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<!DOCTYPE html>
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<h1 class="title">Research Projects</h1>
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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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<h5 class="m-bottom-30"> <a
href="https://www.nsf.gov/awardsearch/show-award?AWD_ID=2531754"
target="_blank">
FAIROS: Disciplinary Improvements: Dark Matter Data Commons -
A FAIR and Open Science Infrastructure for Astrophysical Discovery
</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>October 1, 2025 - September 30, 2028 </dd>
<dt>Award Number:</dt>
<dd><a href="https://www.nsf.gov/awardsearch/show-award?AWD_ID=2531754" target="_blank">2531754</a></dd>
<dt>Location of Project:</dt>
<dd>University of Tennessee, Knoxville</dd>
<dt>Description: </dt>
<dd> <b>NEXUS-DM</b> is a next-generation <b>Dark Matter Data Commons</b> designed to make experimental data more <b>transparent</b>, <b>reproducible</b>, and <b>accessible</b> to researchers everywhere. Built on FAIR principles, it provides an open platform for storing, curating, and analyzing dark matter experiment data. The commons offers easy-to-use command-line and Python tools, along with <b>AI and machine learning </b> workflows that help reduce bias, remove noise, and improve calibration for reliable, explainable results. Through open tutorials, Jupyter notebooks, and an <b>ACCESS Affinity Group</b>, NEXUS-DM builds a vibrant community that shares knowledge, fosters collaboration, and drives <b>open science and discovery</b> in one of physics’ most intriguing frontiers. </dd>
<dt>Web Page:</dt>
<dd><a href="https://nexus-dm.org/" target="_blank"> NEXUS-DM</a></dd>
</div>
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<h5 class="m-bottom-30"> <a
href="https://www.nsf.gov/awardsearch/show-award?AWD_ID=2530461"
target="_blank">
SAFARI: Scientific Analytics, Forensics, and
Reproducibility for Workflows in Cyberinfrastructure (CI)
</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>October 1, 2025 - September 30, 2028 </dd>
<dt>Award Number:</dt>
<dd><a href="https://www.nsf.gov/awardsearch/show-award?AWD_ID=2530461" target="_blank">2530461</a></dd>
<dt>Location of Project:</dt>
<dd>University of Tennessee, Knoxville</dd>
<dt>Description: </dt>
<dd> <b>SAFARI</b> brings forensic insight to scientific computing by embedding data analytics directly into workflow systems. This integration ensures that the data and software scientists use are reliable, reusable, and reproducible. By combining provenance tracking, automated verification, and modular artifact management within the Pegasus Workflow Management System, SAFARI makes complex, AI-driven analyses transparent and trustworthy. Through applications such as soil moisture modeling, irrigation forecasting, and wildfire prevention, SAFARI advances secure, scalable cyberinfrastructure that supports national priorities in AI and data-driven science. </dd>
<dt>Web Page:</dt>
<dd><a href="https://globalcomputing.group/safari/" target="_blank">SAFARI</a></dd>
</div>
</div>
</div>
<div class="sp-line-40"></div>
<div class="row">
<div class="col-sm-3"> <img src="assets/img/research/osearth_logo.png"></div>
<div class="col-sm-9">
<h5 class="m-bottom-30"> <a
href="https://www.nsf.gov/awardsearch/show-award?AWD_ID=2449103"
target="_blank">
POSE: Phase I: Towards an Open-Source Ecosystem for Accelerating High-Resolution Terrain Parameter Computation in Earth Science 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" target="_blank"></a></dd>
<dt>Project Period: </dt>
<dd>June 15, 2025 - May 31, 2026 </dd>
<dt>Award Number:</dt>
<dd><a href="https://www.nsf.gov/awardsearch/show-award?AWD_ID=2449103" target="_blank">2449103</a></dd>
<dt>Location of Project:</dt>
<dd>University of Tennessee, Knoxville</dd>
<dt>Description: </dt>
<dd> <b>OS-EARTH</b> builds a sustainable, open-source software ecosystem for analyzing the shape and structure of Earth’s terrain. Centered on the <b>GEOtiled</b> platform, it enables fast, accurate, and scalable computation of terrain parameters—critical data for understanding <b>wildfire behavior</b>, <b>soil moisture</b>, and <b>agricultural planning</b>. By making high-performance geospatial tools openly available, OS-EARTH lowers technical barriers and empowers researchers, educators, and decision-makers to turn elevation data into actionable insights. The project cultivates an inclusive community of developers and scientists through <b>transparent governance</b>, <b>training workshops</b>, and <b>open documentation</b>, fostering collaboration and long-term sustainability. Through this open-science ecosystem, OS-EARTH strengthens U.S. leadership in <b>geospatial analytics</b>, <b>environmental modeling</b>, and <b>data-driven decision-making</b>. </dd>
