and the design of mechanisms under incomplete information. Recent Publications Trend: Recent work (2020–2025) explores entropy-regularized optimal transport in information design, optimal security design for risk-averse investors, order independence in sequential voting, and insurance design under adverse selection. These studies combine sophisticated theoretical tools with applications in finance, political economy, and market design. Scientific Awards and Honors: Winner of the first AEA Best Paper Award for the American Economic Journal: Microeconomics (2009) for “Dynamic Revenue Maximization with Heterogeneous Objects: A Mechanism Design Approach”. Advising and Doctoral Training: Professor Moldovanu has supervised a large cohort of doctoral students and postdocs, many of whom now hold faculty positions worldwide (e.g., Philipp Strack—Clark Medal recipient, Alex Gershkov, Andreas Kleiner, Xianwen Shi, Thomas Kittsteiner). He teaches Microeconomics II (summer 2025) and offers thesis supervision to BGSE students. Labs and Research Groups: He leads the Microeconomics Research Group at Bonn and coordinates research activities in the CRC TR 224 project B01, focusing on market design and information economics.

Tadeusz TyszkaView profile

Professor

• Economic Psychology
• Decision-Making Psychology
• Risk Perception
• +3 more

Prof. Tadeusz Tyszka is a Full Professor and Director of the Center for Economic Psychology and Decision Research (Centrum Psychologii Ekonomicznej i Badań Decyzji ALK) at Kozminski University. His primary research interests include decision-making psychology, economic psychology, and behavioral economics. He has authored numerous books and articles, including Psychologiczne Pułapki Oceniania i Podejmowania Decyzji and Psychologia Zachowań Konsumenckich . He has held leadership roles in international scientific associations, such as the International Association for Research in Economic Psychology and the Division 9 (Economic Psychology) of the International Association of Applied Psychology. His research focuses on cognitive and emotional factors influencing risk perception, decision-making under uncertainty, and consumer behavior. Recent work examines how psychological biases affect choices related to insurance, disaster preparedness, and entrepreneurial risk-taking. Prof. Tyszka’s interdisciplinary approach bridges psychology, economics, and behavioral science to understand human decision processes in complex scenarios. He has contributed to debates on moral judgment, probabilistic reasoning, and the application of psychological principles to policy design. His publications span experimental studies, theoretical analyses, and critiques of literary and historical works through a psychological lens.

• Actuarial Science
• Actuarial Statistics
• Life Insurance
• +6 more

Steven Haberman is a Professor of Actuarial Science at Bayes Business School, City, University of London. He has held senior leadership roles, including Deputy Dean, Director, and Dean of Bayes Business School until 2015. Prior to this, he was Dean of the School of Mathematics (1995–2002). His academic journey began at the University of Cambridge (BA in Mathematics), followed by PhD and DSc in Actuarial Science from City University. He has served as a Lecturer in Actuarial Science since 1974 and worked part-time at the Government Actuary's Department (1986–2006). He is a Fellow of the Institute of Actuaries and Royal Statistical Society. Research focuses on mortality modeling (e.g., the Renshaw-Haberman model), longevity risk, pensions, and annuities. He has authored/co-authored 5 books and over 180 papers, and edited journals like Journal of Pension Economics and Finance . Awards include an honorary doctorate from the University of Haifa (2018) and research prizes from the Institute of Actuaries. He has supervised 33 doctoral students and served on professional bodies like the Institute and Faculty of Actuaries and the Financial Reporting Council’s Board for Actuarial Standards. Current roles include Editor-in-Chief of Risks and Chair of the Board of Governors of the London Foundation for Banking and Finance.

