Keynote Speakers
Professor Sudipto Banerjee
Sudipto Banerjee, Ph.D., is a Professor in the Department of Biostatistics and in the Department of Statistics & Data Science with an affiliate appointment in the UCLA Institute of the Environment and Sustainability. His research expertise includes Bayesian inference and statistical machine learning methods for complex systems involving massive datasets ("BIG DATA"); environmental processes and their impact on public health; spatial data science; spatial epidemiology; stochastic process models; statistical learning from physical and mechanistic systems; survey sampling and survival analysis. His theoretical and methodological contributions in statistics are available in the form of several scholarly publications in highly regarded peer-reviewed journals in statistics, biostatistics and health sciences; an authoritative textbook on spatial statistics; a textbook on linear algebra for statisticians; an edited comprehensive handbook on spatial epidemiology; and two committee reports for the National Research Council of the National Academies. He has served as Principal Investigator of over 14 major federally funded research projects from NIH and NSF primarily devoted to the advancement of statistical theories and methods for space-time processes and their substantive impact. Methods developed by Professor Banerjee are being widely employed in epidemiological and environmental health research to enhance scientific understanding of how environmental factors affect human health over space and time. Dr. Banerjee has made pioneering contributions in several important areas at the interface of spatial data science and health research including spatial survival analysis; space-time mapping and analysis of multiple diseases; statistical inference for mechanistic systems; case studies in complex scientific data analysis; space-time BIG DATA analytics; and statistical computing, data science and software development. He has played prominent roles in substantive public health projects such as “The GuLF Study” (Deepwater Horizon disaster) and is currently overseeing data analysis efforts to evaluate the health effects from the massive natural gas leak disaster in Aliso Canyon in California.
Professor Antonietta Mira
Antonietta Mira is Professor of Statistics at Università della Svizzera italiana (Lugano, Switzerland), Insubria University (Como, Italy), and elected member of the Swiss National Science Foundation Research Council. She is a Fellow of the International Society for Bayesian Analysis, the Institute of Mathematical Statistics, the Lombardy Academy of Science, and elected member of the International Statistical Institute. She serves on the board of the Harvard Data Science Review, the Swiss Statistical Society, and was member of the Swiss Federal Statistics Committee. Her main research interests are Bayesian statistical models for complex data, uncertainty quantification, and efficient Monte Carlo simulation algorithms. She has led multiple competitive research projects, received awards for teaching, science dissemination and research, and served on scientific committees and journal editorial boards, including Statistica Sinica, Bayesian Analysis, and Journal of Computational and Graphical Statistics. Antonietta is also active in public engagement and science communication, has published two award winning books, and led initiatives such as the exhibit Numbed by Numbers! and the Data and Society lecture series. Through television, radio, and journalistic media, she actively promotes the culture of data science.
Invited Speakers
A/Professor Susan Wei
Susan Wei is an Associate Professor in the Department of Econometrics and Business Statistics at Monash University. She previously held a Discovery Early Career Researcher Award (DECRA) and was a visiting faculty researcher at Google DeepMind in Sydney. Her research focuses on statistical machine learning, particularly in Bayesian approaches to deep learning, alongside variational inference and singular learning theory.
Dr Leah South
Dr Leah South is a senior lecturer and DECRA fellow at Queensland University of Technology, with research interests in Bayesian computational statistics methodology. She completed her PhD in 2019 and has since been the recipient of awards for early career excellence, including the Blackwell Rosenbluth Award from the International Society for Bayesian Analysis and the Paul Bourke award from the Academy of the Social Sciences in Australia. She is especially interested in Monte Carlo variance reduction methods and applications of Stein's method in computational statistics.
Professor Dino Sejdinovic
Dino Sejdinovic is a Professor of Statistical Machine Learning at Adelaide University (since 2022) and a Visiting Professor at Nanyang Technological University, Singapore (since 2025). He was previously a Lecturer and an Associate Professor in the Department of Statistics at the University of Oxford (2014–2022), a Fellow of Mansfield College, Oxford, and a Turing Faculty Fellow at the Alan Turing Institute. He held postdoctoral positions at the Gatsby Computational Neuroscience Unit, University College London (2011–2014), and at the Institute for Statistical Science, University of Bristol (2009–2011). He received a PhD in Electrical and Electronic Engineering from the University of Bristol in 2009. His research spans topics at the interface of machine learning and statistical methodology, including large-scale nonparametric and kernel methods, robust and trustworthy machine learning, causal inference, and uncertainty quantification.
Professor Fabrizio Ruggeri
Fabrizio Ruggeri, holding degrees from Milano, Carnegie Mellon, and Duke, is a Senior Fellow at IMATI in Milano under CNR, with a tenure spanning from 1988 to May 2023, serving as Research Director from 2001. He contributed to Ph.D. programs at Milano-Bicocca, Pavia, and international institutions like QUT, NYU, Universidad Carlos III, ICMAT-CSIC, and Universidad de Valparaiso. ISI President (2025-2027), he's led ENBIS, ISBA, ISBIS, and served as ISI Vice President. Recognized as Fellow by IMS, ASA, and ISBA (earning the inaugural Zellner Medal), he's an ENBIS Honorary Member, editing journals and directing CNR-IMATI's summer school while chairing workshops on Bayesian Inference in stochastic processes. With over 200 publications and 6 books, his focus spans Bayesian Statistics, Decision Analysis, reliability in industrial applications, and recent forays into healthcare fraud and Adversarial Risk Analysis, along with interests in concentration functions and wavelets.
Professor Michael Smith
Michael Smith completed his PhD at the University of New South Wales in 1996 and has held the Chair of Management in Econometrics at Melbourne Business School (MBS) since 2007. He has been a past Alexander von Humboldt fellow and an Australian Research Council Future Fellow. His research focuses on Bayesian computational methodology and its application to large models and datasets that arise in business, economics and elsewhere. He has made contributions to Bayesian variable selection, copula modelling, spatial and time series methodology, and to applications in marketing science, neuroimaging, macroeconomics and business forecasting. Much of his work has been joint with graduate students and post-doctoral researchers. Current projects include new modular and variational inference methodologies, along with scalable copula models for time series and regression data. At MBS he currently teaches data analysis to MBA students and risk analytics to students in the Master of Business Analytics. He has extensive experience with executive education.