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<!DOCTYPE html>
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<h1 class="title">Research Projects</h1>
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<h3>Projects</h3>
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<h5 class="m-bottom-30"> <a href="https://github.com/TauferLab/Mimir.git" target="_blank">Mimir: MapReduce over MPI, Perfomance Optimizations, and Power Usage for Data Management on Supercomputers</a></h5>
<div class="dl-horizontal text-left tight m-bottom-30">
<dt>Funding bodies:</dt>
<dd><a href="https://www.sandia.gov/" target="_blank">Sandia National Laboratories </a><a href="https://www.anl.gov/" target="_blank">and Argonne National Laboratories</a></dd>
<dt>Role: </dt>
<dd>Principle investigator</dd>
<dt>Value:</dt>
<dd>$63,107.00</dd>
<dt>Duration:</dt>
<dd>Feb 1, 2019 – Jan 31, 2020</dd>
<dt>Description: </dt>
<dd>The project tackles scheduler specializations by systematically studying fully hierarchical scheduling models with Flux and defining models supporting a given workflow to employ the best scheduler specialization strategy at runtime.</dd>-->
<!--<dd><a href="http://analytics4md.org/"> Analytics4MD </a></dd>-->
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<div class="col-sm-9">
<h5 class="m-bottom-30"> <a
href="https://www.nsf.gov/awardsearch/showAward?AWD_ID=2223704&HistoricalAwards=false"
target="_blank">
SHF: Small: Methods, Workflows, and Data Commons for Reducing Training Costs in Neural
Architecture Search on High-Performance Computing Platforms </a></h5>
<div class="dl-horizontal text-left tight m-bottom-30">
<dt>Source of Support:</dt>
<dd><a href="https://www.nsf.gov/" target="_blank">National Science Foundation (NSF) <a
href="https://www.nsf.gov" target="_blank"></a></dd>
<dt>Project Period: </dt>
<dd>Oct 01, 2022 - Sep 30, 2025 </dd>
<dt>Award Number:</dt>
<dd>2223704</dd>
<dt>Location of Project:</dt>
<dd>University of Tennessee, Knoxville</dd>
<dt>Description: </dt>
<dd>This project addresses the urgent need to reduce the use of high-performance-computing resources
for the training of neural networks, while assuring explainable, reproducible and nearly-optimal
neural networks. </dd>
<dt>Web Page:</dt>
<dd><a href="https://analytics4neuralnetworks.ai/"> A4NN</a></dd>
</div>
</div>
</div>
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<div class="col-sm-9">
<h5 class="m-bottom-30"> <a
href="https://www.nsf.gov/awardsearch/showAward?AWD_ID=2138811&HistoricalAwards=false"
target="_blank">
OAC: Piloting the National Science Data Fabric: A Platform Agnostic Testbed for Democratizing Data
Delivery </a></h5>
<div class="dl-horizontal text-left tight m-bottom-30">
<dt>Source of Support:</dt>
<dd><a href="https://www.nsf.gov/" target="_blank">National Science Foundation (NSF) <a
href="https://www.nsf.gov" target="_blank"></a></dd>
<dt>Project Period: </dt>
<dd>Oct 01, 2021 - Sep 30, 2024 </dd>
<dt>Award Number:</dt>
<dd>2138811</dd>
<dt>Location of Project:</dt>
<dd>University of Tennessee, Knoxville</dd>
<dt>Description: </dt>
<dd> This project aims to build a National Science Data Fabric (NSDF), a testbed experimenting with
critical technology needed to democratize data-driven sciences by constructing a CI platform
designed for equitable access. NSDF connects storage, compute, and networking components with a
software stack that empowers end-users with scalable tools that are easy to use, integrate and
scale. Community-driven education and outreach will guarantee equitable access to all resources
and engage an open network of universities, including minority-serving institutions in a federated
data fabric configurable for individual and shared scientific use.</dd>
<dt>Web Page:</dt>
