Foundations of Model Driven Discovery from Massive Data

该项目在布朗大学创建了一个研究所，汇集了数学、统计学和理论计算机科学的学科，以定义和完善新兴数据科学领域的基础景观。该研究所赞助由跨越学科界限的研究人员小组组织的重点活动。它与广泛的本科生和研究生水平的学生联系并告知他们，他们在广泛的领域领域工作，从神经科学到基因组学，到气候建模，到公共政策。理论的发展可以改善诊断成像和肿瘤分类，可以开发改进的神经结构模型，甚至可以为罗德岛州食品券和累犯的发现提供信息。该研究所的使命是促进大数据理论和方法的发展和原则性应用，以发现、完善和验证管理系统或数据生成过程的基础理论模型，从而改进对新结果的预测。“基于因果关系和模型的推理”、“大规模网络上的数据分析”和“分析和可视化复杂数据的几何和拓扑方法”等科学项目，推动了模型及其不断完善在数据分析中的作用。与寻找更好的“黑匣子”进行分析相比，该研究所将强调“调查人员在循环中”的作用，对整个数据管道进行询问，寻求理论改进和影响。它连接着布朗数据科学倡议和数学计算与实验研究所（ICERM）。该项目的资金来自CISE计算与通信基金会、MPS数学科学部、成长融合研究和EPSCoR。(融合的特征是来自多个领域的知识、技术和专业知识的深度整合，形成新的和扩展的框架，以应对科学和社会的挑战和机遇。该项目通过汇集代表许多学科的社区，包括数学、统计学和理论计算机科学，以及将数据科学应用于实际研究问题的社区，促进了融合。

This project creates an institute at Brown University that brings together the disciplines of mathematics, statistics, and theoretical computer science to define and refine the foundational landscape of the emerging area of data science. The institute sponsors focused activities organized by small groups of researchers that cut across disciplinary boundaries. It connects with and informs a broad range of students at the undergraduate and graduate levels, working in a wide area of domain areas, from neuroscience, to genomics, to climate modeling, to public policy. Theoretical developments can improve diagnostic imaging and tumor classification, can develop improved models for neural structure, and can even inform findings regarding food stamps and recidivism in Rhode Island. The mission of the institute is to foster development and principled application of theory and methods of big data to discover, refine, and validate underlying theoretical models that govern a system or data-generating process, which in turn improve predictions of new outcomes. Scientific projects in "causal and model-based inference," "data analysis on massive networks," and "geometric and topological methods to analyze and visualize complex data" drive home the role of the model, and its continuous refinement, in data analysis. Rather than seeking better "black-boxes" for analysis, the institute will emphasize the role of the "investigator-in-the-loop" interrogating the entirety of the data pipeline, seeking theoretical improvements and implications. It connects to the Brown Data Science Initiative and the Institute for Computational and Experimental Research in Mathematics (ICERM). Funds for the project come from CISE Computing and Communications Foundations, MPS Division of Mathematical Sciences, Growing Convergent Research, and EPSCoR. (Convergence can be characterized as the deep integration of knowledge, techniques, and expertise from multiple fields to form new and expanded frameworks for addressing scientific and societal challenges and opportunities. This project promotes Convergence by bringing together communities representing many disciplines including mathematics, statistics, and theoretical computer science as well as engaging communities that apply data science to practical research problems.)