[{"id":729,"date":"2025-08-13T12:44:08","date_gmt":"2025-08-13T16:44:08","guid":{"rendered":"https:\/\/events.ornl.gov\/smc2025\/?page_id=729"},"modified":"2025-08-23T20:47:46","modified_gmt":"2025-08-24T00:47:46","slug":"committee","status":"publish","type":"page","link":"https:\/\/events.ornl.gov\/smc2025\/committee\/","title":{"rendered":"Committee"},"content":{"rendered":"\n
<\/div>\n\n\n\n
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Dr. Gina Tourassi<\/a><\/h2>\n\n\n\n

SMC 25 Chair<\/h3>\n\n\n\n

Associate Laboratory Director, Computing and Computational Sciences Directorate <\/p>\n\n\n\n

Oak Ridge National Laboratory<\/p>\n<\/div><\/div>\n\n\n\n

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<\/a><\/a>Heidi Hanson<\/a><\/h3>\n\n\n\n
SMC 25 Co-Chair<\/h5>\n\n\n\n

Group Leader, Computing and Computational Sciences Directorate <\/p>\n\n\n\n

Oak Ridge National Laboratory<\/p>\n<\/div><\/div>\n\n\n\n

<\/div>\n\n\n\n
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<\/a>Oscar Hernandez, PhD<\/a><\/h3>\n\n\n\n
SMC 25 Co-Chair<\/h5>\n\n\n\n

Senior Computer Scientist, Computing and Computational Sciences Directorate <\/p>\n\n\n\n

Oak Ridge National Laboratory<\/p>\n<\/div><\/div>\n\n\n\n

<\/div>\n\n\n\n
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Suzanne Parete-Koon, PhD<\/a><\/h3>\n\n\n\n
SMC 25 Co-Chair<\/h5>\n\n\n\n

HPC Engineer, Computing and Computational Sciences Directorate <\/p>\n\n\n\n

Oak Ridge National Laboratory<\/p>\n<\/div><\/div>\n\n\n\n

<\/div>\n\n\n\n
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Alysha Tackett<\/a><\/h3>\n\n\n\n
SMC 25 Organizer, Logistics Director, and Corporate Partnerships Manager<\/h5>\n\n\n\n

Programs & Planning Specialist, Computing and Computational Sciences Directorate <\/p>\n\n\n\n

Oak Ridge National Laboratory<\/p>\n<\/div><\/div>\n\n\n\n

<\/div>\n\n\n\n
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Lora Wolfe<\/a><\/h3>\n\n\n\n
SMC 25 Executive Assistant and Coordinator<\/h5>\n\n\n\n

Executive Administrative Assistant, Computing and Computational Sciences Directorate <\/p>\n\n\n\n

Oak Ridge National Laboratory<\/p>\n<\/div><\/div>\n\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

Dr. Gina Tourassi SMC 25 Chair Associate Laboratory Director, Computing and Computational Sciences Directorate Oak Ridge National Laboratory Heidi Hanson SMC 25 Co-Chair Group Leader, Computing and Computational Sciences Directorate Oak Ridge National Laboratory Oscar Hernandez, PhD SMC 25 Co-Chair Senior Computer Scientist, Computing and Computational Sciences Directorate Oak Ridge National Laboratory Suzanne Parete-Koon, PhD<\/p>\n","protected":false},"author":44,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"class_list":["post-729","page","type-page","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/events.ornl.gov\/smc2025\/wp-json\/wp\/v2\/pages\/729","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/events.ornl.gov\/smc2025\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/events.ornl.gov\/smc2025\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/events.ornl.gov\/smc2025\/wp-json\/wp\/v2\/users\/44"}],"replies":[{"embeddable":true,"href":"https:\/\/events.ornl.gov\/smc2025\/wp-json\/wp\/v2\/comments?post=729"}],"version-history":[{"count":0,"href":"https:\/\/events.ornl.gov\/smc2025\/wp-json\/wp\/v2\/pages\/729\/revisions"}],"wp:attachment":[{"href":"https:\/\/events.ornl.gov\/smc2025\/wp-json\/wp\/v2\/media?parent=729"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}},{"id":634,"date":"2025-08-11T08:23:39","date_gmt":"2025-08-11T12:23:39","guid":{"rendered":"https:\/\/events.ornl.gov\/smc2025\/?page_id=634"},"modified":"2025-08-23T20:53:03","modified_gmt":"2025-08-24T00:53:03","slug":"panel-members","status":"publish","type":"page","link":"https:\/\/events.ornl.gov\/smc2025\/panel-members\/","title":{"rendered":"Panel Members"},"content":{"rendered":"\n

Featured Session II: Energy Security Panel Discussion<\/h2>\n\n\n\n
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Panel Moderator<\/h3>\n\n\n\n
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Mason Rice, PhD<\/a><\/h4>\n\n\n\n

Oak Ridge National Laboratory<\/p>\n\n\n\n

Division Director, Cyber Resilience and Intelligence Division <\/p>\n\n\n\n

National Security Sciences Directorate<\/p>\n\n\n\n

<\/p>\n<\/div><\/div>\n\n\n\n

<\/div>\n\n\n\n

Panel Members<\/h3>\n\n\n\n
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Kenneth (KC) Carnes II <\/h4>\n\n\n\n

Tennessee Valley Authority <\/p>\n\n\n\n

Vice President, Cybersecurity & Chief Information Security Officer<\/p>\n<\/div><\/div>\n\n\n\n

KC currently serves as the VP Cybersecurity & Chief Information Security Officer of the Tennessee Valley Authority. KC is also designated as the Chief Artificial Intelligence Officer and the agency\u2019s Federal Senior Intelligence Coordinator. Starting in nuclear generation, KC has spent his career in public power in various regions and has been in the industry for over twenty-five years. He has experience supporting all verticals of electric operations, from the perspectives of security, resiliency, and recovery. He is passionate about information sharing and building partnerships to enable understanding of emerging threats in this fast-changing industry. KC has previously been the chair of multiple industry groups and remains active in various security organizations and the with broader industry. KC has a degree in Computer Science and an MBA.<\/h6>\n\n\n\n
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James Goosby<\/h4>\n\n\n\n

Auburn University<\/p>\n\n\n\n

McCrary Institute for Cyber and Critical Infrastructure Security Southern Company Executive in Residence<\/p>\n\n\n\n

<\/p>\n<\/div><\/div>\n\n\n\n

James currently serves as the Southern Company Executive in Residence at Auburn University\u2019s McCrary Institute for Cyber and Critical Infrastructure Security. In this role Goosby works closely with industry, academia, and federal partners, including national laboratories on electric grid cyber security strategies. With more than 28 years of service at Southern Company, <\/h6>\n\n\n\n
James co-led an initiative to implement an enhanced Operations cyber and NERC-CIP program support model and has held various operational and engineering support leadership positions. Prior to joining Southern Company, James served in the U.S. Marines. <\/h6>\n\n\n\n
He holds a B.S. in electrical engineering from Auburn University. James also served as a senior fellow at Auburn University\u2019s McCrary Institute, a prestigious group of nationally renowned cyber and critical-infrastructure security experts.<\/h6>\n\n\n\n
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David Manz, PhD <\/h4>\n\n\n\n

Pacific Northwest National Laboratory <\/p>\n\n\n\n

Principal Cyber Security Scientist, National Security Directorate<\/p>\n<\/div><\/div>\n\n\n\n