<dt>Web Page:</dt>
<dd><a href="https://os-earth.org/" target="_blank">OS-EARTH</a></dd>
</div>
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<div class="sp-line-40"></div>
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<div class="col-sm-3"> <img src="assets/img/research/pegasusai_logo.png"></div>
<div class="col-sm-9">
<h5 class="m-bottom-30"> <a
href="https://www.nsf.gov/awardsearch/show-award?AWD_ID=2513101"
target="_blank">
CSSI: Frameworks: Applying Artificial Intelligence Advances to the Next Generation of Workflow Management on Modern Cyberinfrastructure
</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>June 15, 2025 - May 30, 2030 </dd>
<dt>Award Number:</dt>
<dd><a href="https://www.nsf.gov/awardsearch/show-award?AWD_ID=2513101" target="_blank">2513101</a></dd>
<dt>Location of Project:</dt>
<dd>University of Tennessee, Knoxville</dd>
<dt>Description: </dt>
<dd> <b>PegasusAI</b> is a next-generation, open-source <b>AI-driven workflow management framework</b> that empowers researchers and engineers to harness the full computing continuum—from edge devices to clouds and supercomputers. By embedding artificial intelligence throughout the workflow lifecycle—covering composition, smart scheduling, and real-time adaptation—PegasusAI enables flexible, scalable, and efficient scientific discovery. Built to be <b>extensible</b>, <b>community-driven</b>, and deployable on national cyberinfrastructure platforms, this framework accelerates innovation across disciplines while lowering the barrier to using advanced computational resources.</dd>
<dt>Web Page:</dt>
<dd><a href="https://pegasus-ai.org/" target="_blank">Pegasus AI</a></dd>
</div>
</div>
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<div class="col-sm-3"> <img src="assets/img/research/compilers_logo.png"></div>
<div class="col-sm-9">
<h5 class="m-bottom-30">
Methodology for Explaining Performance Variations Across Compilers and Compiler Options in 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
href="https://www.llnl.gov/" target="_blank"></a></dd>
<dt>Project Period: </dt>
<dd>January 2, 2025 - December 31, 2025 </dd>
<dt>Location of Project:</dt>
<dd>University of Tennessee, Knoxville</dd>
<dt>Description: </dt>
<dd> Performance-portable programming models like <b>RAJA</b> allow scientists to write applications that run efficiently on a wide range of computer architectures. Yet, achieving consistent performance often depends on compiler decisions that are difficult to observe and explain. This project introduces an <b>explainable performance analysis framework</b> that links compiler behavior to runtime results, helping developers make informed optimization choices. Using <b>Caliper</b> for detailed data collection and <b>Thicket</b> for multi-dimensional analysis, the study applies an iterative methodology across <b>RAJAPerf</b> kernels, open-source benchmarks, and <b>LLNL’s MARBL</b> simulation to advance understanding and efficiency in high-performance computing.</dd>
</div>
</div>
</div>
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<div class="row">
<div class="col-sm-3"> <img src="assets/img/research/vpic_logo.png"></div>
<div class="col-sm-9">
<h5 class="m-bottom-30">
Study Performance Portability of the Vector Particle-In-Cell Project (VPIC) across Architectures (Stage 3)
</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
href="https://www.lanl.gov/" target="_blank"></a></dd>
<dt>Project Period: </dt>
<dd>October 17, 2024 - September 1, 2026 </dd>
<dt>Location of Project:</dt>
<dd>University of Tennessee, Knoxville</dd>
<dt>Description: </dt>
<dd> <b>VPIC (Vector Particle-In-Cell)</b> is a high-performance plasma simulation code optimized for the world’s largest supercomputers. Using the <b>Kokkos performance-portability framework</b>, this project enhances VPIC’s efficiency across diverse architectures while preserving portability. Through vectorization, algorithmic refinement, and mixed-precision strategies, it advances scalable plasma simulations for next-generation computing systems.</dd>
</div>
</div>
