ABSTRACT: In this talk I will discuss, from my own perspective, some technical aspects of the field of Bayesian non-parametric statistics and its interface with concepts arising in CSP. Part of my talk will be tutorial in nature aimed at those at a graduate or higher level and will also sprinkle in a few things I have been thinking about if time permits. BIO--- Prof. Lancelot F. James is currently a Professor in the Business School at the Hong Kong University of Science and Technology. He has been at HKUST since 2001. Prior to that, he was an Assistant Professor in the School of Engineering at the Johns Hopkins University in Baltimore. He is well known for his work in Bayesian nonparametric statistics. Since the early 2000s, he has advocated and developed ideas related to the usage of Chinese restaurant processes, stick-breaking priors, and Pitman-Yor processes. Since that time, these colourfully named processes (the latter two named by James and his co-author Hemant Ishwaran in 2001) have played a major role in the development of intricate applications in Bayesian Statistical Machine Learning and Bayesian nonparametric methods in general, all of which are pertinent to the analysis of Big Data. Prof. James is an elected member of the International Statistical Institute. Since 2008 he is an elected Fellow of the Institute of Mathematical Statistics where he is cited for contributions to Bayesian nonparametric statistics, the development of Poisson partition calculus for Levy processes, and for dedicated service to IMS. Professor James was born on the island of Jamaica in the Caribbean Sea and grew up in Westchester County, New York.