David Manz, Ph.D. is a Principal Cyber Security Scientist within the National Security Directorate at Pacific Northwest National Laboratory (PNNL). <\/h6>\n\n\n\n
David’s areas of expertise include cyber resilience and critical infrastructure security. Since 2010 he has led multiple cybersecurity related projects as both primary investigator and project manager including co-leading the PNNL investment, PACiFiC: Proactive Adaptive Cybersecurity Framework for Control. He has been responsible in the design and execution of over $30M in project funding during his career.<\/h6>\n\n\n\n
David is a co-author of the book \u201cResearch Methods for Cyber Security,\u201d as well as numerous papers and presentations on cybersecurity, control system security, and cryptographic key management. He is a recipient of PNNL\u2019s Ronald L. Brodzinski Laboratory Director\u2019s Early Career Exceptional Achievement Award.<\/h6>\n\n\n\n
Prior to PNNL, David spent five years as a researcher on Group Key Management Protocols for the Center for Secure and Dependable Systems at the University of Idaho (U of I). He has taught undergraduate and graduate computer science courses at U of I, and as an adjunct faculty at Washington State University.<\/h6>\n\n\n\n
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\n

Kristin Nordin<\/h4>\n\n\n\n

Oak Ridge National Laboratory<\/p>\n\n\n\n

Staff Consultant, Energy and Control Systems Security, National Security Sciences Directorate<\/p>\n<\/div><\/div>\n\n\n\n

Kristin Nordin joined Oak Ridge National Laboratory in 2025 and leads projects to mitigate cyber threats to US critical infrastructure. Prior to joining ORNL, she spent fifteen years supporting the intelligence community and led interdisciplinary and cross-laboratory teams to address national security challenges and critical cyber threats to the U.S. energy sector. She holds a B.A. from the University of Colorado and an M.A. from the University of St Andrews.<\/h6>\n\n\n\n
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Joseph Price<\/h3>\n\n\n\n

Deloitte & Touche LLP<\/p>\n\n\n\n

Government and Public Service OT Cybersecurity Program Lead Senior Manager, Cyber & Strategic Risk<\/p>\n\n\n\n

<\/p>\n\n\n\n

<\/p>\n<\/div><\/div>\n\n\n\n

Joseph Price has worked in cybersecurity for 30 years, serving in various roles in both defensive and offensive cyber disciplines. He began his journey in cybersecurity before the term \u201ccyber\u201d was even used\u2026 as a Computer Network Defense Crew Commander serving in the Air Force\u2019s first Information Warfare Squadron. He quickly developed a passion for the work on the front lines of such early watershed events as Solar Sunrise, Code Red, and the Nimda worm. Most of his time was spent working in offensive operations and capability development, which provides him a unique perspective of how an adversary approaches targeting and impacting our critical infrastructure.  After 20 years with the Department of Defense, Joseph moved to Idaho Falls, Idaho, in 2014 and began working for the Idaho National Laboratory, where he served as Deputy Director for Critical Infrastructure Protection and focused on nation-state threats to the U.S.\u2019s Energy, Water, and Transportation sectors. Again, he found himself directly involved in watershed moments in OT cybersecurity such as the 2015 and 2016 cyber attacks against Ukraine\u2019s electric infrastructure as well as co-authoring the seminal work on Cyber-Informed Engineering (CIE). At Deloitte, Joseph leads our OT cybersecurity program along with investments, focusing on growth in multiple critical infrastructure verticals, serving both government and commercial clients.<\/h6>\n\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

Featured Session II: Energy Security Panel Discussion Panel Moderator Mason Rice, PhD Oak Ridge National Laboratory Division Director, Cyber Resilience and Intelligence Division National Security Sciences Directorate Panel Members Kenneth (KC) Carnes II Tennessee Valley Authority Vice President, Cybersecurity & Chief Information Security Officer KC currently serves as the VP Cybersecurity & Chief Information Security<\/p>\n","protected":false},"author":44,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"class_list":["post-634","page","type-page","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/events.ornl.gov\/smc2025\/wp-json\/wp\/v2\/pages\/634","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/events.ornl.gov\/smc2025\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/events.ornl.gov\/smc2025\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/events.ornl.gov\/smc2025\/wp-json\/wp\/v2\/users\/44"}],"replies":[{"embeddable":true,"href":"https:\/\/events.ornl.gov\/smc2025\/wp-json\/wp\/v2\/comments?post=634"}],"version-history":[{"count":0,"href":"https:\/\/events.ornl.gov\/smc2025\/wp-json\/wp\/v2\/pages\/634\/revisions"}],"wp:attachment":[{"href":"https:\/\/events.ornl.gov\/smc2025\/wp-json\/wp\/v2\/media?parent=634"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}},{"id":712,"date":"2025-08-08T08:56:50","date_gmt":"2025-08-08T12:56:50","guid":{"rendered":"https:\/\/events.ornl.gov\/smc2025\/?page_id=712"},"modified":"2025-08-08T08:56:50","modified_gmt":"2025-08-08T12:56:50","slug":"keynote-speaker","status":"publish","type":"page","link":"https:\/\/events.ornl.gov\/smc2025\/keynote-speaker\/","title":{"rendered":"Keynote Speaker"},"content":{"rendered":"\n

<\/div>\n\n\n\n
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Dr. Manish Parashar<\/h2>\n\n\n\n

University of Utah<\/h4>\n\n\n\n

Manish Parashar is the inaugural Chief AI Officer at the University of Utah. He is also Executive Director of the Scientific Computing and Imaging (SCI) Institute, and Presidential Professor in the Kalhert School of Computing. He leads the University\u2019s One-U Responsible AI Initiative.<\/p>\n\n\n\n

Manish\u2019s expertise is in high-performance parallel and distributed computing and cyberinfrastructure, and his research has enabled new insights across multiple science domains.<\/p>\n\n\n\n

Manish has received several awards for his research and leadership, including the 2023 IEEE Computer Society Sidney Fernbach Award, the 2024 CRA Distinguished Service Award, and the 2025 ACM Distinguished Service Award. Manish is a Fellow of AAAS, ACM, and IEEE.<\/p>\n<\/div><\/div>\n\n\n\n

<\/div>\n\n\n\n

Presentation Title: Harnessing the Digital Continuum for Science<\/em><\/h4>\n\n\n\n

Emerging data-driven, AI-enabled scientific workflows, such as those underlying digital twins, integrate distributed data sources with pervasively available computing resources, spanning HPC to the edge, to understand end-to-end phenomena, drive experimentation, and facilitate critical decision making. Despite the exponential growth of available digital data sources at the edge and the ubiquity of non-trivial computational power for processing this data, realizing such science workflows remains challenging. This talk will explore a digital continuum spanning resources at the edges, in HPC centers and clouds, and in-between, and providing abstractions that can be harnessed to support science. The talk will also introduce recent research in programming abstractions that can express what data should be processed and when and where it should be processed, as well as autonomic middleware services that automate the discovery of resources and the orchestration of computations across these resources.<\/p>\n\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

Dr. Manish Parashar University of Utah Manish Parashar is the inaugural Chief AI Officer at the University of Utah. He is also Executive Director of the Scientific Computing and Imaging (SCI) Institute, and Presidential Professor in the Kalhert School of Computing. He leads the University\u2019s One-U Responsible AI Initiative. Manish\u2019s expertise is in high-performance parallel<\/p>\n","protected":false},"author":44,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"class_list":["post-712","page","type-page","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/events.ornl.gov\/smc2025\/wp-json\/wp\/v2\/pages\/712","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/events.ornl.gov\/smc2025\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/events.ornl.gov\/smc2025\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/events.ornl.gov\/smc2025\/wp-json\/wp\/v2\/users\/44"}],"replies":[{"embeddable":true,"href":"https:\/\/events.ornl.gov\/smc2025\/wp-json\/wp\/v2\/comments?post=712"}],"version-history":[{"count":0,"href":"https:\/\/events.ornl.gov\/smc2025\/wp-json\/wp\/v2\/pages\/712\/revisions"}],"wp:attachment":[{"href":"https:\/\/events.ornl.gov\/smc2025\/wp-json\/wp\/v2\/media?parent=712"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}},{"id":589,"date":"2025-07-14T14:08:15","date_gmt":"2025-07-14T18:08:15","guid":{"rendered":"https:\/\/events.ornl.gov\/smc2025\/?page_id=589"},"modified":"2025-08-21T13:29:08","modified_gmt":"2025-08-21T17:29:08","slug":"early-career-poster-session","status":"publish","type":"page","link":"https:\/\/events.ornl.gov\/smc2025\/early-career-poster-session\/","title":{"rendered":"Early Career Poster Session"},"content":{"rendered":"\n

