جست‌وجوی استاد و پژوهشگر

Sven Ottoمشاهده پروفایل

استادیار

• Time Series Analysis
• Functional Data Analysis
• Econometric Theory
• +۳ مورد دیگر

Dr. Sven Otto is an Assistant Professor of Econometrics at the University of Cologne, Faculty of Management, Economics and Social Sciences, where he has been employed since 2023. His academic work focuses on advanced econometric methods, particularly in time series analysis and functional data. He is affiliated with the Institute of Econometrics and Statistics at the university. His educational background includes: 2019: Dr. rer. pol., University of Cologne 2014: MSc in Economics, University of Bonn 2012: BSc in Mathematics and Economics, University of Bonn Dr. Otto's research centers on developing and applying sophisticated econometric techniques to analyze complex time-dependent data. His work bridges theoretical econometrics with practical applications, particularly in financial and economic time series analysis. He has made significant contributions to the understanding of structural breaks, unit root testing, and functional data models. His research has important implications for economic forecasting, financial risk assessment, and policy evaluation, especially in volatile periods such as the COVID-19 pandemic. Otto collaborates with researchers across institutions, including Jörg Breitung at the University of Cologne and Florian Stark. Dr. Otto's recent publications demonstrate a clear trajectory toward increasingly sophisticated modeling of time-dependent phenomena, with a growing emphasis on functional data approaches and real-time monitoring techniques. His work shows a progression from theoretical foundations to practical applications, particularly in economic and financial contexts. Current research projects include: Approximate Factor Models for Functional Time Series (with Narazii Salish) Functional Factor Regression with an Application to Electricity Price Curve Modeling (with Luis Winter) Combining Concurrent and Historical Functional Linear Regression (with Alois Kneip and Dominik Liebl) DFG Project No. 511905296 (2024-2026): Modeling Functional Time Series with Dynamic Factor Structures and Points of Impact

Ba Chuمشاهده پروفایل

استاد

• Econometrics
• Time-Series Econometrics
• Nonparametric and Semiparametric Econometrics
• +۷ مورد دیگر

Professor Ba Chu is a faculty member in the Department of Economics at Carleton University, Ottawa, Canada. He holds a Ph.D. from the University of London and has a strong academic background in econometrics and financial economics. His research is supported by the Natural Sciences and Engineering Research Council of Canada (NSERC), including a 2020 Discovery Grant for work on simulation-based methods in large dynamic latent variable models. B.A. (Hanoi) M.Sc., M.Phil., Ph.D. (London) His research focuses on optimal asset allocation , time-series econometrics , and nonparametric and semiparametric methods . He has made significant contributions to dependence modelling using copulas , asymptotic theory , and large deviations approaches to risk assessment . His work bridges theoretical econometrics with practical applications in finance and macroeconomics. The recent publications highlight a consistent focus on high-dimensional and dynamic models, with applications in financial markets, portfolio optimization, and macroeconomic data analysis. His methodological contributions include advanced inference techniques in moment restriction models, duration dependence in high-frequency trading, and robust estimation under dependence and structural breaks. The research trend shows a deep integration of probability theory, statistical inference, and financial economics. NSERC Discovery Grant (2020), $90,000 over five years Professor Chu teaches courses such as Econometrics I (Econ 5027) , Applied Time Series Analysis (Econ 5713w) , and Statistical Methods in Economics . While no formal advisees are listed, his collaborative research involves prominent econometricians such as D.T. Jacho-Chavez, O.B. Linton, and S. Satchell. He has no known lab or research team listed publicly, but his work is theoretically rigorous and mathematically sophisticated, often involving simulation-based and asymptotic methods.

