Award Abstract # 1840265
RTG: Data-Intensive Research and Computing at the University of California, Merced

NSF Org: DMS
Division Of Mathematical Sciences
Recipient: UNIVERSITY OF CALIFORNIA, MERCED
Initial Amendment Date: May 28, 2019
Latest Amendment Date: May 19, 2023
Award Number: 1840265
Award Instrument: Continuing Grant
Program Manager: Stacey Levine
slevine@nsf.gov
 (703)292-2948
DMS
 Division Of Mathematical Sciences
MPS
 Direct For Mathematical & Physical Scien
Start Date: June 1, 2019
End Date: May 31, 2025 (Estimated)
Total Intended Award Amount: $2,092,605.00
Total Awarded Amount to Date: $2,092,605.00
Funds Obligated to Date: FY 2019 = $1,422,971.00
FY 2020 = $167,408.00

FY 2021 = $167,408.00

FY 2022 = $167,408.00

FY 2023 = $167,410.00
History of Investigator:
  • Arnold Kim (Principal Investigator)
    adkim@ucmerced.edu
  • Roummel Marcia (Co-Principal Investigator)
  • Francois Blanchette (Co-Principal Investigator)
Recipient Sponsored Research Office: University of California - Merced
5200 N LAKE RD
MERCED
CA  US  95343-5001
(209)201-2039
Sponsor Congressional District: 13
Primary Place of Performance: University of California - Merced
CA  US  95343-5001
Primary Place of Performance
Congressional District:
13
Unique Entity Identifier (UEI): FFM7VPAG8P92
Parent UEI:
NSF Program(s): APPLIED MATHEMATICS,
COMPUTATIONAL MATHEMATICS,
WORKFORCE IN THE MATHEMAT SCI
Primary Program Source: 01002021DB NSF RESEARCH & RELATED ACTIVIT
01001920DB NSF RESEARCH & RELATED ACTIVIT

01002223DB NSF RESEARCH & RELATED ACTIVIT

01002122DB NSF RESEARCH & RELATED ACTIVIT

01002324DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 7301, 9263
Program Element Code(s): 1266, 1271, 7335
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.049

ABSTRACT

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.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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(Showing: 1 - 10 of 23)
González-Rodríguez, Pedro and Kim, Arnold D. and Tsogka, Chrysoula "Quantitative signal subspace imaging" Inverse Problems , v.37 , 2021 https://doi.org/10.1088/1361-6420/ac349b Citation Details
Carvalho, Camille and Kim, Arnold and Lewis, Lori and Moitier, Zoïs "Quadrature by Parity Asymptotic eXpansions (QPAX) for Scattering by High Aspect Ratio Particles" Multiscale Modeling & Simulation , v.19 , 2021 https://doi.org/10.1137/21M1416801 Citation Details
Heydari, A. Ali and Sindi, Suzanne S. and Theillard, Maxime "Conservative finite volume method on deforming geometries: The case of protein aggregation in dividing yeast cells" Journal of Computational Physics , v.448 , 2022 https://doi.org/10.1016/j.jcp.2021.110755 Citation Details
Derakhshani, Afshin and Asadzadeh, Zahra and Safarpour, Hossein and Leone, Patrizia and Shadbad, Mahdi Abdoli and Heydari, Ali and Baradaran, Behzad and Racanelli, Vito "Regulation of CTLA-4 and PD-L1 Expression in Relapsing-Remitting Multiple Sclerosis Patients after Treatment with Fingolimod, IFN?-1?, Glatiramer Acetate, and Dimethyl Fumarate Drugs" Journal of Personalized Medicine , v.11 , 2021 https://doi.org/10.3390/jpm11080721 Citation Details
Ho, Alex and Alvarez, Jacqueline and Marcia, Roummel F. "Convolution Padding in Recurrent Neural Networks for Image Denoising with Limited Data" 2021 55th Asilomar Conference on Signals, Systems, and Computers , 2021 https://doi.org/10.1109/IEEECONF53345.2021.9723313 Citation Details
Kim, Arnold D. and Tsogka, Chrysoula "Tunable High?Resolution Synthetic Aperture Radar Imaging" Radio Science , v.57 , 2022 https://doi.org/10.1029/2022RS007572 Citation Details
Polimeno, Matteo and Kim, Changho and Blanchette, François "Toward a Realistic Model of Diffusion-Limited Aggregation: Rotation, Size-Dependent Diffusivities, and Settling" ACS Omega , v.7 , 2022 https://doi.org/10.1021/acsomega.2c03547 Citation Details
Kim, Arnold D. and Tsogka, Chrysoula "High-Resolution, Quantitative Signal Subspace Imaging for Synthetic Aperture Radar" SIAM Journal on Imaging Sciences , v.15 , 2022 https://doi.org/10.1137/21M1467109 Citation Details
Blanchette, François "Shallow-water equations and box model simulations of turbidity currents from a moving source" Physical Review Fluids , v.7 , 2022 https://doi.org/10.1103/PhysRevFluids.7.084301 Citation Details
Ilan, Boaz and Kim, Arnold D. and Venugopalan, Vasan "Radiance backscattered by a strongly scattering medium in the high spatial frequency limit" Journal of the Optical Society of America A , v.39 , 2022 https://doi.org/10.1364/JOSAA.462683 Citation Details
Lee, Jane HyoJin and Kim, Changho and Colvin, Michael E. "Molecular Dynamics Studies of the Melting Kinetics of Superheated Crystals" The Journal of Physical Chemistry C , v.126 , 2022 https://doi.org/10.1021/acs.jpcc.1c10392 Citation Details
(Showing: 1 - 10 of 23)

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