<\/div>\n\n\n\n

Poster Session Chairs<\/h2>\n\n\n\n
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Dr. Antigoni Georgiadou, ORNL<\/a><\/h4>\n<\/div><\/div>\n\n\n\n
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Dr. Asim YarKhan, ORNL<\/a><\/h4>\n<\/div><\/div>\n\n\n\n
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Poster Session Speakers<\/h2>\n\n\n\n
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Dr. Jackson Gainer, ORNL<\/a><\/h4>\n<\/div><\/div>\n\n\n\n
<\/div>\n\n\n\n
<\/div>\n\n\n\n
\"Suzanne<\/figure>
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Dr. Suzanne Parete-Koon, ORNL<\/a><\/h4>\n<\/div><\/div>\n\n\n\n
<\/div>\n\n\n\n

Poster Presenters<\/h2>\n\n\n\n
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Dr. Philip Dee, ORNL<\/a><\/h5>\n\n\n\n

Eugene P. Wigner Distinguished Staff Fellow<\/em><\/p>\n\n\n\n

Poster Title: Accelerating Discoveries in Quantum Materials Using Machine Learning Augmented Many-body Approaches<\/em><\/h6>\n\n\n\n
Machine learning is powering a new wave of advances in quantum many body research by accelerating solvers, generating synthetic data, optimizing workflows, and more. Surrogate models now predict forces or energies for updates on the fly, lowering autocorrelation times in quantum Monte Carlo and molecular dynamics runs while retaining their nonperturbative accuracy. On the data side, rapid diagrammatic approximations can be chained into cost-effective pipelines that produce large, labeled datasets\u2014fuel for training models that interpret spectroscopic experiments in real-time. We will survey our ongoing work on both fronts. First, we present self-learning hybrid Monte Carlo for a 2D electron\u2013phonon model to learn forces in a quantum model. Second, we describe a framework that stitches together fast, perturbative solvers to create diverse training corpora, enabling ML models to deliver instant, physically constrained feedback on experimental datasets. Together, these advances chart a route toward routine, high\u2011resolution exploration of quantum materials.<\/h6>\n<\/div><\/div>\n\n\n\n
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Dr. Ayana Ghosh, ORNL<\/a><\/h5>\n\n\n\n
Poster Title: Physics-guided causal reasoning for complex material systems<\/em><\/h6>\n\n\n\n
Traditional computational methodologies such as density functional theory (DFT), molecular dynamics, Monte Carlo simulations have long guided the study of complex materials. Today, the integration of AI\/machine learning (ML), high-performance computing, with real-time theory-experiment loops is transforming this process\u2014enabling autonomous discovery and optimization. We present a multifaceted framework combining first-principles simulations, ML, proxy theory design, infused with causal reasoning to explore complex physical phenomena such as cation ordering, polarization switching alongside emergent behaviors in perovskites, two-dimensional materials. A key focus is developing and adapting causal models\u2014using algorithms like LiNGAM, PC\u2014tailored to incorporate physical constraints, domain knowledge, enabling us to move beyond surface correlations to uncover underlying mechanisms. By embedding causal frameworks into ML pipelines, we seek to enhance interpretability, improve generalization, thereby enabling actionable scientific insights. This paradigm is aimed to accelerate closed-loop materials design while advancing our fundamental understanding of complex physical systems.<\/h6>\n<\/div><\/div>\n\n\n\n
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Dr. Matthias Heinz, ORNL<\/a><\/h5>\n\n\n\n

Eugene P. Wigner Distinguished Staff Fellow<\/em><\/p>\n\n\n\n

Poster Title: Leadership computing for atomic nuclei and fundamental physics<\/em><\/h6>\n\n\n\n
Atomic nuclei are essential probes for fundamental physics, particularly for possible new physics beyond the standard model. Theoretical predictions for the properties of these nuclei are required to identify and interpret rare events being searched for in high-precision experiments across the globe. Precise and robust predictions require large-scale first-principles computations. We are developing leadership computations for the structure of deformed nuclei to predict key properties relevant for the possible discovery of new fundamental physics.<\/h6>\n<\/div><\/div>\n\n\n\n
<\/div>\n\n\n\n
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Dr. Paul Kairys, ORNL<\/a><\/h5>\n\n\n\n

