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Robert AzencottView profile

Professor

• Genomics
• Proteomics
• Medical Imaging
• +9 more

Robert Azencott is a Professor of Mathematics at the University of Houston and holds the title of Emeritus Professor at École Normale Supérieure in France. He specializes in interdisciplinary research at the intersection of mathematics, biosciences, and imaging. His work spans stochastic processes, data mining, and medical imaging applications such as 3D-echocardiography analysis and deformable shape matching. Azencott has contributed to probabilistic methods for bacterial genetic evolution models and financial market microstructure analysis. His research also addresses texture classification, kernel-based learning, and stochastic differential equations (SDEs) in finance and biology. Research Interests Genomics & Proteomics : Sparse modeling of gene interactions, microRNA impact on cancer survival, proteomic mass spectra analysis. Bacterial Evolution : Stochastic models for genetic evolution with periodic selection and large deviations theory. Medical Imaging : 3D-echocardiography strain analysis, deformable shape dynamics via diffeomorphic matching, and ROI reconstruction in X-ray tomography. Probability & Statistics : SDE parameter estimation, kernel-based clustering, and applications to algorithmic trading and option pricing. Labs & Collaborations Azencott collaborates on projects such as digital stains for live-cell microscopy and intra-cardiac echography analysis to assess myocardium deformations. His work intersects with teams in cardiology, computational biology, and financial engineering.

Jeffrey PaiView profile

Professor

• Risk management
• Derivatives statistical modeling
• Term structure of interest rates
• +3 more

Jeffrey Pai is a Professor at the Warren Centre for Actuarial Studies and Research within the Asper School of Business at the University of Manitoba. With over 30 years of academic experience spanning multiple countries, he specializes in actuarial science, risk modeling, and financial derivatives. His research examines livestock/crop insurance, weather derivatives, and Bayesian statistical methods for risk assessment. Research encompasses quantitative risk management frameworks, stochastic modeling of insurance products, and econometric analysis of financial instruments. Recent work focuses on parametric insurance for natural disasters, mortality catastrophe modeling, and agricultural risk transfer mechanisms using advanced statistical approaches. Publications demonstrate consistent focus on practical applications of actuarial science, particularly in agricultural insurance and weather-related financial instruments. Recent trends show increased emphasis on Bayesian methods, catastrophe modeling, and developing market applications. Awards: Students' Teacher Recognition Awards (2021, 2002) CAS Best Paper Award (2016) Literati Network Outstanding Paper Award (2011) Featured in MACLEAN's Guide to Canadian Universities (2004) Extensive teaching includes courses in life contingencies, risk theory, and actuarial modeling. Secured research funding from international agencies including SSHRC (Canada), Society of Actuaries (USA), and UK Development Partnerships.

Asokan Mulayath VariyathView profile

Professor

• Machine Learning
• Statistical Process Control
• Empirical Likelihood
• +8 more

Dr. Asokan Mulayath Variyath is a Professor of Statistics at the Department of Mathematics and Statistics, Memorial University of Newfoundland. He holds roles as Deputy Head (Graduate) and Director of the Statistical Consultancy Centre. His academic journey includes positions at Texas A&M University, University of Waterloo, and the Indian Statistical Institute. Education: Ph.D. in Statistics (2006), University of Waterloo S.D.P. Fellow (1991) and Postgraduate Diploma (1988) in SQC & OR, Indian Statistical Institute M.Sc. in Agricultural Statistics (1987), Kerala Agricultural University Research Focus Dr. Variyath's research spans Machine Learning, Statistical Process Control, Empirical Likelihood, Spatial Analysis, and Fisheries Data Modeling. He has developed statistical applets to enhance learning, such as interactive tools for Normal Distribution and Sampling Techniques. Awards: 2024: Dean of Science Distinguished Service Award 2023: Dean’s Award for Service Excellence 2010: Professional Statistician (P.Stat) from SSC Teaching & Supervision: Currently supervising 1 Ph.D., 5 M.Sc., and 3 professional students. Over 20 advisees graduated under his mentorship. Courses taught include Design of Experiments, Survival Analysis, and Computational Statistics. Key Contributions: Principal Investigator for the 'Statistical Applets' project funded by MUN, advancing interactive learning in statistics.

