Ideas Labs: Data-Intensive Research in Science and Engineering

点击翻译按钮获取中文摘要

In 2016, the National Science Foundation (NSF) unveiled a set of "Big Ideas," 10 bold, long-term research and process ideas that identify areas for future investment at the frontiers of science and engineering. The Big Ideas represent unique opportunities to position our Nation at the cutting edge of global science and engineering leadership by bringing together diverse disciplinary perspectives to support convergence research. NSF's Harnessing the Data Revolution (HDR) Big Idea is a national-scale activity to enable new modes of data-driven discovery that will allow fundamental questions to be asked and answered at the frontiers of science and engineering. This project describes a series of Ideas Labs on "Data-Intensive Research in Science and Engineering (DIRSE)". Ideas Labs are intensive workshops focused on finding innovative and bold transdisciplinary solutions to grand challenge problems. The overarching goal of the DIRSE Ideas Labs is to foster convergent approaches to enable data-intensive research in science and engineering through a series of facilitated activities bringing together scientists and engineers working on important data-intensive science and engineering problems with data scientists.There are numerous science and engineering challenges that require, or will soon require, data science to help address research and technological questions. Advancing knowledge in these areas requires solutions to many modeling and data challenges such as real-time sensing, learning, and decision making; social, political, and behavioral implications of machine learning and impacts of new data uses; issues related to ethics and fairness; and integrating heterogeneous data for explaining or predicting complex phenomena. There is also a need for approaches that combine physical models with data driven models for learning and decision making. Data science tools, such as signal and image processing, visualization, statistical modeling and inference, machine learning, and optimization, offer a starting point for solving important scientific and engineering challenges. However, extracting new information and knowledge from data will benefit from new, convergent strategies that capitalize on existing NSF investments in data and cyberinfrastructure and that build synergy between the researchers with expertise in the generation or measurement of data and those with expertise in processing and analyzing that data.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.