Brendan Kinnane Beare is a Professor of Econometrics at the University of Sydney , affiliated with the School of Economics under the Faculty of Arts and Social Sciences. His research focuses on econometric theory, time series analysis, copula modeling, and stochastic processes. Education: PhD in Economics (2007) from Yale University Research areas: Time series, copulas, Markov modulation, unit root testing, financial econometrics Recent work includes stochastic arbitrage with options, Pareto exponents in economic models, and tests for copula symmetry Beare's publication trends span econometric theory, applied statistics, and financial modeling, with a focus on mathematical rigor and empirical relevance. He teaches undergraduate and postgraduate econometrics courses and participates in international seminars on unit roots and cointegration.
Steven Culpepper is a Professor at the University of Illinois at Urbana-Champaign with joint appointments in the Department of Educational Psychology (College of Education), Department of Statistics, Department of Psychology, and Beckman Institute for Advanced Science and Technology. He holds a PhD in Educational Psychology from the University of Minnesota (2006) and BS in Economics from Bowling Green State University (2001). His research program focuses on developing advanced psychometric models and statistical methods for educational and psychological measurement. Primary research areas include: Latent class and hidden Markov models for cognitive diagnosis Large-scale testing and assessment methodologies Bayesian inference for restricted latent class models Statistical computing applications for psychometrics Longitudinal modeling of learning trajectories His recent publications demonstrate consistent focus on improving parameter estimation for diagnostic classification models, with methodological innovations in Bayesian computation, model identifiability, and handling of polytomous response data. Research consistently addresses fundamental challenges in educational measurement through rigorous statistical frameworks.
Constance van Eeden (1927–2021) was an Honorary Professor at the University of British Columbia (UBC) and held academic positions at institutions such as the University of Minnesota, Université de Montréal, and Université du Québec à Montréal. She contributed significantly to statistical theory, particularly in estimation in restricted parameter spaces, decision theory, and nonparametric methods. Her career spanned over five decades, during which she supervised numerous PhD and MSc students. Van Eeden was recognized with prestigious awards, including the 1990 Gold Medal from the Statistical Society of Canada and Fellowships from the Institute of Mathematical Statistics and American Statistical Association. Her research interests included foundational statistical problems such as parameter estimation under constraints, selection procedures, and robust estimation. She played a pivotal role in developing statistical programs in Canada and contributed to the establishment of the Constance van Eeden Endowment Fund at UBC, supporting statistical education and research initiatives. Van Eeden's work also involved editorial roles in journals like the Annals of Statistics and Canadian Journal of Statistics . Her legacy includes over 70 publications, including influential books and articles on estimation theory. The van Eeden Fund continues to support distinguished lectures, summer schools, and student awards, reflecting her commitment to advancing statistical science.
Michael McDermott, PhD, is a Professor of Biostatistics, Neurology, and the Center for Health + Technology at the University of Rochester Medical Center. He holds an academic appointment in the Department of Biostatistics and Computational Biology. His research focuses on statistical methodology including order-restricted inference, clinical trial design, diagnostic test evaluation, and meta-analysis. Dr. McDermott has collaborated extensively on neurological disorders such as Parkinson’s, Huntington’s, and multiple sclerosis, and has a joint appointment with the Department of Neurology. Educated at the University of Rochester (PhD, 1989), his work bridges statistical theory and clinical application. He has authored influential papers on hypothesis testing under order constraints and developed methods for verification bias correction in diagnostic studies. McDermott serves on editorial boards for journals like International Statistical Review and Movement Disorders , and has held leadership roles in professional societies. His research interests span statistical theory (e.g., multivariate analysis, ROC curves) and applied biostatistics (e.g., dose-response modeling, clinical trial optimization). McDermott’s methodological contributions address challenges in medical research design, including adaptive trial phases and missing data imputation techniques. He has been recognized as a Fellow of the American Statistical Association and an Elected Member of the International Statistical Institute. Collaborative efforts include leading national/international groups studying neurological diseases and serving as an advisor for the T32 Training Grant in Biostatistics. His lab focuses on advancing statistical tools for translational medical research while maintaining active involvement in neurology-related clinical studies.