• Biostatistics
• Medical Biometrics
• Epidemiology
• +۳ مورد دیگر

Prof. Dr. rer. nat. Annika Hoyer is a leading Professor in Biostatistics and Medical Biometrics at the Medical Faculty OWL of the University of Bielefeld. She serves as a member of multiple critical committees including the Faculty Conference, Doctoral Committee, and Research Commission, and contributes to the Center for Statistics . Her work integrates advanced statistical methodologies with public health applications. Academic affiliation: University of Bielefeld Primary role: Professor for Biostatistics and Medical Biometrics Committee memberships: Faculty Conference, Habilitation Committee (Deputy), Doctoral Committee, APL Committee (Deputy), Research Commission Research Interests focus on: Methodological developments in meta-analysis and ROC curve modeling Diabetes epidemiology with emphasis on incidence trends, mortality projections, and socioeconomic factors Chronic disease modeling using illness-death models and copula-based approaches Clinical trial statistics and medical data synthesis Public health surveillance for early-onset cancer and long-term care demand Publications highlight methodological innovations in time-to-event analysis , diagnostic accuracy studies , and diabetes burden estimation . Her collaborative work spans neurosurgery , rheumatology , and pediatric emergency care . Key Projects include ongoing methodological-epidemiological research on type 2 diabetes dynamics (funded by private third-party support until 2025) and statistical models for chronic disease surveillance in low-resource settings.

Taufiq Choudhryمشاهده پروفایل

استاد

• Financial Markets
• Financial Economics
• International Trade
• +۲ مورد دیگر

Professor Taufiq Choudhry is a leading academic in Finance at the University of Southampton , affiliated with the Southampton Business School and serving as Head of Department for Banking and Finance . His work bridges empirical finance, quantitative analysis, and policy economics. Education: BSc, MSc, PhD (USA) Current since: 2006 Research Focus : Financial Markets Financial Economics Economic Uncertainty International Trade Housing Market Dynamics Publication Trends : Recent work analyzes financial crises (2007 and COVID-19), credit risk modeling, consumer behavior impacts, and historical economic policy effects. Scientific Recognition : Stanford 2% Global Scientist Citation Rankings (2021) Teaching & Supervision : Currently teaches Introduction to Finance at MSc level. Has extensive experience teaching Corporate Finance, Financial Markets, and Financial Econometrics across UG, PG, and MBA programs. Supervising PhD students Dmitri Mustanen and Anoud Abdulaziz Alsaab.

Agnieszka Marciniukمشاهده پروفایل

پژوهشگر

• Actuarial Science
• Life Insurance
• Financial Mathematics
• +۳ مورد دیگر

Agnieszka Marciniuk is a Researcher in the Department of Statistics at Wrocław University of Economics. She serves as Coordinator of the Statistics Teaching Team and Treasurer of the PTS Wrocław Branch. Her work focuses on actuarial science, life insurance, stochastic modeling, and financial instruments for retirement planning. She has published extensively on equity release contracts, marriage-based financial instruments, and the socio-economic impacts of online education. Her research interests include life annuity equity release mechanisms, stochastic interest rate modeling, and the application of copulas in insurance products. She actively participates in international academic events, such as the Wrocław-Marburg Statistical Seminars. Recent articles highlight her expertise in demographic factors affecting pension benefits and cross-national comparisons of financial instruments. Her work frequently addresses aging populations and financial protection for retirees, combining mathematical rigor with practical policy implications. She maintains an active academic presence, with consultations scheduled in both on-site and online formats, and oversees diploma and master’s seminars in statistics and economics.

Matteo Malavasiمشاهده پروفایل

مدرس

• Climate Change
• Climate Risk
• Cyber Risk
• +۸ مورد دیگر

Matteo Malavasi is a Lecturer at the University of New South Wales (UNSW) School of Risk and Actuarial Studies. His academic expertise spans the critical intersection of climate risk, cyber risk, and actuarial science. With a PhD in Actuarial Studies and Business Analytics from Macquarie University (in cotutelle with University of Bergamo, Italy), Malavasi contributes to UNSW's reputation as Australia's most entrepreneurial university with research that addresses pressing global challenges in risk management and climate adaptation. Dr. Malavasi's educational background includes: PhD in Actuarial Studies and Business Analytics from Macquarie University (in cotutelle with University of Bergamo, Italy) Malavasi's research interests span multiple critical domains in contemporary risk management. His work focuses on climate change and associated risks, particularly flood risk management and climate adaptation pathways under uncertainty. He has developed sophisticated models for assessing sea level rise impacts and optimal adaptation strategies, as evidenced by his recent work on New York City. His research also addresses the growing threat of cyber risk, where he has created taxonomies and quantification methods to better understand cyber loss patterns across business sectors. His methodological expertise includes advanced statistical techniques, particularly copula modeling for dependency analysis in risk scenarios. Malavasi's scholarly output demonstrates a clear evolution from foundational work in financial mathematics toward applied research addressing climate adaptation and cyber risk. His recent publications (2021-2024) show increasing focus on practical applications of risk theory to real-world challenges, with particular emphasis on the timing of climate adaptation decisions and cyber insurance viability. His articles reveal expertise in statistical modeling and a growing contribution to interdisciplinary risk assessment frameworks that bridge actuarial science, climate science, and cybersecurity. While specific advising information isn't provided in the available materials, Malavasi's research profile suggests he likely supervises students working at the intersection of actuarial science, data analytics, and climate/cyber risk modeling. His work with quantitative risk modeling techniques would provide valuable training for graduate students pursuing careers in actuarial science, risk management, and financial analytics.