Alvin M.<\/em> Weinberg Distinguished Staff Fellow<\/em><\/p>\n\n\n\n

Poster Title: Entangling Neutron and Quantum Science<\/em><\/h6>\n\n\n\n
Quantum technologies are anticipated to significantly increase the rate and quality of science by providing fundamentally new avenues for processing, sensing, and communicating information. This ongoing work is concentrated upon the design of a quantum simulation and metrology framework for neutron sciences. It’s focus is to define and characterize quantum algorithms for adaptive neutron scattering experiments and produce a quantitative analysis of the metrological utility of entangled neutron scattering in the study of quantum materials.<\/h6>\n<\/div><\/div>\n\n\n\n
<\/div>\n\n\n\n
Poster Presenter<\/strong><\/td>Subject Area<\/strong><\/td>Poster Title<\/strong><\/td>Poster <\/strong>Abstract<\/strong><\/td><\/tr>
Yejoon Ham,
Bearden High School, Knoxville, TN
High School Student<\/td>		Artificial Intelligence<\/td>ML-Based Propylene Yield Prediction in Thermo-Catalytic Propane Dehydrogenation<\/td>Propane dehydrogenation is a chemical process used to convert propane into propylene, an important compound in plastic production and manufacturing. Applying Joule heating with pulsed heating and quenching is an emerging technology that leverages an electrical potential gradient to perform catalytic dehydrogenation. To predict conversion and selectivity to optimize yield, AI\/surrogate models such as machine learning, deep learning, and ensemble models were trained on experimental data. Despite limited sample data, random forest saw high accuracies when using peak temperature, heating and quenching time, and voltage as inputs. Using hyperparameters tuned with 5-fold cross-validation, it boasted an out-of-bag (OOB) score of 85\u201390%. Ensemble models built on diverse base models consistently performed around 90% in analyzing catalysts’ elemental composition for both conversion and selectivity. These findings indicate AI models are a viable, predictive technology for optimizing Joule heating and propane dehydrogenation processes.<\/td><\/tr>
Maggie Miller,
Bearden High School, Knoxville, TN
High School Student<\/td>		Artificial Intelligence<\/td>Brain Abnormality Detection Using Convolutional Neural Networks<\/td>Tumors, strokes, and infections can cause irreversible damage or death without prompt diagnosis. Limited access to expert radiologists and slow manual MRI analysis delay critical treatment. This study explores Convolutional Neural Networks (CNNs) to automate and accelerate accurate diagnosis of these conditions.<\/td><\/tr>
Dominick Pelaia,
High School Student<\/td>		Artificial Intelligence<\/td>AI-Driven Evaluation of Compounds for Mitigating Alzheimer\u2019s Disease<\/td>Alzheimer\u2019s disease (AD) is driven by complex biological processes including amyloid-beta aggregation, tau hyperphosphorylation, neuroinflammation, and oxidative stress. To support the discovery of multi-target therapeutic compounds, we developed a novel dataset and machine learning pipeline linking small molecules to AD pathological pathways. Using a Python-based workflow, we extracted gene targets from GeneCards and retrieved corresponding bioactive compounds and molecular descriptors from ChEMBL, including SMILES strings and activity data. We then trained a neural network using PyTorch and RDKit to predict the probability that a given compound influences each of the four major AD pathways. Our model outputs four pathway probabilities for each compound, helping to prioritize molecules with broad or targeted biological potential. The model achieved 99.1% accuracy on test data and was applied to 100 natural compounds, with several results supported by findings in existing literature. This approach demonstrates how AI can guide early-stage drug discovery research.<\/td><\/tr>
Channing Tan,
University of Tennessee, Knoxville
Undergraduate Student<\/td>		Artificial Intelligence<\/td>Spatial-temporal GNN based Zone Load Forecasting for Texas Electricity Market<\/td>Accurate short-term electricity load forecasting is essential for electricity producers to submit competitive bids in market transactions and for Distribution System Operators to ensure efficient economic dispatch. This project focuses on developing a one-hour-ahead load prediction model for the Electric Reliability Council of Texas (ERCOT) region. While traditional forecasting methods and existing artificial intelligence techniques have been widely applied, many fail to capture spatial dependencies inherent in regional load patterns. To address these limitations, this study explores the use of Graph Neural Networks (GNNs) for enhanced prediction accuracy. A graph-based representation of the ERCOT network is constructed using the geographical layout of its eight weather zones. Spatial-temporal attention mechanisms and various GNN layer architectures are investigated. The proposed model leverages publicly available historical load and weather data and is benchmarked against classical forecasting approaches to evaluate its performance.<\/td><\/tr>
Damien Staebler,
Pellissippi State Community College, Knoxville, TN
Undergraduate Student<\/td>		Artificial Intelligence and High Performance Computing<\/td>Establishment of a Histopathological Image Analysis Pipeline on the Frontier Supercomputer<\/td>Histopathology has traditionally been analyzed manually, but recent advances in technology now enable the use of Machine Learning models to accelerate this process through Whole Slide Imaging (WSI). However, a major challenge is the enormous size of each WSI file\u2014often several gigabytes\u2014which makes them computationally intensive to process using conventional methods. To address this, we leveraged High-Performance Computing to handle large-scale data efficiently. Each WSI is first preprocessed by removing the background and retaining only the tissue regions. The tissue is then divided into multiple non-overlapping patches of 256×256 pixels, which collectively form a \u201cbag\u201d for analysis. Each patch is processed through a ResNet50 convolutional neural network to extract feature embeddings, which are then combined into a single feature vector used for WSI-level classification. By utilizing the Frontier supercomputer, we achieved a classification accuracy of 98%, along with an approximate 40,000 times speedup compared to standard workstation performance. <\/td><\/tr>
Nasik Muhammad Nafi,
Oak Ridge National Laboratory
Postdoc Research Associate<\/td>		Artificial Intelligence, Material Science <\/td>Enabling Multimodal Microstructure Image Generation through Vision Transformer-based Scalable Diffusion Model<\/td>Analyzing the microscopic structure of materials \u2014 that denotes its physical and mechanical properties \u2014 is essential to understand the structure-property relationship, optimize the manufacturing processes, and design materials with intended functionalities. However, microstructure imaging in high resolution is often very costly and time-consuming due to sophisticated instrumentation and long acquisition time. To address this, we developed a scalable generative model that can generate microstructure images conditioned on the material (polycrystal, concrete) and the modality (X-ray and Neutron Computed Tomography (CT), Electron Microscopy, etc.). We integrated an efficient Vision Transformer (ViT) architecture with a diffusion-based generative model. Such integration allows us to simultaneously leverage the scalability and self-attention mechanism of transformers as well as the benefits of diffusion models such as stability and high-quality output. Our initial experimentation with different modality and materials shows promising results in capturing accurate structural representation using reasonable computing resources.<\/td><\/tr>
Ran Elgedawy,
University of Tennessee, Knoxville
Graduate Student<\/td>		Applied AI<\/td>\u200b Enhancing Safety at DOE Sites through Predictive AI \u200b<\/td>The ORNL AI Forecasting Tool is an advanced artificial intelligence framework developed to improve operational safety at Department of Energy (DOE) facilities. It targets two key challenges: predicting and preventing incidents through risk analysis, and reducing human error in safety documentation. The system supports two main applications. The first uses a structured AI workflow to forecast risks by analyzing current work plans and retrieving patterns from 65,000 historical DOE incident records. It includes four core components: Work Plan input processing, Information Retrieval, Risk Analysis using large language models, and Safety Standards Integration with ORNL\u2019s Standards Based Management System to ensure compliance. The second application automates Activity-Based Work Control (ABWC) document generation for Chemical Sciences, replacing labor-intensive manual processes with a generative AI system linked to PubChem databases. This proof of concept significantly improves accuracy and efficiency, laying the groundwork for a conversational AI interface to streamline ABWC-Chemistry safety documentation further. <\/td><\/tr>
Cindy Salinas Avelino,
University of Tennessee, Knoxville
Undergraduate Student<\/td>		Computational and Predictive Biology<\/td>From Doubt to Data: Mapping Poplar-Microbe-Environment Networks <\/td>“Poplar (Populus trichocarpa) is a widely distributed resource across the U.S. and is vital to biofuel production, though its productivity remains sensitive to climate variability.

Among the climate factors affecting productivity, drought is one of the most influential. Ilyonectria colonization in plants impacts cellular metabolic and mechanistic functions. Because Ilyonectria is heavily influenced by environmental conditions, understanding this interaction is key to promoting plant growth and adaptation.