<dd><a href="http://nsdf.sci.utah.edu/"> NSDF</a></dd>
</div>
</div>
</div>
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<div class="col-sm-3"> <img src="somospie\images\logos\conus.png"></div>
<div class="col-sm-9">
<h5 class="m-bottom-30"> <a
href="https://nsf.gov/awardsearch/showAward?AWD_ID=2103845&HistoricalAwards=false"
target="_blank">
SENSORY: Software Ecosystem for Knowledge Discovery - a Data-Driven Framework for Soil Moisture
Applications </a></h5>
<h5 class="m-bottom-30"> <a
href="https://nsf.gov/awardsearch/showAward?AWD_ID=2334945&HistoricalAwards=false"
target="_blank">
EAGER: A Comprehensive Approach for Generating, Sharing, Searching, and Using High-
Resolution Terrain Parameters </a></h5>
<div class="dl-horizontal text-left tight m-bottom-30">
<dt>Source of Support:</dt>
<dd><a href="https://www.nsf.gov/" target="_blank">National Science Foundation (NSF) <a
href="https://www.nsf.gov" target="_blank"></a></a></dd>
<dt>Project Period: </dt>
<dd> Jun 01, 2021 - May 31, 2024 </dd>
<dd> Nov 01, 2023 - Sep 30, 2025</dd>
<dt>Award Number:</dt>
<dd>2103845, 2334945 </dd>
<dt>Location of Project:</dt>
<dd>University of Tennessee, Knoxville</dd>
<dt>Description: </dt>
<dd> This project connects multi-disciplinary advances across the scientific community (such as
generating datasets at scale and supporting cloud-based cyber-infrastructures) to develop a
data-driven software ecosystem for analyzing, visualizing, and extracting knowledge from the
growing data collections (from fine-grained, in situ soil sensor information to coarse-grained,
global satellite measurements) and releasing this knowledge to applications in environmental
sciences.</dd>
<dt>Web Page:</dt>
<dd><a href="https://globalcomputing.group/somospie/"> SOMOSPIE</a></dd>
</div>
</div>
</div>
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<div class="row">
<div class="col-sm-3"> <img src="somospie\images\logos\anacin-logo-d4-border.png"></div>
<div class="col-sm-9">
<h5 class="m-bottom-30"> <a
href="https://www.nsf.gov/awardsearch/showAward?AWD_ID=1900888&HistoricalAwards=false"
target="_blank">ANACIN-X: Analysis and Modeling of Non-determinism and Associated Costs in eXtreme
Scale Applications </a></h5>
<div class="dl-horizontal text-left tight m-bottom-30">
<dt>Source of Support:</dt>
<dd><a href="https://www.nsf.gov/" target="_blank">National Science Foundation (NSF): <a
href="https://www.nsf.gov/div/index.jsp?div=CCF" target="_blank"> CCF</a></a></dd>
<dt>Project Period: </dt>
<dd>Aug 1, 2019 - Jul 31, 2025</dd>
<dt>Award Number:</dt>
<dd>1900888</dd>
<dt>Location of Project:</dt>
<dd>University of Tennessee, Knoxville</dd>
<dt>Description: </dt>
<dd>This project advances the reproducibility study of HPC applications by proposing an open-source
modular framework for automatic measurement, analysis, and visualization of non-determinism and
root causes of non-determinism in MPI applications.</dd>
<dt>Web Page:</dt>
<dd><a href="https://globalcomputing.group/anacin-x/"> Anacin-x </a></dd>
</div>
</div>
</div>
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<div class="row">
<div class="col-sm-3"> <img src="assets/img/research/a4md.png"></div>
<div class="col-sm-9">
<h5 class="m-bottom-30"> <a href="https://www.nsf.gov/awardsearch/showAward?AWD_ID=1841758"
target="_blank">Analytics for Molecular Dynamics (A4MD)</a></h5>
<div class="dl-horizontal text-left tight m-bottom-30">
<dt>Source of Support: </dt>
<dd><a href="https://www.nsf.gov/" target="_blank">National Science Foundation (NSF): <a
href="https://www.nsf.gov/div/index.jsp?div=IIS" target="_blank"> IIS</a> and <a
href="https://www.nsf.gov/div/index.jsp?div=OAC" target="_blank"> Advanced Cyberinfrastructure
(OAC)</a></a></dd>
<dt>Project Period:</dt>
<dd>Jun 1, 2018 – Sep 30, 2023</dd>
<dt>Award Number:</dt>