</div>
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<div class="row">
<div class="col-sm-3"> <img src="assets/img/research/exascale_logo.png"></div>
<div class="col-sm-9">
<h5 class="m-bottom-30">
LLNL-LDRD-Software Stack Development for Next Generation Exascale Platforms
</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
href="https://www.llnl.gov/" target="_blank"></a></dd>
<dt>Project Period: </dt>
<dd>March 26, 2024 - September 30, 2026 </dd>
<dt>Location of Project:</dt>
<dd>University of Tennessee, Knoxville</dd>
<dt>Description: </dt>
<dd> Developing a robust, high-performance <b>software stack</b> is key to powering the next generation of <b>exascale supercomputers</b>. The <b>LLNL-LDRD Software Stack Development for Next-Generation Exascale Platforms</b> project builds portable libraries, runtimes, and tools that enable scientific applications to scale efficiently across diverse architectures—from CPUs and GPUs to emerging accelerators. Emphasizing <b>performance portability, modularity, and sustainability</b>, the work advances national computational capabilities and empowers researchers to achieve faster, more reliable scientific discovery on future exascale systems.</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">
Improvement of Checkpointing Performance for Reproducibility Studies
</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 ANL <a
href="https://www.anl.gov/" target="_blank"></a></dd>
<dt>Project Period: </dt>
<dd>February 26, 2024 - December 31, 2025 </dd>
<dt>Location of Project:</dt>
<dd>University of Tennessee, Knoxville</dd>
<dt>Description: </dt>
<dd> Enhancing the <b>reproducibility of high-performance computing (HPC) applications</b> is essential as computational resources become increasingly diverse and heterogeneous. This projectleverages <b>intermediate checkpoints and hash-based validation</b> with user-defined error bounds to detect divergences early in execution. By organizing checkpoint data through <b>Merkle trees</b>, it efficiently captures significant differences while reducing I/O overhead, enabling reliable and transparent large-scale scientific computing.</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://www.nsf.gov/awardsearch/show-award?AWD_ID=2103845"
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://www.nsf.gov/awardsearch/show-award?AWD_ID=2334945"
target="_blank">
EAGER: A Comprehensive Approach for Generating, Sharing, Searching, and Using High-
Resolution Terrain Parameters </a></h5>
<h5 class="m-bottom-30"> <a
href="https://www.nsf.gov/awardsearch/show-award?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" target="_blank"></a></a></dd>
<dt>Project Period: </dt>
<dd> Jun 01, 2021 - May 30, 2026 </dd>
<dd> Oct 01, 2023 - Sep 30, 2026</dd>
<dd> Jul 01, 2017 - Oct, 2018</dd>
<dt>Award Number:</dt>
<dd>
<a href="https://www.nsf.gov/awardsearch/show-award?AWD_ID=2103845" target="_blank">2103845</a>,
<a href="https://www.nsf.gov/awardsearch/show-award?AWD_ID=2334945" target="_blank">2334945</a>,
<a href="https://www.nsf.gov/awardsearch/show-award?AWD_ID=1724843" target="_blank">1724843 </a>
</dd>
<dt>Location of Project:</dt>
<dd>University of Tennessee, Knoxville</dd>
<dt>Description: </dt>
<dd> <b>SENSORY</b> brings together advances in large-scale data generation and cloud cyber-infrastructure to build a unified, data-driven software ecosystem for the environmental sciences. From fine-grained soil sensor networks to global satellite measurements, the platform enables seamless <b>analysis</b>, <b>visualization</b>, and <b>knowledge extraction</b> from diverse data collections. By bridging multi-disciplinary communities and delivering actionable insights to real-world applications, SENSORY empowers researchers, practitioners, and decision-makers to tackle environmental challenges with scalable, transparent tools.</dd>
<dt>Web Page:</dt>
<dd><a href="https://globalcomputing.group/sensory/" target="_blank"> SENSORY</a></dd>
</div>
</div>
</div>
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<div class="col-sm-3"> <img src="assets/img/research/robustscience_logo.png"></div>
<div class="col-sm-9">
<h5 class="m-bottom-30"> <a
href="https://www.nsf.gov/awardsearch/show-award?AWD_ID=2331152"
target="_blank">
Collaborative Research: SHF: Small: Model-driven Design and Optimization of Dataflows for Scientific 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" target="_blank"></a></dd>
<dt>Project Period: </dt>