Network-based tools such as Multiplex Embedding of Networks for Team-Based Omics Research (MENTOR) and large language models (LLMs) like the Mentor Interpretation Agent (MENTOR-IA) are instrumental in mapping interactions among microbes, the environment, and plant genetics by integrating GWAS with multi-omics data. In this project, we explore how this approach accelerates plant gene discovery by biologically contextualizing multi-omic data\u2014transforming uncertain hypotheses into clear insights!”<\/td><\/tr>
Colter Richardson,
University of Tennessee, Knoxville
Graduate Student<\/td>		Computational Astrophysics<\/td>Low-Frequency Gravitational Waves from Core-Collapse Supernovae: Theory and Detection Prospects<\/td>The low-frequency contribution to gravitational wave signals from core-collapse supernovae has often been overlooked due to the rapid increase in the LIGO noise floor at 10 Hz, but recent studies have illuminated the rich content of core-collapse supernova gravitational wave emission in this frequency range and the exciting prospects for its detection. Here, I will present a brief review of these past studies. I will also present our methodology for the detection of core-collapse supernova gravitational wave memory, utilizing matched filtering, as well as the low-frequency gravitational wave emission sourced from both the matter and the anisotropic neutrino emission in the three-dimensional models of the CHIMERA group, associated with the most recent release of gravitational wave data from that group.<\/td><\/tr>
Cheng-Hsin Cheng,
University of Illinois Urbana-Champaign, IL
Graduate Student<\/td>		Computational Astrophysics<\/td>Compact object initial data in Flash-X<\/td>Neutron stars are astrophysical laboratories where the properties of dense nuclear matter can be probed in a regime inaccessible by terrestrial experiments. The gravitational and electromagnetic signals resulting from the merger of neutron stars can be predicted through numerical simulation, a problem requiring solving Einstein’s Equations of general relativity coupled to general relativistic hydrodynamics and neutrino radiation transport. The initial data for these simulations are highly non-trivial and require the solution of coupled non-linear elliptic partial differential equations. Using spectral elliptic solvers provided by FUKA, we construct compact object initial data and interface it with the newly developed numerical relativity unit in Flash-X. Our work paves the foundation for numerical relativity simulations of binary neutron star and black hole neutron mergers with realistic microphysics and neutrino radiation transport at scale using Flash-X.<\/td><\/tr>
Alyvia Bitterly,
Roane County High School, TN
High School Student<\/td>		Data Science\/ Machine Learning<\/td>The Great Texas Power Outage Study<\/td>The goal of this project was to explore patterns between the frequency of power outages and natural events. Using the Department of Energy\u2019s EAGLE-I Power Outage Database and resources at the Oak Ridge Leadership Computing Facility, we applied K-Means clustering to group outages over the past five years by U.S. location and time of year. We then used a deep learning method called Long Short-Term Memory (LSTM), a type of Recurrent Neural Network, to predict outages for the last year of the dataset and compared peaks in the predictions to K-Means cluster centroids by season. I tested an existing K-Means implementation developed by Thomas J. Fillers as part of a student training activity and worked with my mentors to develop and apply the LSTM model. While we didn\u2019t directly link outages to specific weather events, our results suggest that long-term outage data follows seasonal, region-specific cycles\u2014similar to natural weather patterns.<\/td><\/tr>
Nabayan Chaudhury,
Virginia Tech University
Graduate Student<\/td>		High-performance computing, Supercomputing, Scientific computing<\/td>Adaptive Task Scheduling for Performance-Portable Scientific Computing on Extremely Heterogeneous Systems<\/td>Achieving performance portability across extremely heterogeneous systems remains a critical challenge for computing applications. We present an adaptive task scheduling framework that can maximize performance through intelligent work distribution on task-based runtime systems, integrating UniSYCL with the IRIS runtime to enable automatic task splitting and scheduling across diverse accelerators. The core contribution is a set of scheduling algorithms that partition parallel workloads based on device performance characteristics to achieve optimal load balancing. We analyze task parallelizability, extract iteration spaces, and creates device-specific subtasks that execute concurrently across all available accelerators. By adaptively calculating proportional work distributions, the scheduler minimizes overall execution time while maintaining perfect synchronization. Through SYCL’s unified programming model, our expectation is that a single source code will achieve near-linear scalability across heterogeneous devices including NVIDIA and AMD GPUs. This work enables transparent exploitation of heterogeneous parallelism in task-based systems, critical for maximizing computational throughput on modern HPC architectures.<\/td><\/tr>
Heber Rocha, Indiana University<\/td>High Performance Computing<\/td>Human-Interpretable Grammar for Multiscale Agent-Based Models: Building the Foundations for Robust Cancer Patient Digital Twins<\/td>Multiscale agent-based models (ABMs) are powerful tools for simulating complex tumor\u2013immune dynamics, yet their adoption and reproducibility are often hindered by steep technical barriers, inconsistent documentation, and difficulties in sharing model logic. We present a human-interpretable grammar for multicellular systems biology, recently integrated into the PhysiCell framework, that encodes ABM rules and parameters in a transparent, modular, and shareable format. This approach democratizes the creation of virtual laboratories, allowing researchers to build, modify, and reproduce complex simulations without deep programming expertise. Such capabilities are critical for advancing cancer patient digital twins (CPDTs)\u2014computational representations of individual patients aimed at guiding precision oncology. Previous CPDT studies have highlighted persistent challenges, including biological variability, parameter uncertainty, and limitations in patient stratification. By combining interpretable grammars with high-throughput computational strategies for uncertainty quantification and calibration, we outline a pathway toward more robust, collaborative, and clinically relevant CPDT development.<\/td><\/tr>
Jakob Bludau,
Oak Ridge National Laboratory
Postdoc Research Associate<\/td>		High Performance Computing<\/td>Understanding GPU Energy Dynamics in HPC Applications<\/td>Modern graphic processing units have varying power demands for computation and memory operations. Therefore, different implementations of the same algorithm can have different power profiles and consequently energy demand even if they have the same runtime and result for the user. To allow comparison of algorithms leveraging the Kokkos performance portability library, we implemented a new power measurement tool that gives users direct insight into the power and energy profiles of the algorithms they run. This tool allows to optimize runtime and energy consumption simultaneously and can be used portably for different GPUs used in HPC.<\/td><\/tr>
Ethan Puyaubreau,
Paris Saclay University, France
Undergraduate Student<\/td>		High Performance Computing<\/td>Understanding GPU Energy Dynamics in HPC Applications<\/td>Modern graphic processing units have varying power demands for computation and memory operations. Therefore, different implementations of the same algorithm can have different power profiles and consequently energy demand even if they have the same runtime and result for the user. To allow comparison of algorithms leveraging the Kokkos performance portability library, we implemented a new power measurement tool that gives users direct insight into the power and energy profiles of the algorithms they run. This tool allows to optimize runtime and energy consumption simultaneously and can be used portably for different GPUs used in HPC.<\/td><\/tr>
Tatiana Melnichenko,
University of Tennessee, Knoxville
Undergraduate Student<\/td>		High Performance Computing<\/td>Mojo: a Python-like, productive language for GPU-portable scientific high-performance computing<\/td>GPUs power most of the world\u2019s fastest supercomputers and are central to modern AI breakthroughs. However, developing GPU-accelerated code remains challenging for many domain scientists. We evaluate Mojo, a new Python-like programming language, designed to bridge the gap between Python\u2019s high productivity and the performance demands of modern computing. Mojo builds on MLIR, a modern compiler infrastructure adopted by all major vendors, enabling code portability between NVIDIA and AMD GPUs. Our work benchmarks Mojo against vendor-specific C++ implementations (CUDA and HIP) on four representative scientific workloads: a 7-point stencil, BabelStream, Hartree-Fock, and miniBUDE. These span memory-bound, compute-bound, and atomics-heavy kernels. We conducted tests on NVIDIA H100 and AMD MI300A GPUs. Our results show that Mojo delivers GPU-portable performance comparable to vendor-specific C++ code. Although Mojo is still evolving and requires familiarity with some low-level concepts, it shows strong potential for portable high-performance computing.<\/td><\/tr>
Elle Brinkman,
University of Tennessee, Knoxville
Undergraduate Student<\/td>		HPC Theoretical Astrophysics<\/td>Analysis of Core Collapse Supernovae Through Neutrino Signals from Flash-X Simulations<\/td>Core collapse supernovae (CCSN) signal the deaths of massive stars as well as the births of neutron stars and black holes. They are also the source of many important elements in the universe, including elements essential for life. CCSN generate rare transient signals in gravitational waves and neutrinos that we can analyze in order to understand the properties of the star before it exploded. Our project focuses on the neutrino signatures from state of the art Flash-X \/ Thornado simulations. These simulations follow all  six neutrino species: Electron, Muon, Tau neutrinos and their respective anti-neutrinos.  Each of these species have different properties and as a result, provide different information about the supernova. We conducted analysis on the neutrino signals of these six species to understand CCSN phenomena more completely. By learning more about how different neutrino signatures reflect  the properties of the star and its CCSN from simulations, we are able to interpret neutrino signals from CCSN that we detect in the real world more effectively.<\/td><\/tr>
Violet YarKhan,
L&N STEM Academy, Knoxville, TN
High School Student<\/td>		Material Science<\/td>Parallelized Validation of Fluid Simulation <\/td>We found the hidden structure of liquid water by segmenting a virtual box and mapping oxygen to oxygen interactions at the atomic scale. Given the scale of data, the previously used serial methods of verification prove too slow to be helpful for many tasks.<\/td><\/tr>
Khadija Alnaggar,
Farragut High School\/University of Tennessee, Knoxville
Rising Freshman Undergraduate Student<\/td>		Molecular Dynamics, Biological and computational sciences<\/td>Analyzing the Effect of Timestep on Bilipid Membrane Simulations\u200b  \u200b<\/td>We investigated how the choice of molecular dynamics timestep affects key physical properties of a simulated membrane. We constructed a membrane based on 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) using VMD, and simulated the hydrated membrane across timesteps of 0.5, 1.0, 2.0, and 2.5 fs using NAMD. Analysis of membrane thickness and area-per-lipid head-group showed major discrepancies from published data. To rule out glassy dynamics, we analyzed movement of the lipid head-group and confirmed that dynamics was diffusive. We inferred poor initial packing allowed water to infiltrate the bilayer unphysically, causing divergence from published data. We rebuilt the system using a more extensive, multi-step relaxation process. This better preserved membrane structure, producing membrane thickness and area values closer to published results. Importantly, we find statistically significant differences versus the simulation timestep. These findings highlight the importance of simulated membrane properties on packing and timestep used in simulations.<\/td><\/tr>
Salatiel Martinez,
University of Michigan-Dearborn
Undergraduate Student<\/td>		Parallel Computing<\/td>Parallelizing Steps Process in Celeritas<\/td>The current Celeritas implementation for simulating electromagnetic particles on GPUs suffers from a bottleneck due to its serial execution pattern. This serialization of steps leads to slow interaction with Geant4
and significant CPU idle time while waiting for GPU operations to complete. We present a novel paral-
lelization approach using NVIDIA CUDA streams to address these limitations. By decomposing the step
sequence into smaller sufficiently atomic actions and orchestrating their execution across two dedicated streams\u2014one for kernel operations and one for memory copy operations\u2014we achieve significant improvements in throughput and resource utilization. Our prototype demonstrates that steps can be overlapped
across streams while avoiding data dependency conflicts, resulting in an improved step completion time.  The parallelization pattern enables concurrent handling of actions within steps, improving GPU
utilization and reducing CPU idle time, thus providing more efficient execution with Geant4. This work
establishes a foundation for future exploration of multi-stream parallelization in Celeritas.<\/td><\/tr>
Kaiser Ramjee,
Webb School of Knoxville, TN
High School Student<\/td>		Quantum<\/td>Variational Quantum Eigensolver Simulations for Multi-Qubit Ising Models\u200b<\/td>Variational quantum eigensolvers (VQEs) are hybrid quantum-classical algorithms utilizing parameterized quantum circuits and classical optimization cost functions to determine the ground state of a given physical system. In this project, VQEs were used to determine the ground state energy and magnetization of two-qubit and four-qubit quantum transverse field Ising models. The accuracy of the VQE solution was compared to a classically-determined ideal ground state solution. Additionally, we examined the efficiency and efficacy of various ansatz circuits, each with 16 parameters. Efficiency was measured by comparing the average number of iterations before convergence for each type of ansatz. The average percent error between the ansatz and the classical solution was also compared. The Quantum Approximate Optimization Algorithm (QAOA) was determined to be the most efficient ansatz for the two-qubit model. Further investigations seek to study the use of VQEs for more complex spin models with different particle interactions.<\/td><\/tr>
Isaac Lawrie,
High School Student, Knoxville, TN<\/td>		Quantum<\/td>Saving Qubits with Variational Nonorthogonal Quantum Optimization<\/td>Current quantum computers are facing challenges due to their limited number of qubits and substantial gate noise, hindering their ability to solve practical real-world computational problems. To save qubit resources, we study a variational nonorthogonal quantum optimization algorithm and apply to solve integer optimization problems, where p different integer values are encoded in a single qubit. After executing parametrized quantum circuit ansatzes, we decode these integer values by performing simultaneous quantum state tomography on each qubit. Numerical simulations are conducted for p=4 (tetrahedron vertices) and p=20 (dodecahedron vertices), where classical values are encoded at these vertices on the Bloch sphere. Exact solutions are obtained with only a moderate number of variational parameters. Our demonstration shows this a promising algorithm suitable to benchmark ORNL\u2019s first on-premises quantum computer consisting of three 2-qubit, room-temperature, diamond-based QPUs developed by Quantum Brilliance, as well as to explore quantum-HPC hybrid workflows. <\/td><\/tr>
Jax Grzywacz-Jones,
L&N STEM Academy, Knoxville, TN
High School Student<\/td>		Quantum Computing<\/td>Saving Qubits with Variational Nonorthogonal Quantum Optimization<\/td>Current quantum computers are facing challenges due to their limited number of qubits and substantial gate noise, hindering their ability to solve practical real-world computational problems. To save qubit resources, we study a variational nonorthogonal quantum optimization algorithm and apply to solve integer optimization problems, where p different integer values are encoded in a single qubit. After executing parametrized quantum circuit ansatzes, we decode these integer values by performing simultaneous quantum state tomography on each qubit. Numerical simulations are conducted for p=4 (tetrahedron vertices) and p=20 (dodecahedron vertices), where classical values are encoded at these vertices on the Bloch sphere. Exact solutions are obtained with only a moderate number of variational parameters. Our demonstration shows this a promising algorithm suitable to benchmark ORNL\u2019s first on-premises quantum computer consisting of three 2-qubit, room-temperature, diamond-based QPUs developed by Quantum Brilliance, as well as to explore quantum-HPC hybrid workflows. <\/td><\/tr>
Seongmin Kim,
Oak Ridge National Laboratory
Postdoc Research Associate<\/td>		Quantum, HPC, AI<\/td>Accelerating Distributed Quantum Optimization with GPUs on HPC\u2013Quantum Architectures  <\/td>Quantum computing holds promise for efficiently solving complex combinatorial optimization problems across diverse scientific fields. The Distributed Quantum Approximate Optimization Algorithm (DQAOA) has been proposed to address high-dimensional optimization challenges by integrating current quantum computing techniques with high-performance computing (HPC) systems. In this work, we enhance the scalability and performance of DQAOA by incorporating advanced problem decomposition strategies and exploiting multicore and multinode parallelism via the Message Passing Interface (MPI) on the Frontier CPU\/GPU supercomputer. By distributing QUBO problem instances across classical and quantum resources, our framework enables efficient orchestration of quantum-classical workloads and optimal utilization of heterogeneous computing resources. Our results show that GPU-accelerated DQAOA achieves up to a 10\u00d7 speedup over its CPU-only implementation, significantly improving scalability for large problem instances. This study demonstrates the potential of GPU-based HPC systems in advancing quantum-classical hybrid optimization and paves the way for practical deployment of large-scale quantum-enhanced algorithms on emerging HPC\u2013quantum platforms.<\/td><\/tr>
Sheikh Md Mushfiqur Rahman,
Boise State University, Idaho
Graduate Student<\/td>		Software Engineering<\/td>Exploring Sustainability in Scientific Software Through Code Quality Metrics<\/td>Scientific open-source software is essential to research and engineering, yet its long-term sustainability is often overlooked. In this study, we analyze projects from the CORSA catalog, a curated collection of scientific software, to assess sustainability based on multiple indicators of sustained development activity. We classify projects as sustainable or unsustainable, and compare them using structural code metrics and test coverage. Sustainable projects exhibit higher and more consistent test coverage, along with stronger correlations between code structure and test quality. In contrast, unsustainable projects show weaker patterns and lower coverage. Overall, testing remains limited across the dataset, with high complexity and coupling linked to reduced testability. Our work offers a data-driven framework to evaluate scientific software sustainability and support long-term quality assurance.<\/td><\/tr><\/tbody><\/table><\/figure>\n","protected":false},"excerpt":{"rendered":"