Dr. Katrina McDonough serves as an Honorary Research Fellow in the School of Psychology with the academic rank of Research Fellow. Her work bridges cognitive and social psychology through experimental investigations of social perception mechanisms. Her research program centers on: Social Perception : How observers interpret others' actions through predictive goal inference Visual Perspective Taking : Cognitive simulation of others' viewpoints at implicit and explicit levels Action Observation : Neural and perceptual processes underlying movement understanding Human-Robot Interaction : Extension of social cognitive principles to artificial agents Mental Imagery : Role of internal simulation in perceptual biasing Cognitive Psychology : Fundamental mechanisms of prediction in social contexts Analysis of her 13 publications (2018-2025) reveals a cohesive trajectory demonstrating that higher-order goals generate action kinematics predictions which shape perceptual representations. Key findings establish that visual perspective taking operates through perceptual simulation unaffected by motor restriction (a non-embodied process), and recent work extends these mechanisms to human-robot interactions where appearance alone doesn't facilitate perspective taking. Her methodology combines controlled laboratory experiments with online replications. Scientific awards: No awards were documented in the source material. Advising and grants: No information regarding student supervision or research funding was provided. Labs and teams: No specific research groups, laboratories, or collaborative teams were referenced in the available information.
Dietrich W. Kuhlmann is a Clinical Professor in the Department of Biostatistics at the University at Buffalo, serving as the Undergraduate Director. He holds academic affiliations within the School of Public Health and Health Professions. Education and Training: PhD in Applied Mathematics, University of Missouri MS in Mathematics, University of Missouri BS in Mathematics, Illinois College Research Interests: Dr. Kuhlmann's work focuses on order restricted statistical inference , particularly in concave regression, as well as logistic regression, design of experiments, statistical simulation, statistics in legal contexts, and mixed media modeling. His research bridges theoretical statistical methods with practical applications across disciplines. Awards/Grants: No awards or grants are explicitly listed in the provided text. Advising: No current advisees or students are documented in this profile. Labs/Teams: No specific labs or collaborative teams are mentioned in the text.
Xiaomi Hu is a Professor of Statistics at Wichita State University (WSU), affiliated with the Department of Mathematics, Statistics, and Physics within the Fairmount College of Liberal Arts and Sciences. He holds a Ph.D. from the University of Missouri-Columbia (1993) and previously served as a Visiting Assistant Professor at the University of South Carolina before joining WSU in 1994 as an Assistant Professor. His office is located in Room 323, Jabara Hall, with online office hours from 3:45–4:45 PM Monday-Thursday. Education: Ph.D. in Statistics, University of Missouri-Columbia (1993) Professional Journey: Tenure-track Assistant Professor at WSU (1994), promoted to Professor Research interests focus on statistical methodologies, including order restricted inference, multivariate order constraints, and applications in aviation composite materials (A-basis/B-basis parameters). His early work established the unbiasedness of likelihood ratio tests under closed convex cone constraints. Recent research involves pseudo restricted maximum likelihood estimation and algorithmic advancements for cone projections. No specific academic awards or grants are listed in the provided materials. He advises Master's theses on aviation material quality parameters but no student names are disclosed. Teaching responsibilities include advanced courses such as Matrix Theory (Stat 701) and Theory of Linear Models I (Stat 872), emphasizing matrix analysis, multivariate distributions, and linear model theory.
