RTG: Networks: Foundations in Probability, Optimization, and Data Sciences

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This research training group (RTG) project will develop a comprehensive training and mentoring program for undergraduate and graduate students and postdoctoral associates, centered around the theme of theory and applications of networks. Faculty team members bring a broad range of expertise to this effort, including stochastic analysis, random discrete structures, discrete and continuous optimization, time series and mathematical statistics, and machine learning. The training of undergraduates, graduate students, and postdocs will contribute to the readying of the workforce in academia and industry in this high-demand field. The engagement of undergraduates in research will form pathways for these students to pursue graduate studies and careers in research. Educational materials and mentoring mechanisms developed as part of RTG activities will have impact on the overall curriculum and training practices in the department as well as on the pan-campus data science initiative. The research intersects with many other fields, such as engineering, social sciences, business, biological and medical sciences, epidemiology, and ecology, and is expected to have impact in these disciplines. Research from the RTG activity will be widely disseminated through posters, meetings, workshops, colloquia, conference proceedings and journal articles. Two key initiatives enabled through this effort are: (1) a set of ten three-week minicourses, taught by international leaders in the field, designed for graduate students and other trainees, which will be broadly disseminated to the community in network science; (2) a weekly ideas seminar that will serve as a central platform to bring undergraduate, graduate, and postdoc trainees together with faculty mentors, and which will serve as a launching pad for undergraduate research projects as well as for identifying topics for Ph.D. dissertations and postdoctoral research. This platform will also provide valuable undergraduate research mentoring opportunities for graduate students and postdocs, and its activities will be instrumental in developing presentation and technical writing abilities of trainees at all levels. Other planned initiatives include (a) a summer boot-camp for incoming graduate trainees; (b) a first year graduate course on research directions in networks that brings together elements of a seminar and an independent reading course; (c) a freshman seminar and a capstone course in networks to form a well-structured pathway for undergraduates, from the freshman year to the senior year, to engage in meaningful and sustained research activity; and (d) a data science lab for organizing undergraduate research activities. The research themes of this RTG will span a broad range of topics, including: development of foundational large network asymptotics using tools from stochastic analysis, percolation theory, and large deviations theory; algorithmic approaches to detection and reconstruction, resource allocation, and computational questions on networks, using tools from applied probability and optimization theory; and approaches to estimation and learning questions using tools from statistics and machine learning.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.