<dd>October 1, 2023 - September 30, 2026 </dd>
<dt>Award Number:</dt>
<dd><a href="https://www.nsf.gov/awardsearch/show-award?AWD_ID=2331152" target="_blank">2331152</a></dd>
<dt>Location of Project:</dt>
<dd>University of Tennessee, Knoxville</dd>
<dt>Description: </dt>
<dd> A domain-spanning taxonomy maps common <b>data-flow motifs</b>—from simple producer–consumer patterns to complex multi-producer/multi-consumer pipelines—onto real scientific applications, enabling a deeper understanding of data movement across disciplines. <b>This project</b> builds a <b>middleware layer</b> that orchestrates these pipelines across <b>HPC</b>, <b>cloud</b>, and <b>edge</b> platforms using a two-step approach to <b>reduce data loss</b> and <b>minimize inefficiencies</b> in data production and consumption.</dd>
<dt>Web Page:</dt>
<dd><a href="https://robustscience.org/#/" target="_blank">RobustScience</a></dd>
</div>
</div>
</div>
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<div class="col-sm-3"> <img src="assets/img/research/a4nn.png"></div>
<div class="col-sm-9">
<h5 class="m-bottom-30"> <a
href="https://www.nsf.gov/awardsearch/show-award?AWD_ID=2223704"
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, 2026 </dd>
<dt>Award Number:</dt>
<dd> <a href="https://www.nsf.gov/awardsearch/show-award?AWD_ID=2223704" target="_blank">2223704</a></dd>
<dt>Location of Project:</dt>
<dd>University of Tennessee, Knoxville</dd>
<dt>Description: </dt>
<dd> <b>Analytics for Neural Networks (A4NN)</b> reduces the computational cost of training neural networks while ensuring their <b>explainability</b>, <b>reproducibility</b>, and <b>near-optimal performance</b>. The project introduces a flexible fitness-prediction method that uses parametric modeling to forecast a network’s future performance, allowing unpromising training runs to be stopped early. By combining analytics and machine learning, A4NN accelerates model discovery, optimizes high-performance computing resources, and promotes sustainable, data-driven AI research.</dd>
<dt>Web Page:</dt>
<dd><a href="https://analytics4neuralnetworks.ai/" target="_blank"> A4NN</a></dd>
</div>
</div>
</div>
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<div class="col-sm-3"> <img src="assets/img/research/23.png"></div>
<div class="col-sm-9">
<h5 class="m-bottom-30"> <a
href="https://www.nsf.gov/awardsearch/show-award?AWD_ID=2138811"
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 - Mar 31, 2026 </dd>
<dt>Award Number:</dt>
<dd><a href="https://www.nsf.gov/awardsearch/show-award?AWD_ID=2138811" target="_blank">2138811</a></dd>
<dt>Location of Project:</dt>
<dd>University of Tennessee, Knoxville</dd>
<dt>Description: </dt>
<dd> <b>The National Science Data Fabric (NSDF)</b> is a nationwide testbed advancing the <b>democratization of data-driven science</b> through an equitable, federated cyberinfrastructure platform. By seamlessly connecting <b>storage, compute, and networking resources</b> with an integrated software stack, NSDF provides researchers with scalable, easy-to-use tools for data access, analysis, and sharing. A strong focus on <b>education</b>, <b>outreach</b>, and <b>community engagement</b> ensures inclusive participation across institutions—including <b>a broader pool of universities across USA</b>—building a sustainable and open ecosystem for collaborative scientific discovery.</dd>
<dt>Web Page:</dt>
<dd><a href="http://nsdf.sci.utah.edu/" target="_blank"> NSDF</a></dd>
</div>
</div>
</div>
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<div class="col-sm-3"> <img src="assets/img/research/aitechx_logo.png"></div>
<div class="col-sm-9">
<h5 class="m-bottom-30">
<a href="https://research.utk.edu/oried/2025/09/15/ai-techx-awards-seed-funding-to-find-ai-solutions-to-real-world-problems/">
AI TechX: A UTK-IBM Partnership for Accelerating AI Inference for Real-World Impact
</a>
</h5>
<div class="dl-horizontal text-left tight m-bottom-30">
<dt>Source of Support:</dt>
<dd><a href="https://research.utk.edu/oried/2025/09/15/ai-techx-awards-seed-funding-to-find-ai-solutions-to-real-world-problems/" target="_blank">University of Tennessee’s AI TechX Initiative <a
href="https://research.utk.edu/oried/2025/09/15/ai-techx-awards-seed-funding-to-find-ai-solutions-to-real-world-problems/" target="_blank"></a></dd>
<dt>Project Period: </dt>
<dd>Jul 1, 2025 - Jun 30, 2026</dd>
<dt>Location of Project:</dt>
<dd>University of Tennessee, Knoxville</dd>
<dt>Description: </dt>
