Bayesian computation is facing many challenges due to the increasing complexity of the models and problems that need to be addressed and the size of the data sets that are being collected. Subthemes of the workshop are MCMC methods, for which theoretical underpinnings are strong but which can struggle with massive data sets; and variational Bayes methods, which can handle many kinds of massive problems, but are not guaranteed to converge to correct values. This workshop will bring together top researchers who work on both methodological developments in Bayesian computation and applications of Bayesian methods to major scientific problems. A major purpose of the workshop is to discuss the many recent significant developments in Bayesian computation and to identify important problems and new research directions. All sessions are plenary, and the confirmed speakers include Yves Atchade, Anirban Bhattacharya, Yuansi Chen, Justin Domke, Galin Jones, Lizhen Lin, Sifan Liu, Art Owen, Rajesh Ranganath, Liyue Shen, Brian Trippe, and Guanyang Wang. The workshop will also include a contributed poster session. Funding is expected to support a limited number of young researchers to attend the workshop and present their work at the poster session. The organizing committee is soliciting applications from senior graduate students (4th year or higher) and researchers who received their Ph.D. in or after 2020. Applications should include a curriculum vitae, an one-page abstract, and the names of two references, including a major professor who is supervising (or has supervised) the applicant’s research. Applications should be emailed by November 12, 2024. Women and under-represented groups are encouraged to apply. Funding applications and workshop registration link are found on the Workshop web page. Emails should be sent to winterworkshop@stat.ufl.edu