Bayesian Modeling and Scalable Inference for Big Data Streams

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We are in the midst of a data revolution. Driven by recent advances in data measurement, collection, and generation technologies, the proliferation of data is reshaping the landscape of numerous scientific and applied disciplines. Internet-based companies must vie for consumer attention by using the vast quantities of available user data to tailor recommendations and advertisements to individuals; financial trading firms must synthesize terabytes of data daily from the news, company reports, and markets to make informed investments; microbiologists are now faced with analyzing the entire transcriptome of tens of thousands of individual cells; the list goes on. This explosion in data has also caused the redefinition of "data analyst:" no longer just those with in-depth statistical and mathematical training, analysts are arising more and more from other technological disciplines as their main challenges shift towards dealing with the large-scale, streaming data in their respective fields. This presents challenges lying at the intersection of computer science and statistics: we need algorithms and models for learning from data that are computationally tractable and can keep pace with the constant deluge of large-scale, streaming data; but to be trusted by practitioners, they must also be easy to implement and use, and come with rigorous theoretical guarantees on the quality of the learned result. The fundamental goal of my research is to address these challenges by developing effective, practical, and easy-to-use probabilistic machine learning methods for modern large-scale and streaming data. My research proposal involves a multifaceted approach to the challenges of big data, with contributions in three main areas: 1) Easy-to-use, theoretically sound algorithms for inference with large-scale data 2) Flexible models and inference algorithms for streaming data 3) Theoretical analysis of the quality of learned models and approximations The developments in this research program will be made available to the broader community through open-source code releases, guided by the overarching goal of making statistical modeling accessible to the growing diversity of practitioners and applicable to the growing diversity of large-scale, streaming data analysis problems.