<dd> Advancing <b>AI inference performance</b> through <b>hardware–software co-design</b>, this collaboration unites <b>IBM’s expertise</b> in accelerator technologies and AI infrastructure with <b>UTK’s leadership</b> in domain-specific AI applications. The project develops a suite of <b>mini-applications</b> that serve as realistic testbeds for benchmarking, optimization, and evaluation across diverse accelerator architectures. By bridging hardware innovation with application-driven design, it drives breakthroughs in <b>efficiency</b>, <b>scalability</b>, and <b>AI system performance</b> for the broader research community.</dd>
</div>
</div>
</div>
<div class="sp-line-40"></div>
<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/show-award?AWD_ID=1900888"
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><a href="https://www.nsf.gov/awardsearch/show-award?AWD_ID=1900888" target="_blank">1900888</a></dd>
<dt>Location of Project:</dt>
<dd>University of Tennessee, Knoxville</dd>
<dt>Description: </dt>
<dd><b>ANACIN-X</b> investigates the nondeterminism in <b>MPI-based high-performance computing (HPC)</b> applications, where even runs with identical inputs on the same machine can produce <b>different execution paths</b>, <b>random bugs</b>, or <b>divergent results</b>. By analyzing and modeling these sources of variability, the project quantifies the recording overheads of <b>Record-and-Replay (R&R)</b> tools and develops new strategies to scale them for the <b>exascale era</b>. Through advanced event-graph analysis and trace mining, ANACIN-X advances the reliability, reproducibility, and debuggability of next-generation scientific applications.</dd>
<dt>Web Page:</dt>
<dd><a href="https://globalcomputing.group/anacin-x/" target="_blank"> Anacin-x </a></dd>
</div>
</div>
</div>
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<div class="col-sm-3"> <img src="assets/img/research/scheduling_logo.png"></div>
<div class="col-sm-9">
<h5 class="m-bottom-30">
Data-Aware Scheduling with the Convergence of HPC and Cloud
</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
href="https://www.llnl.gov/" target="_blank"></a></dd>
<dt>Project Period: </dt>
<dd>Aug 1, 2023 - Dec 31, 2024 </dd>
<dt>Location of Project:</dt>
<dd>University of Tennessee, Knoxville</dd>
<dt>Description: </dt>
<dd> <b>Data-Aware Scheduling with the Convergence of HPC and Cloud</b> develops intelligent scheduling strategies that optimize <b>data locality</b> and <b>performance</b> across complex scientific workflows. Building on <b>LLNL’s DYAD framework</b>, which facilitates data sharing between producer and consumer tasks within the Flux workload management system, the project extends DYAD with <b>runtime performance tracking</b> and <b>data-aware scheduling policies</b>. By integrating data-movement strategies for converged <b>HPC–Cloud environments</b>, this effort enhances workflow efficiency and enables scalable, transparent data sharing across hybrid computing platforms.</dd>
</div>
</div>
</div>
<div class="sp-line-40"></div>
<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 03, 2021 - Dec 31, 2023 </dd>
<dt>Location of Project:</dt>
<dd>University of Tennessee, Knoxville</dd>
<dt>Description: </dt>
<dd> <b>Leveraging Kokkos Abstractions to Automate Checkpointing</b> explores how <b>memory and execution patterns</b> in performance-portable applications can be automatically captured and preserved. By combining <b>Kokkos abstractions</b> with the <b>VELOC checkpointing framework</b>, the project develops efficient methods to ensure data persistence and recovery across diverse hardware platforms. This integration advances fault tolerance and accelerates reproducibility for next-generation high-performance computing applications.</dd>
</div>
</div>
</div>
<div class="sp-line-40"></div>
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<div class="col-sm-3"> <img src="assets/img/research/contenv_logo.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></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> <b>Leverage Containerized Environments for Reproducibility and Traceability of Scientific Workflows</b> develops a prototype framework that uses container technologies to improve the transparency and reliability of scientific workflows. By encapsulating each workflow component—data, software, and execution context—within an <b>individual container environments</b>, the project enables <b>automatic metadata collection</b>, <b>clear record trails</b>, and <b>strong links between data and metadata</b>. This approach simplifies the reproduction and verification of results across computing platforms, strengthening trust and traceability in computational science.</dd>
