Johanna Genest Nešlehová is a Professor at the Institute for Statistics and Mathematics at Vienna University of Economics and Business. She previously served as Assistant Professor at McGill University (2009-2020) and holds a PhD in Mathematics from Carl von Ossietzky University of Oldenburg. Her research spans statistics, probability theory, and financial mathematics with focus areas including multivariate analysis, dependence modeling, copulas, and statistical methods for financial applications. She serves as editor for the Canadian Journal of Statistics and Statistics and Risk Modeling. Research publications demonstrate focus on multivariate statistical methods, dependence modeling, and applications in financial mathematics. Recent work includes stochastic decomposition methods, causal inference techniques, and rank-based estimation. Carrie M. Derick Award for Graduate Supervision and Teaching CRM-SSC Prize
John Hughes is an Associate Professor and Chair of the Department of Biostatistics and Health Data Science at Lehigh University's College of Health. He holds a PhD in Statistics from Penn State University and has over 29 years of experience in academia, with previous appointments at institutions like the University of Minnesota and the University of Colorado. His research focuses on methodological advancements in statistical modeling for dependent data, Bayesian methods, and statistical computing. His interdisciplinary work spans environmental health, bioimaging, magnetic resonance safety, and vaccine hesitancy. He has developed numerous software packages for R and Perl, including copCAR and batchmeans. Education: PhD in Statistics, Penn State University MS in Statistics, Penn State University MS in Applied Computer Science, Frostburg State University BS in Mathematics and Computer Science, Frostburg State University Teaching: Courses include Advanced R Programming, Biostatistics, Population Health Data Science, and Computational methods. His research interests emphasize spatial and spatiotemporal data analysis, with applications to public health and medical imaging. He has consulted for organizations such as the Minnesota Center for Chemical and Mental Health and Temple University. His software contributions include packages like krippendorffsalpha for agreement measurement and copCAR for spatial regression modeling. Recent work focuses on improving statistical inference methods, analyzing vaccination refusal patterns, and developing frameworks for copula-based agreement coefficients. His articles highlight innovations in Bayesian computation, spatial epidemiology, and nonparametric statistics. Dr. Hughes leads academic initiatives in biostatistics and health data science, fostering interdisciplinary collaborations and advancing statistical methodologies for real-world applications.
Shai Gorsky is a Senior Lecturer at the University of Massachusetts Amherst, based at the Newton Mount Ida Campus in the School of Design. He holds a PhD in Statistical Science from Duke University and conducts research in Bayesian nonparametrics, multi-scale modeling, and social science applications. His published work includes developing statistical methods for multivariate dependence testing, flow cytometry analysis, and modeling cardiac systems. Dr. Gorsky teaches courses in statistical methods and their applications in design-related fields.
Haben Michael is an Assistant Professor and Director of the Stat MS & PhD Graduate Admissions program at the University of Massachusetts Amherst. He holds a PhD in Statistics from Stanford University and is affiliated with the Department of Mathematics and Statistics. His primary research focuses on statistical methodology, particularly in meta-analysis, publication bias correction, and AUC analysis. He also develops computational tools for statistical inference, including an R package addressing Begg's test for publication bias. Education: PhD in Statistics, Stanford University. Research interests include: Statistical methodology for publication bias correction Meta-analytical techniques Diagnostic testing and AUC estimation Time-varying treatment effects in causal inference Recent work emphasizes improving statistical methods for clustered data analysis, bias adjustment in meta-studies, and robust estimation in marginal structural models. His programming repositories include contributions to correcting Begg’s test, demonstrating practical applications of his theoretical research. Affiliations: Department of Mathematics and Statistics, University of Massachusetts Amherst. Office located at LGRT 1336.
Ted Westling is an Assistant Professor in the Department of Mathematics and Statistics at the University of Massachusetts Amherst. His research focuses on nonparametric statistics, causal inference, survival analysis, and shape-constrained inference. He holds a PhD in Statistics from the University of Washington and a BS in Mathematics from Stanford University. His work emphasizes methodological advancements in causal inference, particularly in observational studies and survival analysis. Notable contributions include developments in nonparametric causal effect estimation, machine learning integration for treatment-specific survival curves, and isotonic regression techniques for monotone functions. Westling's recent publications explore topics such as debiased covariate-adjusted regression, clustered observational studies, and statistical robustness in causal hypothesis testing. His research bridges theoretical statistics with applied problems in healthcare, epidemiology, and social network analysis. He is actively involved in interdisciplinary collaborations, addressing challenges in global health, vaccine efficacy evaluation, and emergency medical services demand forecasting. His methodological innovations aim to enhance the reliability and applicability of statistical methods in real-world settings.
