Balancing auditability and privacy in vehicular networks. - Jong Youl Choi; Markus Jakobsson; Susanne Wetzel (Q2SWinet, 2005)

Tamper-Evident Digital Signatures: Protecting Certification Authorities Against Malware. - Jong Youl Choi; Philippe Golle; Markus Jakobsson (IACR Cryptol. ePrint Arch., 2005)

Packet vaccine: black-box exploit detection and signature generation. - XiaoFeng Wang 001; Zhuowei Li 001; Jun Xu 003; Michael K. Reiter; Chongkyung Kil; Jong Youl Choi (CCS, 2006)

Tamper-Evident Digital Signature Protecting Certification Authorities Against Malware. - Jong Youl Choi; Philippe Golle; Markus Jakobsson (DASC, 2006)

Auditable Privacy: On Tamper-Evident Mix Networks. - Jong Youl Choi; Philippe Golle; Markus Jakobsson (Financial Cryptography, 2006)

SpyShield: Preserving Privacy from Spy Add-Ons. - Zhuowei Li 001; XiaoFeng Wang 001; Jong Youl Choi (RAID, 2007)

Social networking for scientists using tagging and shared bookmarks: a Web 2.0 application. - Marlon E. Pierce; Geoffrey C. Fox; Joshua J. Rosen; Siddharth Maini; Jong Youl Choi (CTS, 2008)

SALSA Project: Parallel Data Mining of GIS, Web, Medical, Physics, Chemical, and Biology Data. - Xiaohong Qiu; Geoffrey C. Fox; Seung-Hee Bae; Jong Youl Choi; Jaliya Ekanayake; Yang Ruan (eScience, 2008)

BioVLAB-Microarray: Microarray Data Analysis in Virtual Environment. - Youngik Yang; Jong Youl Choi; Kwangmin Choi; Marlon E. Pierce; Dennis Gannon; Sun Kim (eScience, 2008)

PRECIP: Towards Practical and Retrofittable Confidential Information Protection. - XiaoFeng Wang 001; Zhuowei Li 001; Ninghui Li; Jong Youl Choi (NDSS, 2008)

Fast and Black-box Exploit Detection and Signature Generation for Commodity Software. - XiaoFeng Wang 001; Zhuowei Li 001; Jong Youl Choi; Jun Xu 003; Michael K. Reiter; Chongkyung Kil (ACM Trans. Inf. Syst. Secur., 2008)

Biomedical Case Studies in Data Intensive Computing. - Geoffrey C. Fox; Xiaohong Qiu; Scott Beason; Jong Youl Choi; Jaliya Ekanayake; Thilina Gunarathne; Mina Rho; Haixu Tang; Neil Devadasan; Gilbert C. Liu (CloudCom, 2009)

Using Web 2.0 for scientific applications and scientific communities. - Marlon E. Pierce; Geoffrey C. Fox; Jong Youl Choi; Zhenhua Guo 004; Xiaoming Gao; Yu Ma (Concurr. Comput. Pract. Exp., 2009)

High Performance Dimension Reduction and Visualization for Large High-Dimensional Data Analysis. - Jong Youl Choi; Seung-Hee Bae; Xiaohong Qiu; Geoffrey C. Fox (CCGRID, 2010)

Dimension reduction and visualization of large high-dimensional data via interpolation. - Seung-Hee Bae; Jong Youl Choi; Judy Qiu; Geoffrey C. Fox (HPDC, 2010)

Browsing large scale cheminformatics data with dimension reduction. - Jong Youl Choi; Seung-Hee Bae; Judy Qiu; Geoffrey C. Fox; Bin Chen 002; David J. Wild 001 (HPDC, 2010)

Hybrid cloud and cluster computing paradigms for life science applications. - Judy Qiu; Jaliya Ekanayake; Thilina Gunarathne; Jong Youl Choi; Seung-Hee Bae; Hui Li; Bingjing Zhang; Tak-Lon Wu; Yang Ruan; Saliya Ekanayake; Adam Hughes; Geoffrey C. Fox (BMC Bioinform., 2010)

Generative topographic mapping by deterministic annealing. - Jong Youl Choi; Judy Qiu; Marlon E. Pierce; Geoffrey C. Fox (ICCS, 2010)

Browsing large-scale cheminformatics data with dimension reduction. - Jong Youl Choi; Seung-Hee Bae; Judy Qiu; Bin Chen 002; David J. Wild 001 (Concurr. Comput. Pract. Exp., 2011)

Cloud computing paradigms for pleasingly parallel biomedical applications. - Thilina Gunarathne; Tak-Lon Wu; Jong Youl Choi; Seung-Hee Bae; Judy Qiu (Concurr. Comput. Pract. Exp., 2011)