Michael GrabchakView profile

Professor

• Tempered Stable Distributions
• Infinitely Divisible Processes
• Heavy Tails
• +3 more

Michael Grabchak is a Professor in the Department of Mathematics and Statistics at the University of North Carolina at Charlotte (UNC Charlotte). His research focuses on tempered stable distributions, infinitely divisible processes, heavy-tailed phenomena, quantitative finance, and entropy estimation. He has developed software packages such as SubTS , SymTS , and EntropyEstimation for statistical analysis and simulation. His work bridges theoretical probability and applications in finance, statistics, and computational methods. Key contributions include studies on Lévy processes, risk estimation, and biodiversity metrics using entropy-based techniques. He has authored a monograph on tempered stable distributions and edited volumes on extreme value theory. His teaching includes advanced courses on applied probability and statistical methods, and he has presented his research at international conferences across the globe. Grabchak’s research has been published in top journals like Statistics and Computing , Journal of Applied Probability , and Quantitative Finance . He actively contributes to academic service through invited lectures, editorial roles, and software development for statistical education and applications.

• Econometrics
• Computational Statistics
• Financial Risk Management
• +5 more

Marco Bee is a Full Professor at the Department of Economics and Management, University of Trento. His expertise spans applied econometrics, computational statistics, finance, and risk modeling. He focuses on methodologies for handling heavy-tailed distributions, extreme value theory, and machine learning applications in financial risk assessment. Education details are available in his CV (CVeng.pdf). His research interests include developing statistical models for operational risk, volatility forecasting, and credit scoring, often employing mixture models, copula-based approaches, and indirect inference techniques. He has contributed significantly to the analysis of spatial econometrics and the application of extreme value theory to financial crises and insurance analytics. His recent work emphasizes tail risk estimation, with over 150 publications since 2006. Notable contributions include methodologies for Value-at-Risk (VaR) forecasting, distribution fitting for skewed data, and the use of machine learning to predict defaults in small businesses. His research bridges theoretical statistics and practical financial applications, with a focus on high-frequency data and scenario-based risk analysis. Awards and grants are not explicitly listed in the provided data, but his extensive publication record reflects recognition in quantitative finance and econometrics. He advises students on topics related to computational econometrics and risk modeling, though specific advisee names are not documented here.

James BoothView profile

Professor

• Statistical Methodology
• Bootstrap Methods
• Monte Carlo Methods
• +5 more

James Booth is a Professor and Department Chair in the Department of Statistics and Data Science at Cornell University, part of the Computing and Information Science school. He holds a joint appointment with the Department of Biological Statistics and Computational Biology in the College of Agricultural and Life Sciences. His research focuses on statistical methodology, including bootstrap methods, clustering, mixed models, and applications in bioinformatics. He has taught numerous courses, including Statistical Methods II and Biological Statistics I, and contributes to Cornell's statistical consulting service. His work spans statistical theory and applications across disciplines like epidemiology and social sciences. Education: PhD from the University of Florida (Department of Statistics), with postdoctoral research at the Australian National University and Colorado State University. Research Interests: James’ methodological work emphasizes computational statistics, including Monte Carlo methods and generalized linear models. His applied research includes bioinformatics, wildlife disease surveillance, and social science data analysis. Key contributions include Bayesian modeling for disease prevalence estimation and statistical tools for proteomic data analysis. Publications: Over 50 peer-reviewed articles, with recent focuses on sample size calculations for wildlife disease studies and statistical methodologies for high-dimensional data. Notable work includes contributions to the Journal of the American Statistical Association and PLoS Computational Biology. Advising: Advised over 15 graduate students, many contributing to statistical methodology and interdisciplinary applications. Labs/Teams: Active in the Cornell Statistical Consulting Unit and collaborates with interdisciplinary groups in biology, public health, and social sciences.

• Risk Measures
• Quantitative Risk Management
• Value at Risk
• +4 more

Tolulope Rhoda Fadina serves as an Assistant Professor in the Department of Mathematics at the University of Illinois Urbana-Champaign, focusing on quantitative risk management and mathematical finance. Her work bridges theoretical frameworks with practical applications in insurance and financial risk assessment. Her research spans Risk Measures , Value at Risk , Expected Shortfall , and Quantile Mathematics , with emphasis on uncertainty modeling and multivariate risk structures. Key contributions include minimalist axiomatizations of quantiles and frameworks for risk measurement under parameter uncertainty, addressing critical gaps in reinsurance optimization and dependence modeling. Analysis of her 2019-2025 publications reveals consistent innovation in foundational risk theory, with high-impact work in journals like Finance and Stochastics and Insurance: Mathematics and Economics . Her research integrates probability theory, operations research, and actuarial science to solve complex financial uncertainty problems. No scientific awards were documented in the available profile. Student advising activities and research grant details were not specified in the provided materials. Collaboration networks indicate international research partnerships, but no dedicated laboratory or team affiliations were mentioned.