Marinho Bertanha is a tenured Associate Professor of Economics at the University of Notre Dame, serving as concurrent faculty in Statistics and a faculty fellow at the Kellogg Institute. His academic work bridges econometrics and applied microeconomics with significant contributions to causal inference methodologies. His educational background includes a Ph.D. in Economics from Stanford University (2015), an M.A. from Fundação Getúlio Vargas in Rio de Janeiro (2009), and a B.A. from Universidade de São Paulo (2006). Prior to his current position, he completed postdoctoral work at the University of Louvain and served as a visiting professor at the University of Chicago. Bertanha's research focuses on causal inference , resampling methods , and policy evaluation , with particular expertise in regression discontinuity designs, bunching estimation, and permutation testing. His methodological innovations address critical challenges in econometric identification and statistical inference, especially in contexts with strategic reporting and imperfect compliance. His publication record shows consistent high-impact output in top journals including the Journal of the American Statistical Association , Journal of Econometrics , and Review of Economics and Statistics . Recent work demonstrates increasing focus on practical implementation through software development (Stata and MATLAB packages) that has become standard in empirical economics research. Through his concurrent appointment in Statistics and faculty fellowship at the Kellogg Institute, Bertanha contributes to interdisciplinary research initiatives focusing on economic policy evaluation and statistical methodology development. His current work-in-progress examines causal effects in matching mechanisms, returns to education with strategic reporting, and tax elasticity estimation across multiple countries.
Igor Cialenco is a Full Professor in the Department of Applied Mathematics at Illinois Institute of Technology (College of Computing). He holds a Ph.D. in Applied Mathematics from the University of Southern California (2007). Research Interests : Quantitative Finance, Statistical Inference for Stochastic PDEs, Stochastic Control, Environmental Finance, and Functional Analysis. Professional Roles : Managing Editor for the International Journal of Theoretical and Applied Finance, and editorial board member of multiple journals including SIAM Journal on Financial Mathematics. Research Trends : His recent publications focus on stochastic modeling in environmental finance (e.g., groundwater markets), robust control under model uncertainty, and statistical inference for SPDEs. The keywords span Financial Mathematics, Stochastic Processes, Environmental Modeling, and Statistical Learning. Scientific Awards : 2024 Researcher to Know (ISTC), 2024 College of Computing Dean's Excellence Award for Research, multiple Dean's Excellence awards in teaching and research. Mentorship & Funding : Mentored numerous PhD and MS students in quantitative finance and SPDEs. Current NSF grant DMS-2407549 supports research on stochastic modeling for water rights management. Past NSF grants include DMS-1907568 and DMS-0908099.
George Karabatsos is a Professor at the University of Illinois at Chicago (UIC), affiliated with the Department of Educational Psychology. His research focuses on developing and applying Bayesian statistical models, computational methods, and psychometric frameworks for complex data analysis. He has contributed to areas such as Bayesian nonparametrics, meta-analysis, causal inference, and educational measurement. His work has been supported by grants from the National Science Foundation, the Spencer Foundation, and the National Institutes of Health. Karabatsos has authored a menu-based statistical software package for analyzing data using over 100 models and served as an Associate Editor for journals like Psychometrika and Computational Statistics and Data Analysis . Education: PhD in Measurement, Evaluation, and Statistical Analysis (MESA) from the University of Chicago, 1998. Research interests include Bayesian modeling, computational statistics, psychometrics, and applications in education and biostatistics. His recent work emphasizes Bayesian methods for addressing issues like hidden bias, sensitivity analysis, and local dependence in statistical inference. Publications span topics such as nonparametric mixture models, meta-analytic techniques, and software development. His articles reflect a focus on methodological advancements and their practical implementation in real-world problems. Grants and advising: Karabatsos has led numerous research projects funded by major institutions. While specific student advisees are not listed, his software contributions indicate involvement in training researchers through accessible tools. Labs/Teams: He is the lead developer of a Bayesian statistical software package, fostering collaboration in applied statistical research.
Dong Li is a Professor of Economics at the University of Texas at Dallas (UT Dallas), affiliated with the School of Economic, Political and Policy Sciences. His research focuses on econometrics, industrial organization (particularly antitrust issues), financial economics, and the Chinese economy. He holds a Ph.D. in Economics from Texas A&M University (2000), an M.A. in Quantitative Economics from Huazhong University of Science & Technology (1994), and a B.A. in Quantitative Economics from the same institution (1991). Li's work emphasizes methodological contributions to panel data models, spatial econometrics, and semiparametric estimation techniques. His recent studies include analyses of cartel behavior in agricultural markets, military aid's impact on terrorism, and the implications of securities transaction taxes in emerging markets. His research bridges theoretical econometrics with applied policy questions, particularly in antitrust and financial regulation contexts. His articles span topics ranging from Bayesian auction analysis to China's economic policies, showcasing interdisciplinary rigor. While no specific awards are noted, his extensive publication record reflects sustained academic influence. His research often addresses practical economic challenges, such as optimizing college admissions systems and evaluating currency valuation impacts on macroeconomic variables like inflation and output growth.
