RTG: Data-Intensive Research and Computing at the University of California, Merced

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The overarching objective of this program is to address the national need to train the next-generation workforce to be highly skilled in the field of computational and data-enabled sciences. To achieve this objective, we propose to establish the Data-Intensive Research And Computing (DIRAC) Research Training Group (RTG). The DIRAC RTG leverages strengths of the UC Merced Applied Mathematics faculty to provide undergraduate and graduate students, and postdoctoral researchers a training experience that prepares them for careers in academia, industry, and government. A key challenge is that computational and data-enabled sciences involve inextricable ties between mathematics, science, technology, and engineering. UC Merced Applied Mathematics is well positioned to address this challenge because of its three main approaches to science that will be at the core of this RTG: (1) modeling of physical and biological systems, (2) scientific computing, and (3) data analysis. To provide its trainees a collaborative training experience in computational and data-enabled sciences, the DIRAC RTG will foster Small Mentoring and Research Training (SMaRT) teams, which are vertically integrated, community-based mentoring structures, each centered on one of four research themes: (I) energy and the environment, (II) sensing and imaging, (III) mathematical biology, and (IV) numerical analysis. These SMaRT teams will provide support to individuals, guide their training, and produce a well-trained, nimble workforce that can contribute to the fast-paced modern computational research. Additionally, the DIRAC RTG is committed to serving the underrepresented and first-generation students that UC Merced Applied Mathematics actively recruits into its undergraduate and graduate programs. Built into each SMaRT Team are active measures for recruiting inclusive teams of trainees, providing continuous mentorship and support to retain these trainees, and developing the professional skills of trainees needed to succeed upon completion of this training program.Computational and data sciences are new paradigms for scientific inquiry and discovery that incorporate mathematics, statistics, computer science, and domain-specific knowledge. Since computational and data-enabled sciences are relatively new, their natural and effective integration into existing training programs in mathematics remains to be perfected. This RTG project brings together the entire Applied Mathematics faculty of UC Merced with the common goal of developing a modernized and comprehensive training program for undergraduate and graduate students, and postdoctoral associates that integrates these subjects in a natural and effective way and prepares the trainees for successful careers in academia, government, and industry in a broad range of fields. The proposed RTG project has three major components: (1) a balanced curriculum tightly integrated with research which is modernized to reflect the current needs in computational and data-enabled sciences; (2) a vertically integrated mentoring program that engages undergraduate, graduate, postdoctoral associates, and faculty participants; and (3) the development of extensive, dynamic, and supportive communities focused on education, research, and professional development. The thematic research areas considered focus on timely and important issues and are divided into (I) energy and the environment, (II) sensing and imaging, (III) mathematical biology, and (IV) numerical analysis. This training program focuses on enhancing each trainee's skills and experience in the process of research (as opposed to just the products of research) and provides practical teaching training, communication skills, and professional development. The activities in this RTG are crucial to making systematic improvements to the existing training program at UC Merced, which can then serve as a model for other programs. These institutional changes will profoundly transform mathematics programs and have long-lasting impact on training the future generations of computational and data-enabled scientists.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.