New Trends in Bayesian Statistics: BAYSM 2023, Online Meeting, November 13-17, Selected Contributions (Springer Proceedings in Mathematics & Statistics, 511)

Alejandra Avalos Pacheco is a tenure-track Universitätsassistentin at the Institute of Applied Statistics at JKU Linz, Austria, and an affiliated member of the Harvard-MIT Center for Regulatory Science at Harvard University. She earned her PhD in Statistics through the joint CDT program between the University of Warwick and the University of Oxford. Her thesis received the prestigious Savage Award in Applied Methodology. She has held postdoctoral positions at Harvard University and worked at the Dana-Farber Cancer Institute. Additionally, she served as a research fellow at the University of Florence and a non-tenure-track Universitätsassistentin at TU Wien. Her research focuses on creating interpretable, computationally efficient models for large, complex data, particularly in medical applications such as cancer. She specializes in Bayesian and probabilistic machine learning, with expertise in high-dimensional inference, dimensionality reduction, graphical models, data integration and clinical trials. Fan Bu is a tenure-track Assistant Professor in Biostatistics at the University of Michigan. She completed her Ph.D. in Statistics at Duke University and was previously a postdoctoral research fellow at UCLA, where she developed Bayesian methods for large-scale observational health data. Her research spans Bayesian modeling for temporal and spatio-temporal processes, networks, and federated data, with applications in health data science and observational studies for comparative effectiveness and safety and has appeared in leading journals such as the Journal of the American Statistical Association and Statistics in Medicine. An active member of the Observational Health Data Sciences and Informatics (OHDSI) collaborative, Bu contributes to statistical methods development and leads large-scale network studies to improve health decisions and patient care. Beatrice Franzolini is a Researcher at the Bocconi Institute for Data Science and Analytics, Bocconi University, Milan, Italy. She is a statistician specializing in Bayesian statistical theory, methods and application, with a particular focus on Bayesian nonparametrics. Her research encompasses random probability measures, species sampling models, dependent random partitions, and dynamic models. She has published in leading journals such as Biometrika and The Annals of Applied Statistics. Franzolini holds a Ph.D. from Bocconi University and has held research positions at the Agency for Science, Technology, and Research in Singapore, as well as the Division of Biomedical Data Science at the National University of Singapore's medical school. Beniamino Hadj-Amar is a Postdoctoral Fellow in the Department of Statistics at Rice University, Houston, TX. His research focuses on Bayesian methods for analyzing complex dynamical time series, with expertise in latent structure identification, non-stationary and non-linear processes, and sparse data structures. He holds a Ph.D. from the Oxford-Warwick Statistics Programme (OxWaSP). Hadj-Amar's methodological toolkit includes switching models, change-point detection, Bayesian nonparametrics, graphical models, and statistical spectral analysis. His work is applied to neuromodulation, respiratory research, and circadian studies, leveraging diverse datasets such as electrophysiological signals, wearable device data, and fMRI. His contributions have appeared in prestigious journals such as the Journal of the American Statistical Association and The Annals of Applied Statistics.