</div>
</div>
</div>
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<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> <b>Flux Scheduler Specializations: Improving Workflow Performance with Scheduler Structure and Policy Tuning</b> investigates how the <b>Flux workload manager</b> can be optimized to enhance workflow performance on large-scale systems. By modeling and tuning <b>scheduler configurations and policies</b>, the project identifies strategies that maintain efficiency even under <b>system stress</b>, such as fragmentation or resource contention. This work strengthens the adaptability and scalability of scientific workflows running on next-generation high-performance computing platforms.</dd>
</div>
</div>
</div>
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<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, 2023</dd>
<dt>Location of Project:</dt>
<dd>University of Tennessee, Knoxville</dd>
<dt>Description: </dt>
<dd><b>Augmenting Hatchet to Support Scalability and Replicability Solutions for HPC Applications</b> enhances the <b>Hatchet performance analysis framework</b> to diagnose and address performance bottlenecks in large-scale scientific applications. Leveraging Hatchet’s powerful <b>query language and analysis tools</b>, the project investigates <b>scalability</b> and <b>replicability</b> challenges in workloads of interest to <b>Lawrence Livermore National Laboratory (LLNL)</b>. The resulting tools enable researchers to identify root causes of inefficiencies and improve the reliability and performance of next-generation high-performance computing systems.</dd>
</div>
</div>
</div>
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<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/show-award?AWD_ID=2028923"
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><a href="https://www.nsf.gov/awardsearch/show-award?AWD_ID=2028923" target="_blank">2028923</a></dd>
<dt>Location of Project:</dt>
<dd>University of Tennessee, Knoxville</dd>
<dt>Description: </dt>
<dd><b>Collaborative Research: PPoSS—Performance, Scalability, Trust, and Reproducibility: A Community Roadmap to Robust Science in High-Throughput Applications</b> brings together a <b>cross-disciplinary community</b> to chart a path toward more <b>reliable and reproducible computational science</b>. Through a series of interactive <b>virtual world cafés</b>, participants collaborate to identify challenges and define actionable strategies for achieving <b>robust, high-throughput, and trustworthy scientific workflows</b>. This effort lays the groundwork for a national roadmap to strengthen the <b>performance, scalability, and integrity</b> of data-driven research.</dd>
<dt>Web Page:</dt>
<dd><a href="https://robustscience.org/#/" target="_blank"> RobustScience </a></dd>
</div>
</div>
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<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/show-award?AWD_ID=2041977"
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><a href="https://www.nsf.gov/awardsearch/show-award?AWD_ID=2041977" target="_blank">2041977</a></dd>
<dt>Location of Project:</dt>
<dd>University of Tennessee, Knoxville</dd>
<dt>Description: </dt>
<dd> <b>Collaborative Research: EAGER—Advancing Reproducibility in Multi-Messenger Astrophysics</b> strengthens the foundation of <b>open and reproducible science</b> in one of the most data-intensive frontiers of modern research. By analyzing the reproducibility processes behind landmark discoveries such as <b>LIGO’s gravitational-wave detections</b> and the <b>Event Horizon Telescope’s first black hole image</b>, the project develops best practices, documentation standards, and data-sharing methods for the astrophysics community. These efforts lay the groundwork for a sustainable roadmap toward <b>transparent, verifiable, and collaborative discovery</b> across multi-messenger astronomy. </dd>
</div>
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<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 18, 2020 - July 31, 2024</dd>
<dt>Location of Project:</dt>
<dd>University of Tennessee, Knoxville</dd>
<dt>Description: </dt>
