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Robert J. StelzerView profile

Professor

• Stochastic Processes
• Financial Mathematics
• Multivariate Time Series Analysis
• +3 more

Robert J. Stelzer is a Professor at the Institute of Mathematical Finance, Ulm University. He holds a Ph.D. in Mathematics from Technische Universität München (TUM) and was a Carl von Linde Junior Fellow at TUM's Institute for Advanced Study (2008–2011). His research focuses on multivariate stochastic processes, Lévy-driven models, and risk analysis. Stelzer's work includes developing methodologies for financial time series analysis, Markov-switching models, and continuous-time volatility modeling. Education: Ph.D., Mathematics, TUM (2007), Thesis: "Multivariate Continuous Time Stochastic Volatility Models Driven by a Lévy Process" Master's (Diploma), Mathematical Finance & Economics, TUM and University of Århus (2005) Research Interests: Lévy processes, stochastic volatility models, risk analysis, numerical methods for stochastic differential equations, and statistical analysis of continuous-time processes. He leads projects on extreme value theory and matrix-valued Lévy processes. Awards: Förderpreis der Fachgruppe Stochastik (2008) "Bund der Freunde" Promotionspreis (2005–2007) E-fellows.net scholarship (2000–2007) Grants & Fellowships: Carl von Linde Junior Fellowship (2008–2011), TUM Graduate Programme support, Bavarian state scholarship. Focus Group: Risk Analysis and Stochastic Modeling (as part of TUM-IAS activities).

Maarten BoksemView profile

Associate Professor

• Decision Neuroscience
• Consumer Behavior
• Neuroimaging
• +2 more

Maarten Boksem is an Associate Professor at Rotterdam School of Management, Erasmus University Rotterdam. His research investigates neural underpinnings of human behavior, including cheating, decision-making, and consumer responses using EEG/fMRI. Key interests: neural decoding of psychological states, movie/advertisement neural predictors, and cognitive control in ethical decisions. He directs studies on dishonesty modulation and neural reward signatures. Publications focus on neuromarketing, neural connectivity, and individual differences in behavior. Recent work predicts stock performance from investor brain activity. Awards include ERIM Early Career Fellowship (2010). He mentors PhD candidates in consumer neuroscience and decision modeling.

Holger DreesView profile

Professor

• Actuarial Mathematics
• Financial Time Series Modeling
• Extreme Value Statistics
• +3 more

Prof. Dr. Holger Drees is a Professor of Actuarial Mathematics at the University of Hamburg, affiliated with the Faculty of Mathematics, Computer Science and Natural Sciences. He holds a position in the Department of Mathematics, specializing in the ST – Mathematical Statistics and Stochastic Processes research group. His office is located at Bundesstraße 55, Room T15 in Hamburg. He earned his diploma in mathematics from the University of Dortmund (1990), his PhD from the University of Siegen (1993), and his habilitation from the University of Cologne (1998). His research focuses on extreme value theory, actuarial mathematics, financial time series modeling, and non/semiparametric statistics. He is a member of the Hamburger Zentrum für Versicherungswissenschaft (HZV) and serves as an Associate Editor for *Bernoulli* and *Extremes* journals. His recent research emphasizes statistical inference on extreme value dependence structures, cluster-based methods for time series extremes, and dimension reduction techniques for multivariate extremes. His work bridges theoretical advancements in extreme value analysis with practical applications in finance and insurance. Teaching activities include advanced courses on extreme value theory and actuarial mathematics. Professional contributions include editorial roles and collaborative projects on statistical methodologies for extremes. His research has been supported by grants such as the DFG Heisenberg grant (2000–2002). He maintains an active international research network, collaborating with institutions like the University of Cologne and the University of Heidelberg.

• Health Informatics
• Distributed Decision Support
• Medical Imaging Analytics
• +3 more