Poster Session Chairs Dr. Antigoni Georgiadou, ORNL Dr. Asim YarKhan, ORNL Poster Session Speakers Dr. Jackson Gainer, ORNL Dr. Suzanne Parete-Koon, ORNL Poster Presenters Dr. Philip Dee, ORNL Eugene P. Wigner Distinguished Staff Fellow Poster Title: Accelerating Discoveries in Quantum Materials Using Machine Learning Augmented Many-body Approaches Machine learning is powering a new wave of<\/p>\n","protected":false},"author":44,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":"[]"},"class_list":["post-589","page","type-page","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/events.ornl.gov\/smc2025\/wp-json\/wp\/v2\/pages\/589","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/events.ornl.gov\/smc2025\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/events.ornl.gov\/smc2025\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/events.ornl.gov\/smc2025\/wp-json\/wp\/v2\/users\/44"}],"replies":[{"embeddable":true,"href":"https:\/\/events.ornl.gov\/smc2025\/wp-json\/wp\/v2\/comments?post=589"}],"version-history":[{"count":0,"href":"https:\/\/events.ornl.gov\/smc2025\/wp-json\/wp\/v2\/pages\/589\/revisions"}],"wp:attachment":[{"href":"https:\/\/events.ornl.gov\/smc2025\/wp-json\/wp\/v2\/media?parent=589"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}},{"id":428,"date":"2025-05-14T08:08:49","date_gmt":"2025-05-14T12:08:49","guid":{"rendered":"https:\/\/events.ornl.gov\/smc2025\/?page_id=428"},"modified":"2025-07-18T11:32:25","modified_gmt":"2025-07-18T15:32:25","slug":"thanks","status":"publish","type":"page","link":"https:\/\/events.ornl.gov\/smc2025\/thanks\/","title":{"rendered":"Thank You to our Sponsors!"},"content":{"rendered":"\n