Asaf CohenView profile

Associate Professor

• Applied Probability
• Stochastic Processes
• Control Theory
• +12 more

Asaf Cohen is an Associate Professor in the Department of Mathematics at the University of Michigan, Ann Arbor, affiliated with the College of Literature, Science, and the Arts. He holds a B.Sc., M.Sc., and Ph.D. from Tel-Aviv University (2005–2013). His research focuses on applied probability, stochastic processes, and control theory, with emphasis on mean-field games, mathematical finance, actuarial science, diffusion and large deviation analysis, machine learning, and risk-sensitive control. His work also addresses applications in stochastic networks, energy markets, epidemiology, and economics. Key research areas include diffusion approximations, large deviations, queueing theory, and partial differential equations. Dr. Cohen has contributed to the analysis of multiclass queueing systems, optimal dividend strategies, and strategic server behavior in heavy traffic regimes. His methods often involve advanced stochastic control techniques and game-theoretic models. He has published extensively on topics such as mean-field games, SIR models for epidemics, and Bayesian sequential testing. His academic contributions span theoretical advancements and practical applications in finance, insurance, and operations research.

Zhiyu QuanView profile

Assistant Professor

• Actuarial Science
• Data Science
• Computational Statistics
• +3 more

Zhiyu (Frank) Quan is an Assistant Professor at the University of Illinois at Urbana-Champaign (UIUC), holding positions in the Department of Mathematics, Department of Statistics, and National Center for Supercomputing Applications (NCSA). He is also an affiliate faculty member at Discovery Partners Institute as InsurTech Lead and serves as an ORMI Faculty Fellow in Finance. His research focuses on data science applications in actuarial science, including tree-based models, natural language processing, and deep learning for insurance risk modeling, predictive analytics, and InsurTech innovation. Education: Ph.D. in Actuarial Science (University of Connecticut, 2019), MS in Applied Statistics (Michigan State University, 2014), and BS in Mathematics and Applied Mathematics (Xiamen University, 2012). Research interests include computational statistics, insurance analytics, and leveraging machine learning for actuarial challenges such as claim prediction, rate-making, and cyber risk modeling. He leads the Illinois Risk Lab, bridging academic research with industry needs, and has pioneered hybrid tree-based models to address imbalanced data in insurance. Notable achievements include the Arnold O. Beckman Research Award and Society of Actuaries Research Institute recognition. He advises two doctoral students and teaches advanced predictive analytics courses, emphasizing practical applications in actuarial science and data ethics. Key collaborations involve InsurTech companies and NCSA, focusing on NLP-driven academic paper repositories (CyLit) and federated learning for privacy-preserving insurance data sharing. His work addresses real-world challenges in cyber insurance and automated machine learning systems.

• Survival Data Analysis
• Frailty Models
• Cure Models
• +3 more

Catherine Legrand serves as Professor of Biostatistics at UCLouvain (Louvain-la-Neuve, Belgium), affiliated with the Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA) and the Louvain School of Statistics, Biostatistics and Actuarial Sciences (LSBA) within the Faculty of Science. She chaired ISBA from 2019-2022 and currently presides over the Louvain Institute for Data Analysis and Modeling (LIDAM). Her academic foundation includes: Master Degree in Mathematics from Université Libre de Bruxelles (1998) PhD in Statistics from Hasselt University (2005) supervised by Prof. Paul Janssen and Prof. Luc Duchateau, specializing in survival analysis and frailty models Her research program centers on Survival Data Analysis with emphasis on frailty models, cure models, and Joint Models for Longitudinal and Survival Data. She develops methodologies for clinical trial design and analysis with direct applications in oncology data, bridging theoretical statistics and clinical practice through collaborations with medical researchers. Analysis of her recent publications (2023-2025) reveals three dominant trends: 1) Advanced survival modeling techniques (semi-Markov, cure rate models) for disease progression; 2) Validation frameworks for surrogate endpoints in clinical trials using joint modeling; 3) Emerging applications in health insurance analytics and public health systems. Her work increasingly addresses real-world healthcare challenges while maintaining methodological rigor. No scientific awards were documented in the available sources. Information regarding student supervision and research grant funding was not provided in the current materials. She leads research within LIDAM and ISBA, building on her prior role as primary statistician for the EORTC Lung Cancer Group where she contributed to multiple Phase II/III trials in lung cancer and mesothelioma. Her current work integrates biostatistical methodology development with practical applications across oncology, public health, and actuarial science domains.