Teresa Wu is a Professor and Associate Dean of Global Engagement at the Fulton Schools of Engineering, Arizona State University (ASU). She holds a joint appointment as a Professor in the School of Computing and Augmented Intelligence and serves as a Health Solutions Ambassador in the College of Health Solutions. Her expertise spans Health Informatics, Distributed Decision Support, and Medical Imaging Analytics. Wu earned her Ph.D. in Industrial Engineering from the University of Iowa and holds M.S. and B.S. degrees in Mechanical Engineering from Beihang University, China. Wu’s research focuses on medical imaging analytics, distributed decision systems, and information systems applications. She leads the ASU-Mayo Center for Innovative Imaging and has pioneered work in MRI-based kidney pathology tracking and headache classification using AI. Her awards include the NSF CAREER Award (2003) and recognition as an ASU Tenure and Promotion Exemplar (2006). Her grants include leadership roles in projects such as the $10M NIH-funded study on post-traumatic headache mechanisms and the NSF-funded CYDRES initiative securing smart buildings. She has advised numerous interdisciplinary collaborations, including work on sleep disorder monitoring and grid-interactive building cybersecurity. Education: Ph.D. Industrial Engineering (University of Iowa), M.S. & B.S. Mechanical Engineering (Beihang University). Awards: NSF CAREER Award, ASU Tenure Exemplar. Labs/Teams: ASU-Mayo Center for Innovative Imaging, CYDRES (Building Cyber Defense).

Birgit RudloffView profile

Professor

• Multivariate risks
• Set-valued risk measures
• Markets with transaction costs
• +4 more

Birgit Rudloff is a Full Professor at the Institute for Statistics and Mathematics at Vienna University of Economics and Business. With a PhD in Financial Mathematics from Martin-Luther-University Halle-Wittenberg, her research focuses on multivariate risks, set-valued risk measures, optimization algorithms, and game theory applications in finance. She leads significant research projects in financial mathematics and vector optimization. Her research interests span dynamic set-valued risk measures, markets with transaction costs, systemic risk measurement, and algorithms for solving vector optimization problems. Recent work explores Nash equilibria computation and convex projections in unbounded spaces. Research outputs demonstrate strong focus on convex optimization techniques in financial mathematics, risk measurement methodologies, and game theory applications. Recent publications show growing interest in machine learning approaches for optimization problems. 2021 SIAM-FME Conference Paper Prize 2021 WU Star Journal Award Advises doctoral students and postdoctoral researchers in financial mathematics and optimization theory. Current research group includes postdocs and PhD students working on vector optimization and risk measures.

Francesca BeccaceceView profile

Associate Professor

• Quantitative Finance
• Decision Theory
• Risk Theory
• +7 more

Francesca Beccacece is an Associate Professor of Financial Mathematics at Bocconi University in Milan. She has held leadership roles as Director of the Bachelor of Science in Economics and Management for Arts, Culture, and Communication (2016–2019) and Director of the Master in Quantitative Finance and Risk Management (2008–2010; 2012–2014). Her educational background includes a Degree in Economics from Università di Parma. Her research focuses on Quantitative Finance (financial markets models, asset pricing, term structure models), Decision Theory (benchmarking models in finance), and Risk Theory (default risk modeling). Recent work explores multivariate risk attitudes in sustainability policies and regulatory principles for financial amortization. Earlier studies include applications of functional ANOVA in utility theory and stochastic frameworks for project evaluation. Teaching responsibilities span undergraduate and graduate courses, including Financial Models for Valuation , Mathematics , and Quantitative Methods for Social Sciences . Past courses include Mathematics, Advanced Financial Mathematics, Quantitative Methods for Management, and Fixed Income/Asset Pricing in master programs. Her contributions to academia include curriculum development and editorial work in finance-related journals. She advises on financial modeling and risk strategies, though specific advisees are not listed. No scientific awards were explicitly mentioned in the provided text.

• Metabolomics
• NMR Spectroscopy
• Multivariate Statistics
• +3 more

Alessia Vignoli is a Research Fellow at the Department of Chemistry, University of Florence, affiliated with the Magnetic Resonance Center (CERM). She holds a M.Sc. in Chemical Sciences (cum laude) and a PhD in Structural Biology from the University of Florence. Her research focuses on NMR-based metabolomics for disease diagnosis and prognosis, particularly in cardiovascular, neurological, and oncological contexts. She has been awarded the GIDRM Under 35 award (2022) and the Airalzh Grant for Young Researchers (2022). Education: M.Sc. in Chemical Sciences (cum laude) - University of Florence (2014) PhD in Structural Biology (cum laude) - University of Florence (2017) Research interests emphasize metabolic profiling of biofluids using NMR spectroscopy, with applications in precision medicine. Key areas include biomarker discovery for diseases like Alzheimer’s, cancer, and cardiovascular conditions. She has contributed to projects analyzing metabolomic signatures in breast cancer recurrence, myocardial infarction outcomes, and drug response monitoring. Awards and grants include the AIRC fellowship (2019-2020), CERM/CIRMMP institutional support, and collaborations with clinical teams to develop diagnostic tools. Her work bridges analytical chemistry, computational statistics, and clinical translation, leveraging multivariate data analysis and metabolic network modeling. Her research has been applied to diverse fields, including veterinary medicine (e.g., dairy calf health) and space-related physiology (e.g., skeletal muscle adaptation in microgravity).