<dd>1841758</dd>
<dt>Location of Project:</dt>
<dd>University of Tennessee, Knoxville</dd>
<dt>Description: </dt>
<dd>This interdisciplinary project tackles the data challenge of data analysis of molecular dynamics
simulations on the next-generation supercomputers. Specifically, this effort combines machine
learning and data analytics approaches, workflow management methods, and high performancecomputing
techniques to analyze molecular dynamics data as it is generated.</dd>
<dt>Web Page:</dt>
<dd><a href="https://analytics4md.org/"> Analytics4MD </a></dd>
</div>
</div>
</div>
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<div class="row">
<div class="col-sm-3"> <img src="assets/img/research/22.png"></div>
<div class="col-sm-9">
<h5 class="m-bottom-30">
Leveraging Kokkos Abstractions to Automate Checkpointing </h5>
<div class="dl-horizontal text-left tight m-bottom-30">
<dt>Source of Support:</dt>
<dd><a href="https://www.anl.gov/" target="_blank">Argonne National Laboratory <a
href="https://www.anl.gov/" target="_blank"> ANL</a></a></dd>
<dt>Project Period: </dt>
<dd> May 01, 2021 - Apr 30, 2022 </dd>
<dt>Location of Project:</dt>
<dd>University of Tennessee, Knoxville</dd>
<dt>Description: </dt>
<dd> This project focuses on what patterns the memory abstractions used by Kokkos form and how they
can be efficiently captured and persistent with VELOC.</dd>
</div>
</div>
</div>
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<div class="row">
<div class="col-sm-3"> <img src="assets/img/research/03.jpg"></div>
<div class="col-sm-9">
<h5 class="m-bottom-30"> <a href="https://www.nsf.gov/awardsearch/showAward?AWD_ID=1724843"
target="_blank">CIF21 DIBBs: PD: Cyberinfrastructure Tools for Precision Agriculture in the 21st
Century</a></h5>
<div class="dl-horizontal text-left tight m-bottom-30">
<dt>Source of Support:</dt>
<dd><a href="https://www.nsf.gov/" target="_blank">National Science Foundation (NSF): <a
href="https://www.nsf.gov/div/index.jsp?div=OAC" target="_blank"> Advanced Cyberinfrastructure
(OAC)</a></a></dd>
<dt>Project Period:</dt>
<dd>Jul 1, 2017 – Oct, 2018</dd>
<dt>Award Number:</dt>
<dd>1724843</dd>
<dt>Location of Project:</dt>
<dd>University of Tennessee, Knoxville</dd>
<dt>Description: </dt>
<dd>This interdisciplinary project applies computer science approaches and computational resources
to large multidimensional environmental datasets, and synthesizes this information into
ecoinformatics, a branch of informatics that analyzes ecological and environmental science
variables suchas information on landscapes, soils, climate, organisms, and ecosystems.</dd>
<dt>Web Page:</dt>
<dd><a href="https://globalcomputing.group/somospie/">SOMOSPIE</a></dd>
</div>
</div>
</div>
<div class="sp-line-40"></div>
<div class="row">
<div class="col-sm-3"><img src="assets/img/research/21.jpg"></div>
<div class="col-sm-9">
<h5 class="m-bottom-30"> <a
href="https://www.nsf.gov/awardsearch/showAward?AWD_ID=2028923&HistoricalAwards=false"
target="_blank">Collaborative Research: PPoSS: Planning: Performance Scalability, Trust, and
Reproducibility: A Community Roadmap to Robust Science in High-throughput Applications</a></h5>
<div class="dl-horizontal text-left tight m-bottom-30">
<dt>Source of Support: </dt>
<dd><a href="https://www.nsf.gov/" target="_blank">National Science Foundation (NSF)</a></a></dd>
<dt>Project Period:</dt>
<dd>Oct 1, 2020 – Sep 30, 2022</dd>
<dt>Award Number:</dt>
<dd>2028923</dd>
<dt>Location of Project:</dt>
<dd>University of Tennessee, Knoxville</dd>
<dt>Description: </dt>
<dd>The project recruits a cross-disciplinary community working together in three virtual
mini-workshops called virtual world cafes to define, design, implement, and use a set of solutions
for robust science.</dd>