• Artificial Intelligence
• Machine Learning
• Computational Psychiatry
• +۷ مورد دیگر

Russ Greiner is a Professor of Computing Science and Adjunct Professor of Psychiatry at the University of Alberta. He is a Canada CIFAR AI Chair and founding Scientific Director of Amii (Alberta Machine Intelligence Institute). His work focuses on applying machine learning to medical challenges, including survival prediction, computational psychiatry, and precision medicine. He has collaborated extensively with clinicians and researchers across disciplines, publishing over 350 papers and receiving prestigious awards like the CS-Can/Info-Can Lifetime Achievement Award (2023) and CAIAC Lifetime Achievement Award (2021). Greiner earned his PhD from Stanford University and has held leadership roles in academic and industrial research. He has supervised over 110 students and mentees, emphasizing interdisciplinary collaboration. His contributions span foundational machine learning, medical AI applications, and metabolomics. Recent projects include predicting opioid overdose risks, modeling stroke lesions, and advancing schizophrenia diagnosis via fMRI analysis. Awards highlight his impact: the 2024 Brockhouse Prize (shared) for metabolomics work, Killam Mentoring Award (2023), and recognition for his role in establishing Alberta as an AI hub. Greiner’s lab develops tools like CFM-ID (metabolite identification) and Bayesil (metabolic pathway analysis), deployed in public health initiatives. His current research emphasizes survival analysis and AI ethics in healthcare.

Loic BRINمشاهده پروفایل

مدرس

• Mathematics
• Computer Science
• Random Matrix Theory
• +۳ مورد دیگر

Loic BRIN is a Lecturer at the National School of Bridges and Roads (École des Ponts ParisTech), affiliated with the Mathematical and Computer Engineering Information Department. He holds degrees from HEC Paris and ENSAE ParisTech. Previously, he worked in the Credit and Operational Risk Modeling team at Société Générale, later transitioning to the Group's General Inspection Department. His research focuses on applying large random matrix theory to calibrate elliptic copulas, bridging mathematical theory with financial risk applications. Teaching responsibilities include engineering training programs at the National School of Bridges and Roads, emphasizing interdisciplinary approaches between mathematics and computational methods. No specific grants, awards, or student advisees are listed in the provided information. His work reflects a blend of academic rigor and industry实践经验, particularly in quantitative risk management.

• Financial Econometrics
• Time Series Analysis
• Volatility Modeling
• +۶ مورد دیگر

Walter Distaso is a Professor of Financial Econometrics at Imperial College Business School, affiliated with the CFM-Imperial Institute of Quantitative Finance. He joined in 2006 after positions at the University of Exeter and Queen Mary, University of London, and has served as a visiting professor at the IMF. His research spans financial volatility modeling in continuous-time frameworks, long-memory macroeconomic analysis, copula-based multivariate financial time series, and market microstructure noise. He also explores interdisciplinary topics like diabetes and metabolic regulation through collaborations. Educations: PhD in Economics from the University of York Research interests include estimating financial volatility, macroeconomic determinants of stock-market volatility, and evaluating trading strategies. His recent work integrates econometric methods with health economics, addressing pandemic impacts on mental health and diabetes management. He has contributed to policy-relevant studies, such as analyzing Italy's economic growth linked to political debates, and financial misconduct around market fixes. Publications span risk management, volatility forecasting, and biomedical applications. His work balances theoretical econometric innovation with practical applications in finance and health. Awards: None explicitly mentioned. Grants/Advising: No grants listed; no student advisees documented. Labs/Teams: Affiliated with CFM-Imperial Institute but no specific lab/team details provided.