<dd> <b>Study of Performance Portability of the Vector Particle-In-Cell (VPIC) Code across Architectures</b> examines how <b>plasma simulation performance</b> can be maintained and optimized as computing architectures evolve. The project evaluates how <b>VPIC</b>—a large-scale, high-performance plasma simulation code—adapts to new hardware by analyzing the trade-offs introduced by <b>performance-portability frameworks</b>. Through systematic benchmarking and analysis, the study advances understanding of how to achieve <b>efficient, scalable, and portable performance</b> on next-generation supercomputers.</dd>
</div>
</div>
</div>
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<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><b>JDRD: Empowering Training and Validation Stages in AI-Orchestrated Workflows</b> advances the design of <b>AI-driven scientific workflows</b> that integrate experimental, computational, and data processing steps across domains. Focusing on the <b>training and deployment of neural networks</b>, the project explores how models trained on clean, simulated data can be adapted to perform reliably on <b>real-world, noisy, and adversarial datasets</b>. By developing and integrating <b>mitigation strategies</b>, the effort strengthens the robustness, trustworthiness, and scientific utility of AI-orchestrated workflows.</dd>
</div>
</div>
</div>
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<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/show-award?AWD_ID=1941443"
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><a href="https://www.nsf.gov/awardsearch/show-award?AWD_ID=1941443" target="_blank">1941443</a></dd>
<dt>Location of Project:</dt>
<dd>University of Tennessee, Knoxville</dd>
<dt>Description: </dt>
<dd> <b>EAGER: Reproducibility in Computational and Data-Enabled Science—Paradigms, Practices, and Infrastructure</b> strengthens understanding of how the scientific community can ensure <b>trustworthy and repeatable results</b> in an era defined by large-scale computing and data. Building on the 2019 <b>National Academies report on Reproducibility and Replication in Science</b>, the project examines how its recommendations translate into <b>practical frameworks, tools, and cultural practices</b> for computational and data-driven research. By connecting reproducibility principles to real-world scientific workflows, the effort advances transparent, verifiable, and sustainable discovery across disciplines.</dd>
</div>
</div>
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<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/show-award/?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><a href="https://www.nsf.gov/awardsearch/show-award/?AWD_ID=1841758" target="_blank">1841758</a></dd>
<dt>Location of Project:</dt>
<dd>University of Tennessee, Knoxville</dd>
<dt>Description: </dt>
<dd><b>Analytics for Molecular Dynamics (A4MD)</b> addresses the growing <b>data analysis challenges</b> of large-scale molecular dynamics (MD) simulations running on next-generation supercomputers. By integrating <b>machine learning</b>, <b>data analytics</b>, <b>and workflow management</b> with <b>high-performance computing (HPC)</b>, A4MD enables the real-time analysis of MD data as it is produced. This interdisciplinary effort accelerates scientific insight, making molecular simulation more <b>scalable</b>, <b>automated</b>, and <b>data-driven</b> for diverse research communities.</dd>
<dt>Web Page:</dt>
<dd><a href="https://analytics4md.org/" target="_blank"> Analytics4MD </a></dd>
</div>
</div>
</div>
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<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/show-award?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><a href="https://www.nsf.gov/awardsearch/show-award?AWD_ID=1724843" target="_blank">1724843</a></dd>
<dt>Location of Project:</dt>
<dd>University of Tennessee, Knoxville</dd>
<dt>Description: </dt>
<dd><b>CIF21 DIBBs: Cyberinfrastructure Tools for Precision Agriculture in the 21st Century</b> advances <b>data-driven agriculture</b> through the development of <b>SOMOSPIE</b>—a cyberinfrastructure platform that integrates computer science and environmental data to support precision farming. By combining large-scale datasets on <b>soils, landscapes, climate, and ecosystems</b> with advanced computational and ecoinformatics tools, the project enables real-time analysis and informed decision-making for sustainable agricultural practices. This interdisciplinary effort bridges <b>cyberinfrastructure and environmental science</b>, empowering researchers and farmers to better understand and manage complex agroecosystems.</dd>