<dt>Web Page:</dt>
<dd><a href="https://robustscience.org/#/"> RobustScience </a></dd>
<!--<dd><a href="http://analytics4md.org/"> Analytics4MD </a></dd>-->
</div>
</div>
</div>
<div class="sp-line-40"></div>
<div class="row">
<div class="col-sm-3"> <img src="assets/img/research/19.png"></div>
<div class="col-sm-9">
<h5 class="m-bottom-30"> Augmenting Hatchet to support scalability and replicability solutions for HPC
applications</h5>
<div class="dl-horizontal text-left tight m-bottom-30">
<dt>Source of Support: </dt>
<dd><a href="https://www.llnl.gov/" target="_blank">Lawrence Livermore National Laboratory</a></a>
</dd>
<dt>Project Period:</dt>
<dd>Aug 1, 2020 – Jul 31, 2022</dd>
<dt>Location of Project:</dt>
<dd>University of Tennessee, Knoxville</dd>
<dt>Description: </dt>
<dd>This project uses Hatchet and its features (e.g. query language) to study scalability and
replicability problems in applications of interest to LLNL at a large scale; and develops tooling
to support the analysis and study of such problems to identify the source of the scalability and
replicability problems. </dd>
<!--<dd><a href="http://analytics4md.org/"> Analytics4MD </a></dd>-->
</div>
</div>
</div>
<div class="sp-line-40"></div>
<div class="row">
<div class="col-sm-3"><a
href="https://www.fosteropenscience.eu/foster-taxonomy/open-reproducible-research"> <img
src="assets/img/research/18.jpg"></a></div>
<div class="col-sm-9">
<h5 class="m-bottom-30"> <a
href="https://www.nsf.gov/awardsearch/showAward?AWD_ID=2041977&HistoricalAwards=false"
target="_blank">Collaborative Research: EAGER: Advancing Reproducibility in Multi-Messenger
Astrophysics</a></h5>
<div class="dl-horizontal text-left tight m-bottom-30">
<dt>Source of Support: </dt>
<dd><a href="https://www.nsf.gov/" target="_blank">National Science Foundation (NSF)</a></a></dd>
<dt>Project Period:</dt>
<dd>Aug 1, 2020 – Aug 31, 2022</dd>
<dt>Award Number:</dt>
<dd>2041977</dd>
<dt>Location of Project:</dt>
<dd>University of Tennessee, Knoxville</dd>
<dt>Description: </dt>
<dd> The project provides the astrophysics community with a transformative building block to a
roadmap for reproducible open science. Findings about the reproducibility process of the EHT and
NICER results are captured and disseminated through documentation, data products, and methods
used. </dd>
<!--<dd><a href="http://analytics4md.org/"> Analytics4MD </a></dd>-->
</div>
</div>
</div>
<div class="sp-line-40"></div>
<div class="row">
<div class="col-sm-3"><img src="assets/img/research/20.jpg"></div>
<div class="col-sm-9">
<h5 class="m-bottom-30"> Leverage Containerized Environments for Reproducibility and Traceability of
Scientific Workflows - the case study of Analytics for Neural Network Workflows</h5>
<div class="dl-horizontal text-left tight m-bottom-30">
<dt>Source of Support: </dt>
<dd><a href="https://www.sandia.gov/" target="_blank">Sandia National Laboratories</a></a></dd>
<dt>Project Period:</dt>
<dd>Jul 15, 2020 – Jul 14, 2022</dd>
<dt>Location of Project:</dt>
<dd>University of Tennessee, Knoxville</dd>
<dt>Description: </dt>
<dd> This project builds a prototype of a containerized environment which encapsulates each
component of a scientific workflow (i.e., data and applications) in individual container
environment for transparent and automatic metadata collection and access, easy-to-read record
trail, and tight connections between data and metadata.</dd>
<!--<dd><a href="http://analytics4md.org/"> Analytics4MD </a></dd>-->
</div>
</div>
</div>
<div class="sp-line-40"></div>
<div class="row">
<div class="col-sm-3"> <img src="assets/img/research/17.jpg"></div>
<div class="col-sm-9">
<h5 class="m-bottom-30"> Study Performance Portability of the Vector Particle-In-Cell Project (VPIC)
across architectures</h5>