Eduardo García-Portugués is an Associate Professor at the Department of Statistics, Carlos III University of Madrid. He holds a PhD in Statistics and Operations Research from the Universidade de Santiago de Compostela (2014). Previously, he was a Postdoctoral Fellow at the University of Copenhagen's Department of Mathematical Sciences (2015–2016) and an Assistant Professor at UC3M (2016–2021). He serves as an Associate Editor for Journal of Computational and Graphical Statistics , Annals of the Institute of Statistical Mathematics , and other journals. His research focuses on statistical methodologies for complex and non-Euclidean data, emphasizing directional statistics, kernel smoothing, and functional data analysis. He develops nonparametric and computational tools for analyzing hyperspherical, toroidal, and circular data, with applications in fields like finance and bioinformatics. His work includes advancements in goodness-of-fit tests, diffusions on manifolds, and optimal stopping problems. He leads the Statistics Reading Club and has secured grants for projects such as STENED (Stein-based goodness-of-fit tests for non-Euclidean data). He advises multiple PhD students, including Diego Serrano, Vinícius Litvinoff, and Paul Axmann. His contributions span theoretical developments and practical implementations, including R packages like rotasym and sphunif for directional data analysis.
Barış Sürücü is a Professor in the Department of Statistics at Middle East Technical University (METU), where he has held academic roles since 2004. He previously served as Deputy Rector at METU (2012-2016) and as a Visiting Professor in the Department of Business at Boğaziçi University (2016-2017). His research spans robust statistics, statistical distributions, goodness-of-fit tests, and applications in reliability engineering, environmental statistics, and big data analytics. PhD in Statistics, METU (2003) MSc in Statistics, METU (1999) BSc in Statistics, METU (1996) His work focuses on developing statistical methods for reliability and survival models, outlier detection, ranked set sampling, and environmental data analysis. His publications address theoretical and applied challenges in goodness-of-fit testing, parameter estimation, and graphical reliability analysis tools. Dr. Sürücü's scientific contributions include 15+ publications in journals such as IEEE Transactions in Reliability and Environmental and Ecological Statistics, with recurring themes in multivariate distribution validation, computational efficiency, and applications to engineering and environmental sciences. Academic Achievement Award, METU (2010) Young Researcher Award, METU Development Foundation (2009) NATO Science Scholarship, TÜBİTAK (2003)
Zhigen Zhao is an Associate Professor and Beury Research Fellow at the Fox School of Business and Management, Temple University, within the Department of Statistics, Operations, and Data Science. He holds a Ph.D. from Cornell University (2009) and specializes in Bayesian/empirical Bayesian statistics, high-dimensional data analysis, and bioinformatics. His research is supported by the National Science Foundation. Dr. Zhao’s research focuses on developing statistical methodologies for high-dimensional problems, including selective inference, multiple comparisons, and sufficient dimension reduction. His work bridges theoretical advancements with practical applications in healthcare, genomics, and predictive analytics. Key contributions include Bayesian hierarchical models for electronic health records and novel techniques for controlling false discovery rates in genomic studies. He teaches courses such as Intermediate Statistics, Regression and Predictive Analytics, and Statistical Methods for Business Research at both undergraduate and graduate levels. His recent publications appear in top-tier journals like the Journal of the American Statistical Association and Journal of the Royal Statistical Society, Series B. Dr. Zhao’s grants include NSF funding for high-dimensional statistical research. He advises on methodological challenges in data science and collaborates on projects involving healthcare analytics, genomics, and machine learning applications.
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.
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.
Pål Christie Ryalen is a Researcher in the Department of Biostatistics at the University of Oslo's Institute of Basic Medical Sciences. His academic journey includes a Master's in Industrial Mathematics from NTNU (2015), a PhD in Biostatistics from UiO (2019), a research position funded by UiO and the Cancer Registry of Norway (2019-2020), and a postdoctoral fellowship at EPFL (2020-2022). His research focuses on causal inference methodologies and survival analysis, particularly in continuous-time frameworks. Key areas include graphical criteria for causal effect identification, recurrent event analysis, and applications in oncology and cardiology. He is affiliated with the Causal Inference and Event History Analysis research group. Recent publications highlight work on treatment strategy comparisons for cardiovascular patients, differential equation-based cancer prognosis models, and foundational contributions to marginal structural models. His work bridges theoretical biostatistics with practical clinical applications, emphasizing rigorous methodological development.