<dt>Web Page:</dt>
<dd><a href="https://globalcomputing.group/somospie/" target="_blank">SOMOSPIE</a></dd>
</div>
</div>
</div>
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<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><b>Study of Data-Intensive Workflows on Next-Generation Systems with Emphasis on Memory Access</b> investigates how <b>memory behavior</b> affects the performance, efficiency, and reproducibility of <b>data-intensive applications</b> on emerging high-performance computing systems. The project develops a <b>C++ suite of mini-applications</b> to measure and analyze <b>memory access patterns, data management costs, and power consumption</b>. These insights inform the design of more efficient, replicable workflows optimized for next-generation architectures.</dd>
</div>
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</div>
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<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><b>Moving Towards Self-Adjusting Scheduling Policies for High-Performance Workflows with Flux’s Fully Hierarchical Scheduling</b> explores how <b>adaptive scheduling strategies</b> can improve workflow efficiency on large-scale computing systems. Using the <b>Flux workload manager</b>, the project systematically studies <b>hierarchical scheduling models</b> and develops methods that allow workflows to <b>dynamically select optimal scheduling policies at runtime</b>. This project enhances system utilization, scalability, and performance for diverse scientific applications.</dd>
</div>
</div>
</div>
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<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 – Jul 31, 2020</dd>
<dt>Location of Project:</dt>
<dd>University of Tennessee, Knoxville</dd>
<dt>Description: </dt>
<dd><b>Driving Next-Generation Schedulers with Machine Learning-Based Application Patterns</b> applies <b>machine learning</b> to improve how <b>HPC schedulers</b> handle irregular and dynamic job behaviors. By identifying and modeling <b>application performance patterns</b>, the project integrates this knowledge into <b>multi-objective scheduling frameworks</b> to enhance system utilization and workflow throughput. Building on prior collaborations with <b>Lawrence Livermore National Laboratory</b>, this work advances adaptive, intelligent scheduling strategies for next-generation supercomputing environments.</dd>
</div>
</div>
</div>
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<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/show-award?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><a href="https://www.nsf.gov/awardsearch/show-award?AWD_ID=1841399" target="_blank">1841399</a></dd>
<dt>Location of Project:</dt>
<dd>University of Tennessee, Knoxville</dd>
<dt>Description: </dt>
<dd><b>Collaborative: EAGER—Exploring and Advancing the State of the Art in Robust Science in Gravitational Wave Physics</b> strengthens the <b>reliability and reproducibility</b> of discoveries in <b>gravitational wave research</b>. By surveying and analyzing the <b>LIGO scientific workflows</b>—which combine experimental data, large-scale computation, and data processing—the project identifies opportunities to improve <b>workflow transparency, validation, and automation</b>. These insights help shape best practices for <b>robust, data-driven science</b> in one of physics’ most groundbreaking fields.</dd>
</div>
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<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><b>Building a “Miniature” Version of ORNL’s Summit Supercomputer for Computational Science Research at UTK</b> expands the university’s capacity for <b>high-performance computing (HPC)</b> research and education. Supported by the <b>IBM Global University Program</b>, the award enabled the acquisition of a <b>supercomputer system</b> that mirrors the architecture of <b>Oak Ridge National Laboratory’s Summit</b>, once the world’s fastest supercomputer. This resource empowers UTK researchers and students to conduct advanced computational experiments, foster innovation, and train the next generation of HPC scientists.
</dd>
</div>
</div>
</div>
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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>
</div>
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