Seminar by Prof. Sudipto Banerjee from Department of Biostatistics, University of California, Los Angeles

Geographic Information Systems (GIS) and related technologies such as remote sensors, satellite imaging and portable devices that are capable of collecting precise positioning information, even on portable hand-held devices, have spawned massive amounts of spatial-temporal databases. Spatial "data science" broadly refers to the use of technology, statistical methods, computational algorithms to extract knowledge and insights from spatially referenced data. Applications of spatial-temporal data science are pervasive in the natural and environmental sciences; economics; climate science; ecology; forestry; and public health. With the abundance of spatial BIG DATA problems in the sciences and engineering, GIS and spatial data science will likely occupy a central place in the data revolution engulfing us. This talk will provide an overview of the various challenges data scientists are encountering in analyzing massive spatial-temporal data sets in diverse applications. I will begin with a description of different types of spatial data structures and the relevant data analytic questions they pose. I will show, with several examples, the importance of formal statistical inference and, in particular, the many benefits of Bayesian modeling for spatial and spatial-temporal data. I will elucidate recent developments in Bayesian statistical science that harness high performance scientific computing methods for spatial-temporal BIG DATA analysis and emphasize how such methods can be implemented on very modest computing architectures (such as a laptop). I will conclude with some recent and ongoing research with my students.

Sudipto Banerjee is a Professor in the Department of Biostatistics at the Fielding School of Public Health and in the Department of Statistics at the College of Physical Sciences with an affiliate appointment in the UCLA Institute of the Environment and Sustainability. He is the Senior Associate Dean of the Fielding School of Public Health, University of California, Los Angeles. Professor Banerjee’s research expertise includes Bayesian hierarchical modeling and inference 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.

Professor Banerjee's research and scholarship has been recognized with honors such as the Abdel El-Shaarawi Award from The International Environmetric Society (TIES), the Mortimer Spiegelman Award from the American Public Health Association and the George W. Snedecor Award from the Committee of Presidents of Statistical Societies (COPSS), elected membership of the International Statistical Institute, elected fellowships in the Institute of Mathematical Statistics (IMS), the American Statistical Association (ASA), the International Society for Bayesian Analysis (ISBA) and the American Association for the Advancement of Science (AAAS), a Distinguished Achievement Medal from the ASA's Section on Statistics and the Environment, the ASA's Outstanding Statistical Application Award and the Jeromme Sacks Award from the National Institute of Statistical Science (NISS). He was elected to serve as President of the International Society for Bayesian Analysis (ISBA) in 2022 (President-Elect in 2021; Past-President in 2023).