Marco GeraciView profile

Professor

• Statistical Methods in Health Sciences
• Quantile Inference
• Random-Effects Models
• +9 more

Marco Geraci is a Professor at the Department of Methods and Models for Economy, Territory and Finance at Sapienza University of Rome . He teaches courses such as Statistics for Health Economics and Computational Tools for Finance . His research focuses on Statistical Methods in Health Sciences , including Quantile Inference , Random-Effects Models , Multivariate Statistics , and Missing Data Analysis . He also specializes in Accelerometer Data , Physical Activity , and Pediatric Oncology . His recent publications emphasize Quantile Regression applications in Public Health , Environmental Science , and Health Economics . Methodological contributions include Directional Quantile Classifiers , Functional Quantile Regression , and Laplace Distribution Projections . Themes span Biostatistics , Surgical Safety , and Maternal/Child Health Disparities .

Lea PetrellaView profile

Professor

• Quantile regression
• Graphical models
• Random Forest
• +3 more

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

Raj MehrotraView profile

Research Fellow

• Hydrology
• Climate Change Impact Assessment
• Statistical Modeling
• +3 more

Dr Raj Mehrotra is a Senior Research Fellow at the Water Research Centre, School of Civil and Environmental Engineering, University of New South Wales. He holds a PhD from UNSW, M.E. from Indian Institute of Technology Roorkee, and B.E. from Jiwaji University. His work focuses on statistical applications in hydrology and hydroclimatology, particularly on multivariate bias correction and stochastic downscaling of climate model simulations. Education: PhD: University of New South Wales M.E.: Indian Institute of Technology, Roorkee B.E.: Jiwaji University, Gwalior Raj develops innovative methodologies for climate data processing, including the Multivariate Bias Correction (MBC) and Multisite Rainfall Downscaling (MRD) software. His research addresses climate change impacts on water resources, reservoir management, and flood/drought risk assessment, with applications in Australia, India, and Thailand. Recent publications reveal a focus on climate projections, bias correction frameworks, and hydrological modeling. Key topics include CMIP5 decadal predictions, stochastic rainfall generation, and uncertainty quantification in climate simulations. His work has been supported by ARC Linkage projects, WaterNSW, Bureau of Meteorology, and international collaborations like the Australia-India Strategic Research Fund. He contributes to climate-hydrology software development and has advised on projects related to water infrastructure, agricultural productivity, and groundwater sustainability. Current projects include multivariate bias correction of regional climate simulations, development of the MINBC package, and ensemble modeling for ungauged catchments.

• Risk Analysis
• Actuarial Science
• Insurance Economics
• +3 more

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.

• Statistics
• Nonlinear Models
• Regression Analysis
• +7 more

Prof. Müjgan Tez is a distinguished academic in the field of Statistics, currently serving as a Professor at Marmara University, Faculty of Arts and Sciences, Department of Statistics , and concurrently at Işık University, Faculty of Arts and Sciences, Department of Mathematics . Her career spans decades with significant contributions to statistical methodology and applications. Doctorate: Çukurova University, Mathematics (1985) Postgraduate: Ankara University, Mathematics (1982) Undergraduate: Selçuk University, Mathematics (1980) Her research interests include nonlinear models, regression analysis with measurement error, meta-analysis, survival analysis, robust statistics, and time series forecasting. She applies advanced statistical techniques to interdisciplinary problems in medicine, engineering, and public health. The recent publications reflect a strong trend in methodological statistics, particularly in nonlinear and semiparametric modeling, robust regression, and optimization techniques. Her work bridges theoretical statistics with real-world applications, including medical studies on neurosurgery career choices and pandemic forecasting. Scientific awards: Nato Bursları TUBITAK (1990–1991) Advising and grants: She has supervised over 20 graduate theses and led funded research projects such as "Genetic Algorithm and Jackknife Method Combined for Estimating Compartment Model Parameters" (BAP, 2016). Her academic leadership includes multiple terms as Head of Department in both Mathematics and Statistics at Marmara University. She has served on PhD thesis monitoring committees and contributed to numerous international conferences. Labs and teams: While no specific lab name is mentioned, her collaborative work with researchers like Taylan, Yerlikaya-Özkurt, İnan, and Türkşen suggests active participation in statistical research groups focusing on optimization, fuzzy systems, and functional data analysis.