Antar Bandyopadhyay is a Professor at the Indian Statistical Institute (ISI) , serving as Head of the Delhi Centre since 2023. He was previously the Professor-in-Charge of the Theoretical Statistics and Mathematics Division (2020–2022). He spent the academic year 2013–2014 as a Visiting Associate Professor at the Department of Statistics, University of California, Berkeley . Education : PhD in Statistics (UC Berkeley, 2003), MA in Statistics (UC Berkeley, 2000), M.Stat. (ISI Kolkata, 1998), B.Stat. (ISI Kolkata, 1996). Research Interests : Theoretical and applied probability, focusing on discrete problems arising from combinatorics, statistical physics, and computer science. Key areas include random graphs , probability on trees , recursive distributional equations , branching random walks , percolation theory , interacting particle systems , Markov chains mixing , random walks in random environments , and urn models . He also explores theoretical statistics and probability questions emerging from it. Publication Trends : His recent work (2006–2025) spans infinite-color urn models , branching random walks , percolation , random graphs , and random walks in dynamic environments . Collaborations include researchers at UC Berkeley, Chalmers University, ISI Kolkata, and the University of Minnesota. Scientific Awards : Outstanding Graduate Student Instructor Award (UC Berkeley, 2002) Teaching Effectiveness Award (UC Berkeley, 2002) Advising and Grants : He has mentored Gursharn Kaur , Debleena Thacker , and others. His work has been supported by institutions such as UC Berkeley, Chalmers University, and the Institute for Mathematics and Its Applications . Labs and Teams : He actively collaborates with the Theoretical Statistics and Mathematics Unit at ISI Delhi and has organized the weekly seminar series (2006–2010), co-organizing workshops like the Lectures on Probability and Stochastic Processes (2006–2014).
Professor Ralf Brüggemann is a full-time faculty member at the University of Konstanz , holding the Chair of Statistics and Econometrics since October 2007. He completed his Habilitation in Time Series Econometrics at Humboldt-Universität zu Berlin in 2007 and received his Ph.D. in Economics in 2003 for work on VAR model reduction techniques. Education : Habilitation: "Topics in Time Series Econometrics", Humboldt University Berlin (2007) Ph.D.: Economics, Humboldt University Berlin (2003) Diplom: Economics, Humboldt University Berlin (1999) His research spans Time Series Econometrics with focus on Cointegrated VAR Models , Structural VAR/VECM , Forecasting Methods , and Empirical Macroeconomics . Key contributions include methodological work on structural identification, variable selection in high-dimensional VAR, and monetary policy analysis using microeconomic data. Recent publications address External instruments in SVAR identification (2022) Directed graphs for VAR variable selection (2022) Stochastic aggregation weights in forecasting (2023) Asymmetric impulse responses in European financial markets (2014) with methodological innovations in heteroskedasticity-robust inference and stochastic aggregation weights. Scientific Awards : Jean Monnet Fellow, European University Institute (2003-2004) He leads research on monetary policy transmission mechanisms and macroeconomic risk through collaborative projects with institutions like the German Research Foundation Collaborative Research Center 649 (2005-present) and serves as editor for the Journal of Economics and Statistics special issue on Economic Forecasts (2011).
Balgobin Nandram is a Professor of Statistics at the Department of Mathematical Sciences , Worcester Polytechnic Institute (WPI). With a PhD in Statistics from the University of Iowa (1989) and a Master's from Imperial College London (1981), his career spans academic leadership, global research collaborations, and methodological innovation. BS, Mathematics & Physics, University of Guyana (1977) BA, Mathematics Education, University of Guyana (1979) MS, Statistics, Imperial College London (1981) PhD, Statistics, University of Iowa (1989) His research focuses on Bayesian statistics applied to survey methodology, small area estimation, categorical data analysis, and nonignorable missing data. He has developed computational methods for health statistics and data science, with significant applications at the National Center for Health Statistics (NCHS) and agricultural surveys. While his 2010-2002 refereed publications highlight Bayesian hierarchical models for BMI data and COPD mortality mapping, his invited presentations (2018-2015) emphasize logistic regression in small areas, multinomial count analysis, and projective inference. These works bridge theoretical advances with practical applications in public health and survey research. 2003 : Fellow, American Statistical Association (ASA) 2006 : SPAIG Award (WPI-NCHS partnership) 2014 : Visiting Global Scholar, Kyungpook National University 2004 : Sigma Xi membership As an advisor, he has mentored PhD students at WPI, the University of the Philippines, and Kyungpook National University, though specific advisees are not named. His grants include CDC collaborations on health monitoring and NCHS research fellowships. He leads international research teams in South Korea, the Philippines, and India, including partnerships with Yonsei University, DLSU, and Banaras Hindu University. His work at NCHS (1999-2000) and subsequent sabbatical (2003/2004) solidified his role in health statistics.
