DataS Research

Amount: $42,864

Sponsor: Department of Defense/Air Force Office of Scientific Research

Awarded: September 2023

Amount: $100,431

Sponsor: Defense Advanced Research Projects Agency (DARPA)

Awarded: November 2023

Co-PIs: Dukka KC, Thomas Oommen, Shiliang Wu

Amount: $2,310,862

Sponsor: National Science Foundation

Awarded: July 2023

Co-PIs: Aref Majdara

Amount: $288,343

Sponsor: Department of Health and Human Services, Centers for Disease Control and Prevention, NIOSH

Awarded: August 2021

Amount: $7,815

Sponsor: Institute of Management Accountants

Awarded: May 2023

Co-PIs: Bo Chen

Amount: $16,700

Sponsor: Michigan Tech Research Excellence Fund

Awarded: May 2023

Amount: $103,312

Sponsor: UCLA/Department of Energy

Awarded: June 2023

Amount: $704,409

Sponsor: CyManII

Awarded: March 2024

Co-PIs: Yu Cai, Chee-Wooi Ten, Nicholas Hendrickson

Amount: $673,389

Sponsor: MxD/Department of Defense

Awarded: September 2023

Amount: $281,982

Sponsor: GIT, DOE

Awarded: March 2024

Amount: $8,167

Sponsor: Orbion Space Technology

Awarded: April 2023

Co-PIs: Vinh Nguyen, Scott Kuhl, David Labyak, Paniz Hazaveh

Amount: $999,930

Sponsor: National Science Foundation

Awarded: September 2023

Read a blog about this award

PI: Jeremy P. Bos, DataS, ECE
Sponsor: Department of Defense
Award: $459,894 | 3 Years
Awarded: April 2017
Abstract: This project will explore the nature of imaging in conditions characterized by extreme anisoplanatism.  Under these conditions each point in an image may be affected by a locally unique blurring kernel implying a violation of the linear shift invariance. Bos and his students will use a combination analysis and extensive experimental data to develop new models and new understanding of this phenomenon. Bos has also proposed using angular diversity as a means of mitigating the effects of extreme anisoplanatism on imaging and beam control problems.

Read an article about this research.

PI: Timothy C. Havens, DataS, Computing
Sponsor: Department of Defense
Award: $96,643 | 1 Years
Awarded: February 2019

PI: Benjamin Ong, DataS, Math
Sponsor: National Science Foundation
Award: $36,636 | 1 Years
Awarded: October 2019
Abstract: Computational simulations are a key part of scientific research for government, industry, and academia, complementing laboratory experimentation and theory.  However changes in computer architectures are leading to future supercomputers that will have billions of processors, as opposed to millions today. Further, each individual processor will be no faster than individual processors today.  Thus, these next generation machines will no longer automatically provide a speedup to existing computational simulations, and new mathematical algorithms must be developed and deployed that can utilize this unprecedented number of processors. One such class of mathematical algorithms, parallel-in-time methods, is the subject of this workshop.  In particular, parallel-in-time methods add a new dimension (time) of parallelism and thus allow existing computer models to be extended to next generation supercomputers. The range of potential applications for parallel-in-time to dramatically speed-up is vast, e.g., computational molecular dynamics (e.g., protein and DNA folding), computational biology (e.g., heart modeling), computational fluid dynamics (e.g., combustion, climate, and weather), and machine learning.

The primary focus of the proposed parallel-in-time workshop is to educate and inspire researchers and students in new and innovative numerical techniques for the parallel-in-time solution of large-scale evolution problems on modern supercomputing architectures, and to stimulate further studies in their analysis and applications. This workshop aligns with the National Strategic Computing Initiative (NSCI) objective: “increase coherence between technology for modeling/simulation and data analytics”.  The conference will feature ten lectures by Professor Gander, an expert in parallel time integration. Using appropriate mathematical methodologies from the theory of partial differential equations in a functional analytic setting, numerical discretizations, integration techniques, and convergence analyses of these iterative methods, conference participants will be exposed to the numerical analysis of parallel-in-time methodologies and their implementations. The proposed topics include multiple shooting type methods, waveform relaxation methods, time-multigrid methods, and direct time-parallel methods. These lectures will be accessible to a wide audience from a broad range of disciplines, including mathematics, computer science and engineering.

Learn more about the CBMS Conference.

PI: Hairong Wei
Sponsor: NSF (Kansas State University)
Amount Funded:  $79,765
Date Awarded: August 2021

PI: Sidike M. Paheding
Co-PI: Samantha L. Smith
Sponsor: NSF
Amount Funded:  $69,711
Date Awarded: August 2021

PI: Snehamoy Chatterjee
Sponsor: DHHS
Amount Funded:  $288,343
Date Awarded: August 2021

PI: Snehamoy Chatterjee
Sponsor: Newmont Mining Corp
Amount Funded:  $25,000
Date Awarded: June 2021

PI: Xiaoyong (Brian) Yuan
Co-PI: Lan (Emily) Zhang
Sponsor: NSF
Amount Funded:  $309,170
Date Awarded: July 2021

PI: Xiaoyong (Brian) Yuan
Sponsor: Oak Ridge Associated Universities
Amount Funded:  $5,000
Date Awarded: June 2022