<div class="dl-horizontal text-left tight m-bottom-30">
<dt>Source of Support: </dt>
<dd><a href="https://www.lanl.gov/" target="_blank">Los Alamos National Laboratory</a></a></dd>
<dt>Project Period:</dt>
<dd>May 1, 2020 – Apr 30, 2023</dd>
<dt>Location of Project:</dt>
<dd>University of Tennessee, Knoxville</dd>
<dt>Description: </dt>
<dd> This project studies aspect of performance portability associate to the Vector Particle-In-Cell
Project or VPIC code across platforms by addressing questions such as “Is the execution of VPIC
sensitive to new architectures on which it runs? How do we continue to extract as much performance
as possible despite differences in hardware? What performance is lost when using a performance
portability framework?”</dd>
<!--<dd><a href="http://analytics4md.org/"> Analytics4MD </a></dd>-->
</div>
</div>
</div>
<div class="sp-line-40"></div>
<div class="row">
<div class="col-sm-3"><img src="assets/img/research/09.jpg"></div>
<div class="col-sm-9">
<h5 class="m-bottom-30"><a
href="https://computing.llnl.gov/projects/flux-building-framework-resource-management"
target="_blank">Flux Scheduler Specializations: Improving Workflow Performance with Scheduler
Structure and Policy Tuning</a></h5>
<div class="dl-horizontal text-left tight m-bottom-30">
<dt>Source of Support: </dt>
<dd><a href="https://www.llnl.gov/" target="_blank">Lawrence Livermore National Laboratory</a></a>
</dd>
<dt>Project Period:</dt>
<dd>Apr 1, 2020 – Mar 31, 2022</dd>
<dt>Location of Project:</dt>
<dd>University of Tennessee, Knoxville</dd>
<dt>Description: </dt>
<dd> This project studies how features of a user-level, highly-configurable scheduler like Flux can
best be leveraged to maximize workflow performance. The project aims to answer this question
through the development of a model that tunes scheduler settings to maximize workflow performance
even under conditions of system stress such as scheduler fragmentation and resource drains.</dd>
<!--<dd><a href="http://analytics4md.org/"> Analytics4MD </a></dd>-->
</div>
</div>
</div>
<div class="sp-line-40"></div>
<div class="row">
<div class="col-sm-3"><a
href="https://www.fosteropenscience.eu/foster-taxonomy/open-reproducible-research"> <img
src="assets/img/research/12.jpg"></a></div>
<div class="col-sm-9">
<h5 class="m-bottom-30"> <a
href="https://www.nsf.gov/awardsearch/showAward?AWD_ID=1941443&HistoricalAwards=false"
target="_blank">EAGER: Reproducibility in Computational and Data-Enabled Science-Paradigms,
Practices, and Infrastructure</a></h5>
<div class="dl-horizontal text-left tight m-bottom-30">
<dt>Source of Support: </dt>
<dd><a href="https://www.nsf.gov/" target="_blank">National Science Foundation (NSF)</a></a></dd>
<dt>Project Period:</dt>
<dd>Aug 16, 2019 – Aug 15, 2022</dd>
<dt>Award Number:</dt>
<dd>1941443</dd>
<dt>Location of Project:</dt>
<dd>University of Tennessee, Knoxville</dd>
<dt>Description: </dt>
<dd> This project seeks to improve understanding of how the scientific community can adapt to the
increasing use of computing and large-scale data resources. One challenge is ensuring that
computational results -such as those from simulations- are "reproducible", that is, the same
results areobtained when one re-uses the same input data, methods, software and analysis
conditions. In 2019, the National Academies of Science, Engineering, and Medicine (NASEM) issued a
report on "Reproducibility and Replication in Science" with a series of recommendations. The
project will assess the implications of these recommendations on the scientific discovery process
for computationally and data-enabled research.</dd>
<!--<dd><a href="http://analytics4md.org/"> Analytics4MD </a></dd>-->
</div>
</div>
</div>
<div class="sp-line-40"></div>
<div class="row">