• Statistical Learning
• Machine Learning
• Probability
• +6 more

Stephan Clémençon is a Professor at Télécom Paris, working within the Information Processing and Communication Laboratory (LTCI) where he leads the Signal, Statistics and Learning (S2A) research team. His academic career spans multiple prestigious institutions including University of Paris X (2000-2005) and INRA Met@risk research unit (2005-07), with membership in the LPMA (Stochastic Modeling and Probability) laboratory of Paris 6 and Paris 7. Clémençon earned his PhD in Applied Mathematics from University of Paris 7 Denis Diderot with a visiting period at Stanford University's Department of Statistics (1997-1998). He currently serves as the responsible for the Specialized Master's in 'Big Data' at Télécom Paris and previously held the 'Machine Learning for Big Data' industrial chair (2013-2018), now actively involved in the 'Data Science and AI for Digitalized Industry and Services' chair. His research interests focus on statistical learning, probability, statistics, machine learning, stochastic processes, and nonparametric statistics, with applications spanning quantitative finance, biosciences, and signal/image processing. Clémençon has developed an 'immoderate taste for stochastic modeling and statistics applied in various application areas.' His extensive publication record (270 documents in HAL) demonstrates consistent research productivity with 15 recent publications (2020-2024) primarily focused on anomaly detection, ranking algorithms, statistical learning theory, and bias correction in machine learning. His work bridges theoretical statistics with practical applications in computer vision, telecommunications, and ethical AI considerations. Clémençon teaches advanced courses including Martingale Theory, Machine Learning, Advanced Nonparametric Statistics, and Big Data Projects at Télécom Paris, while also contributing to programs at Ensae Paris, Paris Diderot University, ENS Paris Saclay, and other institutions. His research team (S2A) operates within the Image, Data, Signal (IDS) department at Télécom Paris, focusing on the intersection of signal processing, data science, and statistical learning methodologies.

Yuri GoegebeurView profile

Associate Professor

• Extreme Value Theory
• Statistical Modeling
• Actuarial Science
• +5 more

Yuri Goegebeur is an Associate Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark, affiliated with the SDU Climate Cluster. His research focuses on extreme value theory, statistical modeling, and actuarial science, particularly in areas like reinsurance pricing, tail risk analysis, and censoring data methods. He has contributed extensively to methodologies for estimating extreme quantiles, tail dependence functions, and risk measures under various conditions. His work integrates advanced statistical techniques with applications in finance, insurance, and environmental risk assessment. Key research interests include extreme value analysis, robust estimation techniques, and the development of risk measures for time series and censored datasets. He has published over 80 articles in peer-reviewed journals and serves on editorial boards for journals like Extremes and Psychometrika . His collaborative projects involve analyzing extreme weather impacts on healthcare systems and advancing multivariate regression models for extreme values. Recent publications emphasize conditional tail moment estimation, dependent risk measures, and applications in reinsurance. His research bridges theoretical statistics with practical challenges in actuarial science and environmental modeling. He has co-led projects funded by institutions like Villum Fonden, focusing on extreme value methodologies for real-world problems such as climate-related health risks.

• Alcohol Use Disorders
• Epidemiology
• Mental Health
• +7 more

Associate Professor Louise Mewton is a public health researcher affiliated with the Matilda Centre at the University of Sydney’s Faculty of Medicine and Health. She also holds a visiting role at the Centre for Healthy Brain Ageing, UNSW Sydney, and collaborates with the COSMIC Collaborators and the Institute for Health Metrics and Evaluation (IHME). Her research focuses on alcohol use disorders across the lifespan, emphasizing their cognitive impacts in gestation, adolescence, and older adulthood. She leads projects on Fetal Alcohol Spectrum Disorders (FASD) teacher resources, neurodevelopmental studies using ABCD Study data, and global dementia epidemiology via COSMIC cohort harmonization. Mewton has secured $16M in funding from NHMRC, NIH, and other bodies to support her work. Her research interests include population neuroscience, comorbidity between mental health and substance use disorders, diagnostic models of psychopathology, and biostatistical methods. Key projects include a randomized controlled trial of an online alcohol intervention in older adults and a large-scale study on the general factor of psychopathology in adolescents. Mewton’s work also addresses pandemic mental health, particularly emotion regulation strategies during the 2020 pandemic year. Awards & Honours : 2024 Fulbright Scholar UNSW Scientia Career Development Fellowship (2019–2022) Young Tall Poppy Science Award (2017) Multiple national and international grants (e.g., NIH, NHMRC) Grants and advising: Mewton leads the $16M-funded ‘Rethink My Drink’ trial targeting older adults and collaborates on global dementia initiatives. She advises research students like Nicholas HOY on transdiagnostic mental health models. Her grants include the Matilda Centre-UCSD Compact for adolescent health research and NIH-supported projects exploring alcohol-dementia links. She also contributed to the Climate Schools Combined Trial, a long-term prevention program. Labs and teams: She is part of the Matilda Centre, Brain and Mind Centre, and COSMIC collaboration. Her work bridges clinical research, population health, and digital intervention design.