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Thank You to our Corporate Sponsors<\/strong>!<\/h6>\n<\/div><\/div>\n\n\n\n
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Co-Sponsorship with AMD<\/figcaption><\/figure>\n\n\n\n
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Co-Sponsorship with Google Cloud<\/figcaption><\/figure>\n\n\n\n
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Conference Planning Committee<\/strong><\/p>\n<\/div><\/div>\n\n\n\n

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Conference Contact<\/p>\n\n\n\n

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SMC Organizing Committee<\/a><\/div>\n<\/div>\n<\/div><\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":21,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"page__hide_page_title.php","meta":{"_acf_changed":false,"footnotes":""},"class_list":["post-382","page","type-page","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/events.ornl.gov\/smc2025\/wp-json\/wp\/v2\/pages\/382","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/events.ornl.gov\/smc2025\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/events.ornl.gov\/smc2025\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/events.ornl.gov\/smc2025\/wp-json\/wp\/v2\/users\/21"}],"replies":[{"embeddable":true,"href":"https:\/\/events.ornl.gov\/smc2025\/wp-json\/wp\/v2\/comments?post=382"}],"version-history":[{"count":0,"href":"https:\/\/events.ornl.gov\/smc2025\/wp-json\/wp\/v2\/pages\/382\/revisions"}],"wp:attachment":[{"href":"https:\/\/events.ornl.gov\/smc2025\/wp-json\/wp\/v2\/media?parent=382"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}},{"id":377,"date":"2025-05-05T12:06:11","date_gmt":"2025-05-05T16:06:11","guid":{"rendered":"https:\/\/events.ornl.gov\/smc2025\/?page_id=377"},"modified":"2025-08-21T11:57:00","modified_gmt":"2025-08-21T15:57:00","slug":"sessions","status":"publish","type":"page","link":"https:\/\/events.ornl.gov\/smc2025\/sessions\/","title":{"rendered":"Sessions"},"content":{"rendered":"\n
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Session Descriptions and Chairs<\/h1>\n<\/div><\/div>\n\n\n\n
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Session I: Autonomous Laboratories of the Future<\/h3>\n\n\n\n

The “Autonomous Labs of the Future” session explores the transformative potential of AI-driven autonomous laboratories to revolutionize scientific discovery by accelerating research timelines from decades to months. Participants will discuss critical challenges and opportunities in developing interconnected autonomous systems, including cross-institutional equipment integration, intelligent data management, hybrid AI decision-making frameworks that combine machine learning with fundamental scientific principles, and standardized communication protocols that enable seamless collaboration between heterogeneous laboratory environments. The session will also highlight breakthrough applications.<\/h6>\n\n\n\n
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Rafael Ferreira Da Silva, ORNL<\/a><\/h4>\n<\/div><\/div>\n<\/div>\n\n\n\n
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Ben Mintz, ORNL<\/a><\/h4>\n<\/div><\/div>\n\n\n\n
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Session II: Scientific Discovery with Scalable HPC and AI Applications<\/h3>\n\n\n\n

This session explores the convergence of high-performance computing (HPC) and artificial intelligence (AI) to accelerate scientific discovery across disciplines. AI is being applied to challenging problems from biology and materials to physics and imaging, enabling new insight from complex experimental and simulation data. In these applications, scalable algorithms like as reinforcement learning, diffusion models, and large language models are being adapted for scientific contexts. More efficient methods are also required to leverage leadership-class HPC systems to train and deploy these models at scale.<\/h6>\n\n\n\n
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Session III: Advanced Computing for Energy Security<\/h3>\n\n\n\n

As our energy landscape enters a new era of complexity, decentralization, and digital convergence, advanced computing stands at the forefront of securing our energy future. This session will bring together leading experts from national laboratories, academia, government agencies, and industry to explore how next-generation computational capabilities\u2014spanning AI, high-performance computing, and digital twin technologies\u2014are redefining what is possible in energy security. From anticipating grid disruptions and neutralizing cyber-physical threats to enabling intelligent, real-time decisions across resilient, adaptive energy networks, this session will illuminate the transformative power of advanced computing in building a safer, smarter, and more secure energy future.<\/h6>\n\n\n\n
Topics will span high-performance computing (HPC), artificial intelligence and machine learning (AI\/ML), digital twins, physics-informed models, and edge-cloud architectures applied to scenarios such as: – Grid disruption modeling and recovery optimization – Cybersecurity threat detection and autonomous response in critical infrastructure – Energy storage modeling under extreme conditions – Simulations for next-gen nuclear and renewable systems integration – Computational frameworks for cross-border energy infrastructure security <\/h6>\n\n\n\n
The session will showcase how advanced computing is enabling situational awareness, anticipatory defense mechanisms, and operational efficiency across energy domains, with special focus on applications that safeguard national and global energy systems.<\/h6>\n\n\n\n
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Tom Evans, ORNL<\/a><\/h4>\n<\/div><\/div>\n\n\n\n
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Femi Omitaomu, ORNL<\/a><\/h4>\n<\/div><\/div>\n\n\n\n
<\/div>\n\n\n\n

Session IV: Emerging Computational Methods and Algorithms for the Future of Computing<\/h3>\n\n\n\n

This session explores cutting-edge computational methods and algorithmic innovations that are driving the evolution of computing. As systems grow in complexity and scale, traditional approaches face limitations in scalability, adaptability, and energy consumption. This session will showcase the landscape of novel methods and energy-aware algorithms, e.g. mixed-precision, resource allocation, and hybrid architectures\u2014to enable smarter, faster, and more sustainable computing. Emphasis will be placed on emerging strategies that improve scientific discovery via scalable performance and cost-efficient high-performance computing and AI-driven applications.<\/h6>\n\n\n\n
<\/div>\n\n\n\n
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Ana Gainaru, ORNL<\/a><\/h4>\n<\/div><\/div>\n\n\n\n
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William Godoy, ORNL<\/a><\/h4>\n<\/div><\/div>\n\n\n\n
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Elaine Wong, ORNL<\/a><\/h4>\n<\/div><\/div>\n\n\n\n
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Session V: Quantum Computing and its Integration with High Performance Computing and Artificial Intelligence<\/h3>\n\n\n\n

Quantum computing (QC), high performance computing (HPC), and artificial intelligence (AI) have the potential to provide significant advancements to computing capabilities, which can have a profound impact in a number of fields. The integration of QC with HPC and\/or AI is expected to bring further advancements.  For example, QC is expected to provide efficient accelerators for HPC operations and has the potential to lead to enhancements of AI\u2019s capabilities.  On the other hand, HPC can benefit QC applications such as quantum chemistry and optimization, while AI can benefit QC through enabling more robust and efficient quantum computers. In this session, we will explore the interface between QC, HPC, and AI from multiple perspectives, including industry, national laboratories, and academia. The focus of the session will encompass not only the current state of the art, but also the potential roadblocks and strategies for realizing a long-term vision for the integration of these technologies<\/h6>\n\n\n\n
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Alberto Marino, ORNL<\/a><\/h4>\n<\/div><\/div>\n\n\n\n
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Claire Marvinney, ORNL<\/a><\/h4>\n<\/div><\/div>\n\n\n\n
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Session VI: Post-Exascale HPC Technologies: Navigating Stalled Scaling and AI Disruption<\/h3>\n\n\n\n

