Bayesian Modeling and Scalable Inference for Big Data Streams

大数据流的贝叶斯建模和可扩展推理

• 批准号: RGPIN-2019-03962
• 负责人: Campbell, Trevor
• 金额: $ 2.84万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=765706
• 关键词: Bayesian; Modeling; Scalable; Inference; Big

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.

我们正处于一场数据革命之中。在数据测量、收集和生成技术的最新进展的推动下，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通过使用大量可用的用户数据来为个人量身定制推荐和广告来争夺消费者的注意力;金融交易公司必须每天从新闻、公司报告和市场中综合数TB的数据来进行明智的投资;微生物学家现在面临着分析数万个个体细胞的整个转录组;数据的爆炸性增长也导致了对“数据分析师“的重新定义：分析师不再仅仅是那些接受过深入统计和数学培训的人，他们越来越多地来自其他技术学科，因为他们的主要挑战转向处理各自领域的大规模流数据。这给计算机科学和统计学的交叉点带来了挑战：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是可处理的，并且可以跟上大规模流数据的不断涌入;但是要得到从业者的信任，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应对这些挑战。我的研究计划涉及到大数据挑战的多方面方法，在三个主要领域的贡献：1）易于使用，理论上合理的大规模数据推理算法2）流数据的灵活模型和推理算法3）学习模型和近似的质量的理论分析在这个研究计划的发展将提供给更广泛的社区，通过开放，源代码发布，以使统计建模可供日益多样化的从业者使用并适用于日益多样化的大规模流数据分析问题的总体目标为指导。