Alexandra DiasView profile

Professor

• Quantitative Risk Management
• Insurance Finance
• Copula Models
• +4 more

Alexandra Dias is a Professor of Finance and Actuarial Science at the University of York, affiliated with the School for Business and Society and the Department of Accounting and Finance. She previously held roles at the University of Leicester and University of Warwick. She earned her PhD from ETH Zurich, with additional degrees from Universidade de Lisboa and Universidade Nova de Lisboa. Roles: Co-programme Leader for BSc Actuarial Science, Fellow of Advance HE, Member of the Pensions Gap Working Party (Institute and Faculty of Actuaries). Educations: PhD in Finance from ETH Zurich, MSc from Universidade de Lisboa, Licenciatura from Universidade Nova de Lisboa. Her research focuses on quantitative risk management, insurance finance, copula models for multivariate dependence, and extreme events analysis. Recent work includes studies on pension gaps, reinsurance impacts, and digital accessibility in academia. She has contributed to journals like Risks, Journal of Banking and Finance, and Quantitative Finance . Key articles explore topics such as copula-based risk aggregation (2025), pensions policy (2024), and stop-loss reinsurance effects (2022). Her work bridges theoretical finance with practical applications in insurance and policy. Awards: Fellow of Advance HE. Grants/Advising: Editor for European Journal of Finance , guest editor for Risks , and active in interdisciplinary research collaborations. Labs/Teams: Part of the York Management School’s actuarial science and finance research groups.

Cesario MateusView profile

Professor

• Corporate Finance
• Corporate Governance
• Asset Management
• +3 more

Cesario Mateus is a Full Professor of Finance at Aalborg University Business School, part of The Faculty of Social Sciences and Humanities. He joined Aalborg University in 2019 and has previously held academic positions at institutions such as Cass Business School, Universitat Pompeu Fabra, and the University of Melbourne. He has also worked in industry roles at entities like the Portuguese Securities and Exchange Commission and Banco Comercial Portugues. His research focuses on corporate finance, corporate governance, and asset management, with over 50 publications in high-impact journals such as the European Journal of Finance and the International Review of Financial Analysis. Education: PhD in Finance from Aarhus University (2008). Research Interests: Corporate Finance: Capital Structure, Cash Holdings, Taxation, Right Issues Corporate Governance: Regulation, Supervisory Boards, Banks’ Risk-Taking Asset Management: Mutual Funds’ Performance, Style Investments Selected Projects: Principal Investigator of 'The Impact of Fiscal and Industrial Policies on listed SMEs: Evidence from Germany and Italy' (2021). Project Participant in 'Climate Risk Reporting by Pension Funds: International Evidence' (2021–2023). Publications Trends: His recent work emphasizes volatility spillovers in energy and equity markets, mutual fund performance analysis, and the impact of fiscal policies on SMEs. Articles often employ econometric models to analyze market dynamics and regulatory effects. Professional Roles: Editor-in-Chief of the Journal of Corporate Governance and Sustainability Review, and editorial board member of the Journal of Banking Regulation and Journal of Industry-University Collaboration.

Gianluca FusaiView profile

Professor

• Financial Engineering
• Mathematical Finance
• Numerical Methods for Finance
• +6 more

