Foundations of Model Driven Discovery from Massive Data

该项目在布朗大学创建了一个研究所，该研究所汇集了数学、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图景。该研究所赞助由跨越学科界限的研究人员小组组织的有重点的活动。它与本科生和研究生水平的广泛学生建立联系，并为他们提供信息，他们在广泛的领域工作，从神经科学到基因组学，到气候建模，再到公共政策。理论的发展可以改进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关于食品券和复发的研究结果提供信息。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本理论模型，从而提高对新结果的预测。科学项目中的“因果和基于模型的推理”、“海量网络上的数据分析”以及“分析和可视化复杂数据的几何和拓扑方法”充分说明了模型在数据分析中的作用及其不断完善。该研究所不会寻求更好的“黑匣子”进行分析，而是强调“环路调查员”的作用，询问整个数据管道，寻求理论上的改进和影响。它与布朗数据科学倡议和数学计算与实验研究所(ICERM)相连。该项目的资金来自CEISE计算和通信基金会、MPS数学科学部、增长融合研究和EPSCoR。(融合可以被描述为深度整合来自多个领域的知识、技术和专业知识，以形成应对科学和社会挑战和机遇的新的和扩大的框架。该项目通过将代表包括数学、统计学和理论计算机科学在内的许多学科的社区聚集在一起，并让将数据科学应用于实际研究问题的社区参与进来，来促进融合。)

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.)