• Statistical Methods
• Machine Learning
• Materials Science
• +۵ مورد دیگر

Matthias Neumann is an Assistant Professor at the Institute of Statistics, Graz University of Technology . He completed his PhD in 2020 at Ulm University under Prof. Volker Schmidt, earning the PhD prize of Ulm University . His research focuses on stochastic 3D modeling and statistical analysis of micro- and nanostructures for functional materials, including battery electrodes , fuel cells , and paper-based materials . He has received start-up funding from ProTrainU (2020-2022) and served as principal investigator in the POLiS Cluster of Excellence (2022-2023). Research Interests: His work integrates mathematical morphology , machine learning , and spatial statistics to develop methods for microstructure quantification , estimation of geometrical descriptors (e.g., tortuosity, constrictivity), and data-driven models linking morphology to effective physical properties . He utilizes random fields , point processes , and copulas for virtual microstructure generation and parameter estimation. Teaching: He lectures on Applied Statistics , Statistical Modeling , and Mathematical Statistics at Graz University of Technology, with prior teaching experience at Ulm University in Multivariate Stochastic Modeling , Point Processes , and Spatial Statistics . Scientific Achievements: PhD prize of Ulm University (2020) ProTrainU start-up funding (2020-2022) POLiS Cluster of Excellence grant (2022-2023) Publications: His 15 most recent articles (2023-2025) emphasize machine learning techniques for microstructure segmentation , stochastic 3D modeling of nanoporous materials , and data-driven quantification of transport-property relationships . Key topics include random forests , neural networks , and R-vine copulas applied to fuel cells , sodium-ion batteries , and polymer electrolytes .

Valeria D'Amatoمشاهده پروفایل

دانشیار

• Actuarial Science
• Risk Management
• Machine Learning
• +۷ مورد دیگر

Valeria D'Amato is an Associate Professor at the Department of Methods and Models for Economy, Territory, and Finance of Sapienza University of Rome . Her academic work focuses on actuarial science, risk management, and statistical modeling. Fields of Interest : Actuarial Science, Risk Management, Machine Learning, Climate Risk, Financial Mathematics, Demographic Economics Email : valeria.damato@uniroma1.it Recent research highlights include: Developing machine learning models to assess climate risk sharing in tourism insurance and ESG score prediction. Analyzing frailty-based mortality models for longevity risk reserving and pandemic-related mortality shocks. Investigating cyber risk dependence using vine copula frameworks and geospatial pricing strategies. Her publications span journals like Frontiers in Applied Mathematics and Statistics , Applied Stochastic Models in Business and Industry , and Risks , reflecting interdisciplinary approaches to modern risk assessment challenges.

Lea Petrellaمشاهده پروفایل

استاد

• Quantile regression
• Graphical models
• Random Forest
• +۳ مورد دیگر

Lea Petrella is a Full Professor at the Department of Methods and Models for Economics, Territory, and Finance, Sapienza University of Rome. She teaches courses in Time Series Analysis and Advanced Statistical Methods , focusing on practical applications using R software. Research Interests: Quantile regression, Graphical models, Hidden Markov Models, Risk measures, and Time Series analysis Key Projects: Generalized Dynamic Graphical Models for pandemic impacts, Penalized quantile regression for risk assessment, Multivariate quantile regression frameworks Her recent publications include: 2025: Mid-quantile mixed graphical models for public shootings 2025: Spatial quantile random forests for economic mobility 2024: Expectile hidden Markov models for cryptocurrency returns 2024: Mixed-frequency quantile regressions for risk forecasting She supervises postdocs and PhD students including Maria Saiz, Beatrice Foroni, and Valentina Raponi. Her work spans financial risk modeling, environmental statistics, and biomedical applications. Email: Lea.Petrella@uniroma1.it or lea.petrella@uniroma1.it

• Dependence modeling (copulas)
• Credit risk
• Risk management
• +۲ مورد دیگر

Jean-David FERMANIAN is a Full Professor of Finance and Statistics at ENSAE Paris (Institut Polytechnique de Paris) and a permanent researcher at CREST . He holds a PhD in Statistics from Université Paris 6 and graduated from École Normale Supérieure and ENSAE. Research Interests: His work focuses on dependence modeling (copulas), credit risk, risk management, multivariate dynamic models, and machine learning applications in finance/insurance. He previously held positions at INSEE and in the financial industry. Professional Background: Combines academic expertise with industry experience in statistical analysis and financial risk modeling. Actively contributes to CREST's research initiatives in quantitative finance.