<div class="col-sm-3"> <img src="assets/img/research/11.jpg"></div>
<div class="col-sm-9">
<h5 class="m-bottom-30"> JDRD: Empowering Training and Validation Stages in Al-Orchestrated Workflows
</h5>
<div class="dl-horizontal text-left tight m-bottom-30">
<dt>Source of Support: </dt>
<dd><a href="https://scialli.utk.edu/" target="_blank">Science Alliance - University of Tennessee,
Knoxville</a></dd>
<dt>Project Period:</dt>
<dd>Oct 1, 2019 – Sep 30, 2021</dd>
<dt>Location of Project:</dt>
<dd>University of Tennessee, Knoxville</dd>
<dt>Description: </dt>
<dd>This project studies AI-orchestrated workflows--workflows including experimental, computational,
and data manipulation steps in one or multiple domains, where an important component is one or
more neural networks (NN) used for searching or decision making. The project aim is to transform
the process of training the NN in AI-orchestrated workflows from simulated data (clean,
non-adversarial data) to deploying on real data (noisy, adversarial data) with the integration of
mitigating methods.</dd>
<!--<dd><a href="http://analytics4md.org/"> Analytics4MD </a></dd>-->
</div>
</div>
</div>
<!--Past/Unfunded projects-->
<div class="sp-line-40"></div>
<div class="row">
<div class="col-sm-3"> <img src="assets/img/research/13.jpg"></div>
<div class="col-sm-9">
<h5 class="m-bottom-30"> Study of Data-intensive Workflows on Next-generation Systems with Emphasis on
Memory Access</h5>
<div class="dl-horizontal text-left tight m-bottom-30">
<dt>Source of Support: </dt>
<dd><a href="https://www.sandia.gov/" target="_blank">Sandia National Laboratories</a></a></dd>
<dt>Project Period:</dt>
<dd>Aug 1, 2019 – Jul 31, 2020</dd>
<dt>Location of Project:</dt>
<dd>University of Tennessee, Knoxville</dd>
<dt>Description: </dt>
<dd>The project designs and implements a C++ suite of data-intensive mini-applications to study data
management costs with emphasis on memory access times and use, power consumption, and
replicability.</dd>
<!--<dd><a href="http://analytics4md.org/"> Analytics4MD </a></dd>-->
</div>
</div>
</div>
<div class="sp-line-40"></div>
<div class="row">
<div class="col-sm-3"> <img src="assets/img/research/09.jpg"></div>
<div class="col-sm-9">
<h5 class="m-bottom-30"> <a
href="https://computing.llnl.gov/projects/flux-building-framework-resource-management"
target="_blank">Moving towards self-adjusting scheduling policies for high performance workflows
with Flux’s fully hierarchical scheduling</a></h5>
<div class="dl-horizontal text-left tight m-bottom-30">
<dt>Source of Support: </dt>
<dd><a href="https://www.llnl.gov/" target="_blank">Lawrence Livermore National Laboratory</a></dd>
<dt>Project Period:</dt>
<dd>Feb 8, 2019 – Jan 31, 2020</dd>
<dt>Location of Project:</dt>
<dd>University of Tennessee, Knoxville</dd>
<dt>Description: </dt>
<dd>The project tackles scheduler specializations by systematically studying fully hierarchical
scheduling models with Flux and defining models supporting a given workflow to employ the best
scheduler specialization strategy at runtime.</dd>
<!--<dd><a href="http://analytics4md.org/"> Analytics4MD </a></dd>-->
</div>
</div>
</div>
<div class="sp-line-40"></div>
<div class="row">
<div class="col-sm-3"> <img src="assets/img/research/15.jpg"></div>
<div class="col-sm-9">
<h5 class="m-bottom-30"> Driving Next-Generation Schedulers with Machine Learning-Based Application
Patterns</h5>
<div class="dl-horizontal text-left tight m-bottom-30">
<dt>Source of Support: </dt>
<dd><a href="https://www.llnl.gov/" target="_blank">Lawrence Livermore National Laboratory</a></dd>
<dt>Project Period:</dt>
<dd>Aug 1 2018, 2018 – Jul 31, 2020</dd>
<dt>Location of Project:</dt>
<dd>University of Tennessee, Knoxville</dd>
<dt>Description: </dt>
<dd>This project develops methods to identify and understand irregular HPC job patterns and