Stephen B. Wicker is a Professor at Cornell University's College of Engineering with a distinguished research career spanning over three decades. His work has significantly impacted the fields of wireless communications, information theory, privacy, and security. Wicker has published extensively in top-tier journals and conferences, demonstrating consistent scholarly productivity from the early 1990s through 2021. His research interests include: Wireless network security and privacy Information theory and error control coding Location-based services and privacy implications Digital rights management and consumer protection Blockchain applications for IoT environments Ethical considerations in technology development Legal implications of digital technologies Wicker's recent publications reveal a strategic evolution in his research focus, increasingly addressing the intersection of technical systems with legal and ethical considerations. His work on smartphone privacy and the Fifth Amendment, eBook surveillance, and zero-day exploits demonstrates his unique ability to bridge technical expertise with societal implications. This interdisciplinary approach has positioned him as a thought leader in understanding how technological capabilities intersect with constitutional rights and civil liberties. His scholarly impact is evident through his extensive collaboration network, with significant work alongside researchers like Kolbeinn Karlsson on blockchain for IoT applications, Dipayan P. Ghosh on eBook privacy issues, and Shion Guha on social surveillance in location-based networks. His research has appeared in prestigious venues including Communications of the ACM, IEEE Proceedings, and IEEE Transactions on Information Theory. Wicker has mentored numerous students who have become productive researchers in their own right, including Xiao-an Wang, Hazer Inaltekin, and Kolbeinn Karlsson, who frequently appear as co-authors on his publications. His work continues to address emerging challenges at the intersection of technology, law, and society, making significant contributions to both academic discourse and public policy discussions.
Florian Schwarz is a Professor and Undergraduate Chair in the Linguistics Department at the University of Pennsylvania's School of Arts & Sciences. He also serves as Associate Director for Education of mindCORE (Penn's hub for the integrative study of the mind) and is a member of the Graduate Group in Psychology. Education Ph.D., University of Massachusetts Amherst, 2009 Professor Schwarz's research focuses on formal semantics and pragmatics of natural language, combining theoretical linguistics with experimental methods from psycholinguistics. His work investigates how humans process meaning in context, examining foundational issues such as presupposition projection, scalar implicatures, and reference resolution. He employs experimental methodologies including visual world eye-tracking and online experiments to uncover the cognitive mechanisms underlying linguistic interpretation. His research program integrates formal tools from linguistic semantics with empirical approaches to better understand the interplay between grammatical knowledge and contextual information in language comprehension. Analysis of Professor Schwarz's recent publications reveals a consistent focus on experimental approaches to semantic and pragmatic phenomena. His work demonstrates a strong emphasis on the cognitive reality of linguistic representations, with particular attention to how contextual factors influence interpretation. There's a clear trajectory toward increasingly sophisticated experimental designs that tease apart competing theoretical accounts, especially regarding presupposition processing and the role of discourse structure in interpretation. His research shows growing integration of social and cognitive factors in understanding linguistic meaning. Professor Schwarz has been actively involved in academic service and community building through numerous presentations and workshops. He has presented at institutions including Rutgers, UMass Amherst, and New York University, and has organized events such as MACSIM at Penn. He has also taught specialized sessions at the LSA Summer Institute and conducted workshops on experimental methods. Professor Schwarz leads the Experimental Study of Meaning Lab at Penn, where his team has developed PCIbex - a significant open-source tool for implementing and hosting online experiments. This platform has become widely used by researchers in linguistics and beyond, facilitating both in-lab and remote experimental research while promoting open science practices.
Anne-Laure Fougères is a Professor in the Department of Mathematics at Claude Bernard University Lyon 1, affiliated with the Camille Jordan Institute (CNRS UMR 5208) under the Probability, Statistics and Mathematical Physics team. Her research spans Statistics, Probability, and Mathematical Physics, with a focus on Extreme Value Theory and environmental/climate applications. Her work includes probabilistic forecasting calibration, spatial extreme risk modeling, multivariate dependence structures, and functional estimation under shape constraints. She has published extensively in top-tier journals like International Journal of Forecasting , Annals of Statistics , and Extremes , often with applications in hydrology and meteorology. Recent research trends show emphasis on Archimax copulas, ensemble forecast post-processing, and Bayesian model averaging for multivariate extremes. She leads the Probability, Statistics and Mathematical Physics team and serves on the French Statistical Society Council. Teaching responsibilities include the "Mathematics for the Environment and Climate" section of the "Maths in Action" master's program, courses at Polytech Lyon, and interdisciplinary Climate and Transitions instruction. She has supervised 9 PhD students, including Anne Sabourin and Alexis Huet, with research grants from Météo-France, EDF, and CIFRE.