Bartosz KołodziejekView profile

Associate Professor

• Probability Theory
• Statistics
• Stochastic Fixed Point Equations
• +7 more

Bartosz Kołodziejek is an Associate Professor at the Faculty of Mathematics and Information Science, Warsaw University of Technology. His research lies at the intersection of probability theory and mathematical statistics, with a focus on stochastic fixed point equations, free probability, and graphical models. He actively publishes in top-tier journals and maintains a strong collaborative network. His research interests include: Stochastic fixed point equations and their applications Characterizations of probability distributions on symmetric and homogeneous cones Free probability and free convolution Graphical models and high-dimensional statistics Symmetry in Gaussian models and model selection Perpetuities and tail behavior of stochastic recursions The analysis of his recent publications reveals a consistent trend in theoretical probability and statistical methodology, particularly in high-dimensional inference, random matrix theory, and invariant models. His work often involves deep analytical techniques and connections across fields such as convex analysis, exponential families, and stochastic processes. He has no listed scientific awards on his homepage. While no students or grants are explicitly mentioned, his active publication record and software contributions (such as the gips R package) suggest ongoing research leadership. He collaborates with prominent researchers in probability and statistics across Europe. He is involved in the development of statistical software, notably contributing to the theoretical foundations of the gips package for Gaussian models with permutation symmetry, used in high-dimensional data analysis.

Ingrid VAN KEILEGOMView profile

Professor

• Survival Analysis
• Duration Analysis
• Measurement Error
• +5 more

Ingrid VAN KEILEGOM is a Full Professor at the Catholic University of Louvain (UCL), affiliated with the Institute of Statistics, Biostatistics and Actuarial Sciences. Her research spans survival analysis, econometrics, nonparametric methods, and copula theory, with significant contributions to statistical methodology for censored data and regression models. Research Expertise Professor Van Keilegom's work focuses on developing innovative statistical methods for complex data structures. Key areas include: Survival/duration analysis with cure models and dependent censoring Econometric techniques for instrumental variables and frontier models Semiparametric regression for measurement error problems Copula-based dependence modeling in multivariate settings Publication Trends Her recent publications (2015-2016) demonstrate a strong focus on semiparametric methods for censored data, frontier estimation in econometrics, and goodness-of-fit testing. Common themes include developing robust inference procedures for survival models, addressing endogeneity in econometric applications, and creating validation techniques for complex regression structures. Awards and Honors Fellow, Institute of Mathematical Statistics (2008) Fellow, American Statistical Association (2013) ERC Advanced Grant (2016-2021): Semiparametric inference for complex structural models ERC Starting Grant (2008-2014): M-/Z-estimation in semiparametric statistics Academic Leadership She supervises 5 doctoral students and has graduated 7 PhDs since 2005. Her editorial leadership includes serving as Joint Editor for Journal of the Royal Statistical Society - Series B (2012-2015) and associate roles in 6 other statistical journals. Research is supported by major grants including two European Research Council awards.

David JobstView profile

Researcher

• Statistical Meteorology
• Distributional Regression
• Copula Modeling
• +3 more

David Jobst is a Researcher at the Institute for Mathematics and Applied Informatics within the Faculty of Mathematics, Natural Sciences, Economics and Computer Science at the University of Hildesheim, where he has been employed since August 2020. He teaches undergraduate mathematics courses including Introduction to Analysis, Stochastics, and Advanced Seminars on Series and Infinite Products. Dr. Jobst holds multiple degrees from the Technical University of Munich: a Master of Education and First State Examination in Mathematics and Sports Education (2017-2020), a Bachelor of Science in Mathematics with a minor in Electrical and Information Technology (2015-2019), and a Bachelor of Education in Mathematics and Sports Education (2014-2017). His research focuses on advanced statistical methods for weather forecasting, specializing in distributional regression, copula modeling (particularly Vine Copulas), and probabilistic weather prediction. He develops innovative approaches to postprocess ensemble weather forecasts using machine learning techniques, with particular attention to spatio-temporal dependencies in meteorological data. Jobst's publication record demonstrates a clear progression in developing sophisticated statistical frameworks for weather forecast postprocessing, moving from traditional methods like Ensemble Model Output Statistics (EMOS) to advanced machine learning approaches including gradient-boosted models and vine copula structures. His work bridges theoretical statistics with practical meteorological applications, addressing challenges in temperature, wind speed, and cloud cover forecasting. While no specific awards are documented in the available materials, his research has been presented at numerous international conferences including the European Geosciences Union General Assembly, CMStatistics conferences, and specialized workshops on ensemble postprocessing across Europe. As a university researcher, Jobst participates in academic service through conference organization and peer review activities. His consultation hours are conducted via virtual meeting platforms, reflecting modern academic practices. He maintains a private academic website (jobstdavid.org) where additional research materials and contact information are available.