Steady performance gains through transistor scaling have stalled, and HPC faces a fundamental inflection point. Meanwhile, AI is disrupting everything from microlithography to continental power grids, and its relationship with HPC remains uncertain and multifaceted. Will we continue to see a divergence, where specialized AI chips and architectures pull resources and talent away from traditional HPC, creating competing ecosystems with different optimization targets? This session brings together leading industry experts to explore the critical questions: what are the technological realities that HPC must navigate to continue to drive the future of scientific discovery, and how must HPC architectures evolve in order to adapt to the changing technological and economic landscape?<\/h6>\n\n\n\n
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Jack Lange, ORNL<\/a><\/h4>\n<\/div><\/div>\n\n\n\n
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Trey White, ORNL<\/a><\/h4>\n<\/div><\/div>\n\n\n\n
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Featured Session I: Advancements in Collaborative and Federated Learning<\/h3>\n\n\n\n

The future of scientific discovery lies in the ability to extract insights from vast, distributed, and often sensitive datasets.  Traditional scientific workflows have relied on centralized data aggregation and monolithic AI training pipelines. However, the increasing volume, sensitivity, and distribution of scientific data\u2014from large experimental scattering instruments to satellite sensors to genomic repositories\u2014has created technical, logistical, and ethical barriers to centralization. These challenges are magnified in DOE\u2019s dual mandate: to accelerate open, collaborative science while safeguarding critical infrastructure, classified data, and national security assets. This session will focus on advancements in collaborative and federated learning of scientific data.<\/h6>\n\n\n\n
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Rick Archibald, ORNL<\/a><\/h4>\n<\/div><\/div>\n\n\n\n
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John Gounley, ORNL<\/a><\/h4>\n<\/div><\/div>\n\n\n\n
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Feiyi Wang, ORNL<\/a><\/h4>\n<\/div><\/div>\n\n\n\n
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Featured Session II: Energy Security Panel Discussion<\/h3>\n\n\n\n

This session brings together leaders from government, industry, and national labs to discuss today\u2019s energy security challenges, from supply chains to cyber threats and the role of advanced computing technologies in strengthening critical infrastructure and preparing for future disruptions.<\/h6>\n\n\n\n
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Mason Rice, ORNL<\/a><\/h4>\n\n\n\n

Panel Moderator<\/p>\n<\/div><\/div>\n\n\n\n

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<\/span>
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Conference Contact<\/p>\n\n\n\n

\n
SMC Organizing Committee<\/a><\/div>\n<\/div>\n<\/div><\/div>\n","protected":false},"excerpt":{"rendered":"

Session I: Autonomous Laboratories of the Future The “Autonomous Labs of the Future” session explores the transformative potential of AI-driven autonomous laboratories to revolutionize scientific discovery by accelerating research timelines from decades to months. Participants will discuss critical challenges and opportunities in developing interconnected autonomous systems, including cross-institutional equipment integration, intelligent data management, hybrid AI<\/p>\n","protected":false},"author":21,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"page__hide_page_title.php","meta":{"_acf_changed":false,"footnotes":""},"class_list":["post-377","page","type-page","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/events.ornl.gov\/smc2025\/wp-json\/wp\/v2\/pages\/377","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/events.ornl.gov\/smc2025\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/events.ornl.gov\/smc2025\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/events.ornl.gov\/smc2025\/wp-json\/wp\/v2\/users\/21"}],"replies":[{"embeddable":true,"href":"https:\/\/events.ornl.gov\/smc2025\/wp-json\/wp\/v2\/comments?post=377"}],"version-history":[{"count":0,"href":"https:\/\/events.ornl.gov\/smc2025\/wp-json\/wp\/v2\/pages\/377\/revisions"}],"wp:attachment":[{"href":"https:\/\/events.ornl.gov\/smc2025\/wp-json\/wp\/v2\/media?parent=377"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}},{"id":371,"date":"2025-05-05T12:00:55","date_gmt":"2025-05-05T16:00:55","guid":{"rendered":"https:\/\/events.ornl.gov\/smc2025\/?page_id=371"},"modified":"2025-08-25T07:40:03","modified_gmt":"2025-08-25T11:40:03","slug":"hotel-and-registration","status":"publish","type":"page","link":"https:\/\/events.ornl.gov\/smc2025\/hotel-and-registration\/","title":{"rendered":"Hotel and Registration"},"content":{"rendered":"\n

\"\"<\/span>
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Hotel Reservations<\/strong><\/h2>\n<\/div><\/div>\n\n\n\n
<\/div>\n\n\n\n

Westin Conference Room Wi-Fi<\/strong><\/h4>\n\n\n\n

Log In-<\/strong> Westin_Conference<\/strong><\/p>\n\n\n\n

Password- Westin2018<\/strong><\/p>\n\n\n\n

<\/div>\n\n\n\n

The Westin Chattanooga<\/a><\/h3>\n\n\n\n

801 Pine Street, Chattanooga, TN 37402<\/p>\n\n\n\n

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Booking Deadline: Friday, July 25, 2025<\/strong>.  After this date, room availability, and rates may be subject to change.<\/p>\n\n\n\n

<\/div>\n\n\n\n
<\/span>
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Registration Fee<\/strong><\/h2>\n<\/div><\/div>\n\n\n\n
<\/div>\n\n\n\n
Registration Fee will cover the hotel venue and the following meal expenses:<\/strong><\/h5>\n\n\n\n
<\/div>\n\n\n\n

Monday, August 25, 2025<\/strong><\/p>\n\n\n\n

breakfast, lunch, and dinner<\/em><\/p>\n\n\n\n

Tuesday, August 26, 2025<\/strong><\/p>\n\n\n\n

breakfast and lunch<\/em><\/p>\n\n\n\n

<\/p>\n\n\n\n

Wednesday, August 27, 2025<\/strong><\/p>\n\n\n\n

breakfast and lunch<\/em><\/p>\n\n\n\n

<\/p>\n\n\n\n

Thursday, August 28, 2025<\/strong><\/p>\n\n\n\n

breakfast<\/em> <\/p>\n\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

Westin Conference Room Wi-Fi Log In- Westin_Conference Password- Westin2018 The Westin Chattanooga 801 Pine Street, Chattanooga, TN 37402 Booking Deadline: Friday, July 25, 2025.  After this date, room availability, and rates may be subject to change. Registration Fee will cover the hotel venue and the following meal expenses: Monday, August 25, 2025 breakfast, lunch, and<\/p>\n","protected":false},"author":21,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"page__hide_page_title.php","meta":{"_acf_changed":false,"footnotes":""},"class_list":["post-371","page","type-page","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/events.ornl.gov\/smc2025\/wp-json\/wp\/v2\/pages\/371","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/events.ornl.gov\/smc2025\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/events.ornl.gov\/smc2025\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/events.ornl.gov\/smc2025\/wp-json\/wp\/v2\/users\/21"}],"replies":[{"embeddable":true,"href":"https:\/\/events.ornl.gov\/smc2025\/wp-json\/wp\/v2\/comments?post=371"}],"version-history":[{"count":0,"href":"https:\/\/events.ornl.gov\/smc2025\/wp-json\/wp\/v2\/pages\/371\/revisions"}],"wp:attachment":[{"href":"https:\/\/events.ornl.gov\/smc2025\/wp-json\/wp\/v2\/media?parent=371"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}},{"id":270,"date":"2024-01-04T09:25:11","date_gmt":"2024-01-04T14:25:11","guid":{"rendered":"https:\/\/conferences-image.ornl.local\/?page_id=270"},"modified":"2025-05-22T19:10:54","modified_gmt":"2025-05-22T23:10:54","slug":"contact","status":"publish","type":"page","link":"https:\/\/events.ornl.gov\/smc2025\/contact\/","title":{"rendered":"Contact"},"content":{"rendered":"\n

<\/span>\"\"
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Contact<\/strong><\/p>\n<\/div><\/div>\n\n\n\n

<\/div>\n\n\n