HDR Tripods: Texas A&M Research Institute for Foundations of Interdisciplinary Data Science (FIDS)

数据科学作为一个重要的跨学科领域正在迅速发展，其进步往往来自多个学科的思想结合。新的数据类型已经出现，呈现出巨大的复杂性和挑战，需要一种新的跨学科思维方式。德克萨斯农工大学跨学科数据科学基础研究所（FIDS）将汇集来自统计学、电气工程、数学、计算机科学和工业工程五个学科领域的研究人员，对生物信息学、能源领域、电力系统和运输系统中出现的问题进行数据科学基础研究。跨学科数据科学基础研究所将有能力发展严谨的理论、新颖的方法和高效的计算技术来解决许多应用领域的数据挑战。现代大型数据集极其复杂，为看似简单的问题寻找答案往往变成一个棘手的问题。为了应对这些挑战，FIDS将通过对复杂数据建模和开发相关理论和算法的研究来推进数据科学的基础。开发有效的方法来识别这些高维复杂数据中的低维结构将是恢复具有相关不确定性的高维信号的关键策略。将开发新的数据分析模型和算法，用于表示学习、信息提取和从复杂数据中发现知识，以实现更好的决策。为了补充研究工作，FIDS将在工程，数学和统计学的界面领域教育和培训学生和博士后研究员。将制定有针对性的外展计划，以增加追求数据科学职业的女性和代表性不足的少数族裔的人数。将设计一个外部参与计划，以促进与领域科学家和外部数据科学家的合作。这些项目将有助于为准备在这个令人兴奋的新领域取得突破性进展的新一代科学家奠定智力基础。该项目是美国国家科学基金会“利用数据革命（HDR）大创意”活动的一部分。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Data Science is rapidly evolving as an essential interdisciplinary field, where advances often result from a combination of ideas from several disciplines. New types of data have emerged and present tremendous complexities and challenges that require a novel way of interdisciplinary thinking. The Texas A&M Research Institute for Foundations of Interdisciplinary Data Science (FIDS) will bring together researchers from five disciplinary areas, Statistics, Electrical Engineering, Mathematics, Computer Science and Industrial Engineering, to conduct research on the foundations of data science motivated by problems arising in bioinformatics, the energy arena, power systems, and transportation systems. The Institute for Foundations of Interdisciplinary Data Science will be well-positioned to develop rigorous theories, novel methodologies, and efficient computational techniques to solve data challenges in many application domains.Modern large datasets are extremely complex and finding answers to seemingly simple questions often turns into an intractable problem. To address these challenges, FIDS will advance the foundations of data science through research on modeling complex data and developing related theory and algorithms. Development of efficient methods to identify low-dimensional structures in these high-dimensional complex data will be the key strategy to recovering high-dimensional signals with related uncertainties. Novel data-analysis models and algorithms will be developed for representation learning, information extraction, and knowledge discovery from complex data to enable better decision making. To complement the research effort, FIDS will educate and train students and postdoctoral fellows in areas at the interface of engineering, mathematics, and statistics. Targeted outreach programs will be developed to increase the pool of women and underrepresented minorities who pursue data-science careers. An external engagement program will be designed to facilitate collaborations with domain scientists and external data scientists. These programs will help to develop the intellectual foundation for a new generation of scientists poised to make novel breakthroughs in this exciting new field.This project is part of the National Science Foundation's Harnessing the Data Revolution (HDR) Big Idea activity.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.