Omer Ozturk is a Professor of Statistics at The Ohio State University (OSU), affiliated with the Department of Statistics. He joined the faculty in 1996 and has held editorial roles at journals including Environmental and Ecological Statistics, and Communications in Statistics. His research focuses on robust and nonparametric statistical methods, particularly in developing efficient sampling designs that minimize costs while maximizing information through auxiliary variables and ranking techniques. He has been funded by the NSA and NSF and actively collaborates with the U.S. Census Bureau as a Summer at Census Scholar. Education: PhD in Statistics from Penn State University (1994). Research Interests: Omer’s work emphasizes statistical inference under relaxed distributional assumptions, including robust methods, nonparametric techniques, and uncertainty quantification. He specializes in finite population sampling designs, such as ranked set sampling and judgment post-stratification, which leverage auxiliary information to enhance efficiency. His contributions span meta-analysis, spatial statistics, and Bayesian mixture modeling, with applications in epidemiology, environmental science, and agriculture. Articles Overview: His recent work addresses meta-analysis of survival times, spatially balanced sampling, and Bayesian modeling with ranked set samples. He developed the R package 'metamedian' for median-based meta-analysis and explored trade-offs in spatial sampling efficiency. His research consistently emphasizes practical applications in reducing sampling costs while improving statistical precision. Awards: ASA Fellow (2010). Advising & Grants: Omer has received grants from NSA and NSF, and his work frequently involves collaboration with institutions like the U.S. Census Bureau. Although specific student advisees are not listed, his research outputs suggest involvement in training graduate students in statistical methodology and applications. Labs/Teams: While no specific lab names are mentioned, his collaborations span statistical methodologies in environmental and medical research contexts, leveraging interdisciplinary teams for applied problems.
Buddika Peiris is an Associate Professor of Teaching in the Department of Mathematical Sciences at Worcester Polytechnic Institute (WPI), where he also serves as the Coordinator of the Applied Statistics Master's Program and the Statistics Consulting Lab. His academic career at WPI has progressed from Postdoctoral Fellow (2014-2016) to Assistant Teaching Professor (2016-2021) and now to Associate Professor of Teaching (2021-present). BS in Mathematics from University of Sri Jayewardenepura (2005) MS in Mathematical Statistics from Southern Illinois University, Carbondale (2010) PhD in Mathematical Statistics from Southern Illinois University, Carbondale (2014) Dr. Peiris's research focuses on developing new statistical methodologies with applications across various fields. His primary research areas include Order Restricted Inference, Meta-Analysis, Bayesian Statistics, and Actuarial Science. His work addresses complex problems in public health, weather forecasting, Food Science, and various industries where traditional 'ad hoc' methods are reaching their limits. His teaching philosophy emphasizes clear communication of statistical concepts, exposing students to statistical analysis structures, and teaching effective communication of statistical results to diverse audiences. His publication record demonstrates consistent contributions to statistical methodology, particularly in constrained regression models, meta-analysis techniques, and Bayesian approaches. His work spans theoretical developments with practical applications in biomedical research, environmental studies, and industrial settings. The publications show a progression from foundational work on order restricted inference to more complex applications involving circular-linear regression and meta-analysis of cylindrical time series data. As an educator, Dr. Peiris has supervised numerous graduate students through WPI's Master's program, with projects spanning healthcare analytics, financial applications, environmental modeling, and industrial statistics. His teaching portfolio includes both undergraduate and graduate courses in probability, mathematical statistics, regression analysis, experimental design, and specialized topics in statistical methodology. Through his role as Coordinator of the Statistics Consulting Lab, he facilitates connections between statistical expertise and real-world problems across disciplines. His current research continues to develop constrained prediction intervals, diagnostic tests in regression, and applications of statistical methodology to forensic analysis and plant science.