• Risk Analysis
• Actuarial Science
• Insurance Economics
• +۳ مورد دیگر

Montserrat Guillén Estany is a Professor of Quantitative Methods for Economics and Business at the University of Barcelona's Faculty of Economics and Business since 2001. She serves as Vice-Secretary of the Governing Board at the Royal Academy of Economic and Financial Sciences (RACEF) and directs the consolidated Risk in Finance and Insurance research center (UBriskcenter), which comprises 25 permanent researchers and 25 international affiliates. She also holds an Honorary Visiting Professorship at City, University of London. Bachelor of Science in Mathematics (Mathematical Statistics), University of Barcelona (1987) PhD in Economics and Business Administration, University of Barcelona (1992) Master's in Data Analysis, University of Essex, UK Visiting researcher: University of Texas at Austin, University of Paris II, University of California-Berkeley, University of Cambridge Her research focuses on risk analysis and actuarial science , particularly developing statistical methods for insurance applications. She pioneered work in operational risk modeling and quantitative approaches to insurance economics, establishing herself as the top-cited woman in Actuarial Sciences and second in Insurance. Her work bridges theoretical statistics with practical insurance industry applications, emphasizing computational methods and real-world data analysis. The 15 most recent publications demonstrate consistent leadership in actuarial methodology innovation , with increasing integration of machine learning techniques since 2020. Her research spans cyber risk, climate risk, longevity modeling, and regulatory compliance, maintaining strong connections to industry needs while advancing theoretical frameworks. Recent work shows particular emphasis on non-traditional data sources and computational approaches to complex risk scenarios. ICREA Academia distinction (2012, 2019) - only social sciences researcher to receive twice Casualty Actuarial Society honor (USA) Julio Castelo International Insurance Prize Honorary member, College of Actuaries of Catalonia Professor Guillén has supervised 17 doctoral theses at the University of Barcelona and co-supervised two internationally. She serves on numerous doctoral committees globally and participates in professor selection committees across Europe and North America. Her research has secured funding from Mapfre Foundation, BBVA Foundation, European Commission H2020, and other major institutions. She leads the UB-Zurich Chair of Insurance and UB-Mutualidad de la Abogacía Chair, focusing on actuarial innovation and aging economics research. As director of UBriskcenter, she leads a multidisciplinary team of 50 researchers conducting cutting-edge work in financial and insurance risk. The center maintains strong industry partnerships and international academic collaborations, particularly through the European Group of Risk and Insurance Economists which she previously presided over.

• Econometrics
• Labour Economics
• Wage Dynamics
• +۷ مورد دیگر

Costanza Naguib is an Assistant Professor in Econometrics at the University of Bern and a Lecturer at the Faculty of Economics, Università della Svizzera italiana (USI). Her academic work bridges quantitative econometric modeling and labor economics, with a strong focus on wage mobility and labor market dynamics. Research Interests: Her research explores wage dynamics using advanced econometric tools such as functional copula models, nonlinear autoregressive processes, and semi-nonparametric estimation techniques. She investigates how education, labor market institutions, and skill mismatches influence individual wage trajectories and job satisfaction. Scientific Contributions: Specializes in non-parametric and semi-nonparametric econometric methods Focuses on labor economics with empirical applications to wage mobility Utilizes copula-based models to capture complex dependencies in labor data She completed her PhD in Economics at the Institute for Economic Research (IRE) in March 2018. During her doctoral studies, she participated in the Swiss Program for Beginning Doctoral Students in Economics (Study Center Gerzensee) in January 2017 and was a visiting PhD student at CREST (Centre de recherche en économie et statistique), Paris, in April 2017. Her PhD thesis developed a novel semi-nonparametric model to analyze wage mobility, emphasizing the role of education. There is no public information available about doctoral students she may advise or specific research grants she leads. She is actively contributing to the fields of econometrics and labor economics through methodological innovation and empirical analysis.