Advancing Probabilistic Programming for Machine Learning and Statistics

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There has never been more demand for methods to make sense of data. The explosion in the variety, complexity, and scale of data sets in the past few years far eclipses the availability of experts with the requisite domain, statistical, machine learning, and computer science know-how to develop methods for data analysis. How can we enable users to reliably build sophisticated probabilistic reasoning systems that can scale to meet the demands of real-world applications? My research in the area of machine learning and probabilistic programming aims to provide a solution: Just as high-level programming languages and compilers empowered experts to solve complex computational problems much more quickly, and made it possible for even nonexperts to solve them, a number of high-level probabilistic programming languages (PPLs) and inference engines have been developed that have the potential to similarly transform the practice of machine learning and statistics. Probabilistic programming systems for machine learning and statistics have progressed rapidly in the past five years, but our understanding of the roadblocks ahead is still limited. One of the key challenges is to characterize when such systems can be efficient, and when they can represent, and perform efficient calculations in, complex stochastic process models that arise in state-of-the-art nonparametric Bayesian statistics. Towards that end, this proposal seeks funding to carry out a systematic study of relationships between computation and representing important structure like conditional independence, which plays a fundamental role in the efficiency of many probabilistic inference algorithms. The hope is that this basic research will significantly extend our understanding of probabilistic programming, and point the way towards the next generation of algorithms.