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George KarabatsosView profile

Professor

• Bayesian Statistics
• Statistical Modeling
• Psychometrics
• +6 more

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.

• Bayesian computations
• Posterior simulation methods
• Econometric and statistical methods
• +7 more

David Gunawan is a Senior Lecturer in Statistics at the School of Mathematics and Applied Statistics, University of Wollongong. He holds a PhD from Monash University and specializes in Bayesian computational methods, bridging methodological development and real-world applications in economics, health, and environmental sciences. His research focuses on posterior simulation techniques (e.g., MCMC, SMC, ABC) and applies these to problems like economic inequality measurement, health outcomes analysis, and offshore engineering challenges. He has secured significant funding from the Australian Research Council (ARC) and other bodies, including leadership in projects on energy infrastructure digitalization and cognitive model inference. Gunawan supervises multiple PhD/Master’s students on topics ranging from wave prediction algorithms to socioeconomic inequality analysis. His work integrates machine learning with traditional statistical methods, exemplified by contributions to phase-resolved wave modeling and spatial statistics. He collaborates with the National Institute for Applied Statistics Research Australia (NIASRA) and maintains a Google Scholar profile at bit.ly/2Gi1PVy .

Owen JonesView profile

Professor

• Operational Research
• Stochastic Modeling
• Environmental Science
• +7 more

Professor Owen Jones is a Chair in Operational Research at the School of Mathematics, Cardiff University. His work focuses on applying mathematical modeling and optimization techniques to address challenges in environment, energy, and sustainability, including renewable energy systems, water management, and disaster risk quantification. Operational Research Stochastic Modeling Environmental Data Analysis Renewable Energy Optimization His research spans environmental science, hydrology, and computational methods, with recent work leveraging approximate Bayesian computation (ABC) and generative adversarial networks (GANs) to model climate impacts, water systems, and biological dynamics. Articles highlight innovations in wastewater surveillance, bat roost detection, and tidal energy optimization. Professor Jones supervises advanced students in areas like spatio-temporal modeling, emergency response simulation, and multifractal processes. He has secured grants for interdisciplinary projects, including UKRO-funded freshwater solutions, GCRF wastewater monitoring, and EU Horizon2020 climate adaptation initiatives. He is part of the Operational Research group at Cardiff University and has collaborated with institutions such as the Australian Department of Agriculture, Fisheries and Forestry, and the EU Horizon2020 program.

Jessi Cisewski-KeheView profile

Associate Professor

• Astrostatistics
• Topological Data Analysis
• Persistent Homology
• +7 more

Jessi Cisewski-Kehe is an Associate Professor in the Department of Statistics at the University of Wisconsin–Madison, part of the School of Computer, Data & Information Sciences. Her research bridges statistics and astronomy, focusing on methodological development for complex, real-world datasets. Her primary research interests lie in astrostatistics , where she develops statistical tools to detect low-mass exoplanets using radial velocity data, disentangling true planetary signals from stellar activity noise. She also specializes in topological data analysis (TDA) , particularly persistent homology , and employs approximate Bayesian computation (ABC) and visualization techniques for interdisciplinary scientific challenges. Her recent publications reflect a strong trend in applying topological and Bayesian methods to astronomical data, especially in exoplanet discovery and stellar variability modeling. These works sit at the intersection of statistics, astronomy, and computational data science. Her scientific awards include the prestigious NSF CAREER Award and the Nellie McKay Fellowship at UW-Madison. She has been awarded multiple competitive grants from NSF and NASA, highlighting the impact and innovation of her research. Jessi has advised research projects and collaborates extensively across disciplines. Her funding includes multiple NSF grants (CAREER, CDS&E, AST) and NASA awards, supporting work in exoplanet detection, nanoparticle-protein interactions, and energy storage systems. She previously held positions at Carnegie Mellon University and Yale University. She leads research initiatives such as ExoStatistics and applies topological data analysis to challenges in astronomy and physical sciences. Her team develops open-source tools, including software for ABC methods and generalized landscapes in TDA.

• Bayesian Statistics
• Approximate Bayesian Computation
• Statistical Modeling
• +6 more

Christopher Drovandi is a Professor at Queensland University of Technology and a Chief Investigator in the Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS). His research spans statistical methodology development and applications across diverse fields including ecology, biomedicine, and environmental science. Dr. Drovandi's primary research interests focus on advancing Bayesian statistical methods, particularly in the areas of: Approximate Bayesian Computation (ABC) and likelihood-free inference Synthetic likelihood methods Sequential Monte Carlo algorithms Bayesian experimental design Statistical computing for complex models His recent work demonstrates a strong emphasis on addressing model misspecification in Bayesian inference, developing robust computational methods for intractable likelihoods, and applying advanced statistical techniques to solve real-world problems in ecology, biomedicine, and environmental science. Drovandi has made significant contributions to both the theoretical foundations and practical applications of modern Bayesian statistics. Dr. Drovandi has received research funding through multiple Australian Research Council grants and collaborates extensively with researchers across disciplines. His work has been published in top-tier statistical and interdisciplinary journals including the Journal of the American Statistical Association, Bayesian Analysis, and PLOS Computational Biology.