Bayesian Modeling and Scalable Inference for Big Data Streams

我们正处于一场数据革命之中。在数据测量、收集和生成技术最新进展的推动下，数据的激增正在重塑众多科学和应用学科的格局。基于互联网的公司必须利用大量可用的用户数据，为个人量身定制推荐和广告，从而争夺消费者的注意力；金融交易公司必须每天从新闻、公司报告和市场中合成数tb的数据，以做出明智的投资；微生物学家现在面临着分析成千上万个单个细胞的整个转录组；这样的例子不胜枚举。数据的爆炸式增长也导致了对“数据分析师”的重新定义：不再仅仅是那些受过深入统计和数学训练的人，越来越多的分析师来自其他技术学科，因为他们的主要挑战转向了处理各自领域的大规模、流数据。******这在计算机科学和统计学的交叉点提出了挑战：我们需要从数据中学习的算法和模型，这些算法和模型在计算上易于处理，并且能够跟上大规模、流数据的持续洪流；但是要得到实践者的信任，它们还必须易于实现和使用，并且对学习结果的质量有严格的理论保证。我研究的基本目标是通过为现代大规模和流数据开发有效、实用和易于使用的概率机器学习方法来解决这些挑战。******我的研究计划涉及一个多方面的方法来应对大数据的挑战，并在三个主要领域做出贡献：******1)易于使用，理论上合理的大规模数据推理算法***2)灵活的模型和流数据推理算法***3)对学习模型和近似质量的理论分析******本研究计划的发展将通过开源代码发布给更广泛的社区。在总体目标的指导下，使统计建模能够为日益多样化的从业者所使用，并适用于日益多样化的大规模、流数据分析问题

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.**