Gianluca Fusai is a Professor of Mathematical Finance at Bayes Business School, University of London. He also holds a Full Professorship at Università del Piemonte Orientale. His research focuses on Financial Engineering, Numerical Methods for Finance, and Energy Markets. He has authored/co-authored over 50 publications, including the textbook 'Implementing Models in Quantitative Finance' (Springer). Fusai has extensive consultancy experience with institutions like BNP Paribas and has served on editorial boards of journals such as Quantitative Finance and Mathematical Finance . Education: BSc in Economics, Bocconi University MSc in Statistics and Operational Research, University of Essex PhD in Finance, Warwick Business School Research Interests: His work spans Credit Risk, Commodity Derivatives, and advanced numerical techniques for pricing exotic options. He has developed methods for efficiently valuing Asian options, swaptions, and basket options under non-Gaussian models. Recent contributions address calibration risk and systemic risk in financial markets. Professional Activities: Organizer of the Financial Engineering Workshops at Bayes Business School Referee for journals including Journal of Banking and Finance and European Journal of Operational Research Consultant for public and private sector institutions Advising & Grants: Supervised multiple PhD students in topics like swaption pricing and counterparty credit risk. Active in grant-related research on systemic risk and quantitative finance applications. Labs/Teams: Leads research initiatives in computational finance and risk management at Bayes Business School, collaborating with institutions globally.

Malvina MarcheseView profile

Associate Professor

• Econometrics
• Financial Econometrics
• Commodity Markets Analysis
• +3 more

Malvina Marchese is an Associate Professor in Data Science for Finance at Bayes Business School, part of City, University of London. She serves as Academic Director of the Finance Cluster degrees and holds a PhD in Econometrics from the London School of Economics (LSE). Previously, she was Head of Risk Management at Shell Oil in Italy and has extensive industry experience in quantitative risk management since 2008. She currently advises Maersk Brokers on shipping econometrics and CBRE Investment on real estate forecasting. Her research focuses on econometric methodologies applied to commodity markets, multivariate GARCH models, long memory volatility analysis, and quantile regression. Key areas include energy markets, shipping economics, and financial stability analysis. Her work bridges academic theory with practical applications in risk management and policy. Publications span topics like green investment strategies in shipping, price premiums for eco-friendly vessels, and volatility modeling in energy and financial markets. She contributes to journals such as Transportation Research Part D , Energy Economics , and Journal of Banking & Finance . Malvina is a Non-Executive Director at Timberlake Consultants and maintains consultancy roles in the energy and real estate sectors. She is fluent in English, French, and Italian, with expertise in peer review and academic leadership.

Pankaj ChoudharyView profile

Professor

• Biostatistics
• Statistical methodology
• Method comparison studies
• +2 more

Pankaj Choudhary is a Professor of Statistics in the Department of Mathematical Sciences at the University of Texas at Dallas, where he also serves as the Associate Dean of Graduate Studies for the School of Natural Sciences and Mathematics since 2021. Previously, he was Associate Head of the Department of Mathematical Sciences from 2015-2021. He has been a faculty member at UTD since 2002, progressing from Assistant Professor (2002-2008) to Associate Professor (2008-2017) and finally to Professor (2017-present). His educational background includes a Ph.D. in Statistics from Ohio State University (2002), an M.S. in Statistics from the Indian Institute of Technology, Kanpur (1998), and a B.S. in Statistics from the University of Delhi (1996). Dr. Choudhary's research focuses on Biostatistics, Statistical Methodology, Method Comparison Studies, Risk Prediction, and Proteomics . His work bridges theoretical statistics with practical applications in healthcare and biomedical research. He has developed specialized statistical methods for agreement assessment in method comparison studies, which have become influential in clinical and laboratory settings where multiple measurement techniques need validation against each other. His research in risk prediction, particularly for contralateral breast cancer through the CBCRisk calculator and R package, demonstrates his commitment to translating statistical methodology into clinical decision support tools. His publication record shows a clear trajectory from foundational work in statistical methodology for method comparison studies toward increasingly sophisticated applications in biomedical research, particularly in cancer risk prediction and substance use disorder modeling. Recent work demonstrates integration of Bayesian approaches and machine learning techniques with traditional statistical methodology. Dr. Choudhary has authored two significant books in his field: "Ordered Data Analysis, Modeling and Health Research Methods" (2015) and "Measuring Agreement: Models, Methods, and Applications" (2017), the latter of which has received positive reviews in major statistical journals. He has been involved in securing research funding, including a $20,000 grant from the Actuarial Foundation (2007-2008) for work on multivariate conditional density estimation and a $100,000 grant from the American Health Assistance Foundation (2005-2007) for proteomics research related to Alzheimer's disease. Dr. Choudhary has also developed the CBCRisk calculator, an online tool for predicting personalized risk of contralateral breast cancer. As Associate Dean of Graduate Studies, he plays a significant role in shaping graduate education within the School of Natural Sciences and Mathematics. He has taught specialized short courses at major statistical conferences and summer schools, including the Joint Statistical Meetings and MESIO UPC-UB Summer School.