integrates knowledge of these irregular HPC patterns into multi-objective schedulers. The work
leverages results of a previous award from Lawrence Livermore National Laboratory.</dd>
<!--<dd><a href="http://analytics4md.org/"> Analytics4MD </a></dd>-->
</div>
</div>
</div>
<div class="sp-line-40"></div>
<div class="row">
<div class="col-sm-3"> <img src="assets/img/research/02.jpg"></div>
<div class="col-sm-9">
<h5 class="m-bottom-30"> <a href="https://www.nsf.gov/awardsearch/showAward?AWD_ID=1841399"
target="_blank">Collaborative: EAGER: Exploring and Advancing the State of the Art in Robust
Science in Gravitational Wave Physics</a></h5>
<div class="dl-horizontal text-left tight m-bottom-30">
<dt>Source of Support: </dt>
<dd><a href="https://www.nsf.gov/" target="_blank">National Science Foundation (NSF): <a
href="https://www.nsf.gov/div/index.jsp?div=OAC" target="_blank"> Advanced Cyberinfrastructure
(OAC) #1823372</a></a></dd>
<dt>Project Period:</dt>
<dd>May 31, 2018 – Apr 30, 2020</dd>
<dt>Award Number:</dt>
<dd>1841399</dd>
<dt>Location of Project:</dt>
<dd>University of Tennessee, Knoxville</dd>
<dt>Description: </dt>
<dd>The project develops and uses a survey to collect information about LIGO workflows that are
composed of a series of experimental, computational, and data manipulation steps.</dd>
</div>
</div>
</div>
<div class="sp-line-40"></div>
<div class="row">
<div class="col-sm-3"><img src="assets/img/research/14.jpg"></div>
<div class="col-sm-9">
<h5 class="m-bottom-30"> <a
href="https://tickle.utk.edu/ut-to-get-cutting-edge-supercomputer-under-landmark-award-with-ibm/"
target="_blank">Building a “Miniature” Version of the ORNL‘s Summit supercomputer for
Computational Science Research at UTK</a></h5>
<div class="dl-horizontal text-left tight m-bottom-30">
<dt>Source of Support: </dt>
<dd>2019 IBM Global University Program Shared University Research Award</dd>
<dt>Project Period:</dt>
<dd>Jun 21, 2019 - June 20, 2024</dd>
<dt>Location of Project:</dt>
<dd>University of Tennessee, Knoxville</dd>
<dt>Description: </dt>
<dd>The award enabled the purchase of a supercomputer for computational science applicationsat UTK.
</dd>
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<h3>Past Projects (Selected)</h3>
<div class="sp-blank-40"></div>
<div class="col-sm-4" align="center"><img src="assets/img/research/06.jpg" class="set-me" height="50"
width="50">
<div class="contents" align="center">
<!--<div class="item-meta"><span class="date">12 Oct 2015</span></div>-->
<div class="title-wrapper">
<h2>
<font face="Courier New" size="1"><a href="https://exscitech.org/" target="_blank">ExSciTecH: a
set of chemistry study tools designed to help students learn about and understand chemical
structures</a></font>
</h2>
</div>
</div></a>
</div>
<div class="col-sm-4" align="center"><img src="assets/img/research/07.jpg" class="set-me" height="50"
width="50">
<div class="contents" align="center">
<!--<div class="item-meta"><span class="date">22 Jan 2012</span></div>-->
<div class="title-wrapper">
<h2>
<font face="Courier New" size="1"><a href="http://qcnexplorer.org/" target="_blank">QCN
Explorer: the simulator of the Quake Catcher Network, a volunteer computing project out of
Stanford University </a></font>
</h2>
</div>
</div></a>
</div>
<div class="col-sm-4" align="center"> <img src="assets/img/research/08.png" class="set-me" height="50"
width="50">
<div class="contents" align="center">
<!--<div class="item-meta"><span class="date">22 Jan 2012</span></div>-->
<div class="title-wrapper">
<h2>
<font face="Courier New" size="1"><a href="http://docking.cis.udel.edu//" target="_blank">
Docking@Home</a></font>
</h2>
</div>
</div></a>
</div>
</div>
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