Mining hidden mixture context with ADIOS-P to improve predictive pre-fetcher accuracy. - Jong Youl Choi; Hasan Abbasi; David Pugmire; Norbert Podhorszki; Scott Klasky; Cristian Capdevila; Manish Parashar; Matthew Wolf; Judy Qiu; Geoffrey C. Fox (eScience, 2012)

ADIOS Visualization Schema: A First Step Towards Improving Interdisciplinary Collaboration in High Performance Computing. - Roselyne Tchoua; Jong Youl Choi; Scott Klasky; Qing Liu 002; Jeremy Logan; Kenneth Moreland; Jingqing Mu; Manish Parashar; Norbert Podhorszki; David Pugmire; Matthew Wolf (e-Science, 2013)

Active workflow system for near real-time extreme-scale science. - Yanwei Zhang; Qing Liu 002; Scott Klasky; Matthew Wolf; Karsten Schwan; Greg Eisenhauer; Jong Youl Choi; Norbert Podhorszki (PPAA@PPoPP, 2014)

Hello ADIOS: the challenges and lessons of developing leadership class I/O frameworks. - Qing Liu 002; Jeremy Logan; Yuan Tian 004; Hasan Abbasi; Norbert Podhorszki; Jong Youl Choi; Scott Klasky; Roselyne Tchoua; Jay F. Lofstead; Ron A. Oldfield; Manish Parashar; Nagiza F. Samatova; et al. (Concurr. Comput. Pract. Exp., 2014)

Loosely Coupled In Situ Visualization: A Perspective on Why It's Here to Stay. - James Kress; Scott Klasky; Norbert Podhorszki; Jong Youl Choi; Hank Childs; David Pugmire (ISAV@SC, 2015)

Combining Phase Identification and Statistic Modeling for Automated Parallel Benchmark Generation. - Ye Jin; Xiaosong Ma; Mingliang Liu; Qing Liu 002; Jeremy Logan; Norbert Podhorszki; Jong Youl Choi; Scott Klasky (SIGMETRICS, 2015)

Towards Real-Time Detection and Tracking of Blob-Filaments in Fusion Plasma Big Data. - Lingfei Wu 001; Kesheng Wu; Alex Sim; Michael Churchill; Jong Youl Choi; Andreas Stathopoulos; Choong-Seock Chang; Scott Klasky (CoRR, 2015)

A synthesized ranking-assisted NSGA-II for interval multi-objective optimization. - Pengfei Zhang; Ruidong Xu; Xiaoyan Sun 002; Dunwei Gong; Yong Zhang 016; Jong Choi 001 (CEC, 2016)

Persistent Data Staging Services for Data Intensive In-situ Scientific Workflows. - Melissa Romanus; Fan Zhang 004; Tong Jin 002; Qian Sun; Hoang Bui; Manish Parashar; Jong Youl Choi; Saloman Janhunen; Robert Hager; Scott Klasky; Choong-Seock Chang; Ivan Rodero (DIDC@HPDC, 2016)

Visualization and Analysis for Near-Real-Time Decision Making in Distributed Workflows. - David Pugmire; James Kress; Jong Youl Choi; Scott Klasky; Tahsin M. Kurç; Michael Churchill; Matthew Wolf; Greg Eisenhauer; Hank Childs; Kesheng Wu; Alexander Sim; Junmin Gu; et al. (IPDPS Workshops, 2016)

Towards Real-Time Detection and Tracking of Spatio-Temporal Features: Blob-Filaments in Fusion Plasma. - Lingfei Wu 001; K. John Wu; Alex Sim; Michael Churchill; Jong Youl Choi; Andreas Stathopoulos; Choong-Seock Chang; Scott Klasky (IEEE Trans. Big Data, 2016)

TGE: Machine Learning Based Task Graph Embedding for Large-Scale Topology Mapping. - Jong Youl Choi; Jeremy Logan; Matthew Wolf; George Ostrouchov; Tahsin M. Kurç; Qing Liu 002; Norbert Podhorszki; Scott Klasky; Melissa Romanus; Qian Sun; Manish Parashar; Randy Michael Churchill; et al. (CLUSTER, 2017)

Extending Skel to Support the Development and Optimization of Next Generation I/O Systems. - Jeremy Logan; Jong Youl Choi; Matthew Wolf; George Ostrouchov; Lipeng Wan 001; Norbert Podhorszki; William F. Godoy; Scott Klasky; Erich Lohrmann; Greg Eisenhauer; Chad Wood; Kevin A. Huck (CLUSTER, 2017)

Canopus: A Paradigm Shift Towards Elastic Extreme-Scale Data Analytics on HPC Storage. - Tao Lu; Eric Suchyta; David Pugmire; Jong Choi 001; Scott Klasky; Qing Liu 002; Norbert Podhorszki; Mark Ainsworth; Matthew Wolf (CLUSTER, 2017)

Computing Just What You Need: Online Data Analysis and Reduction at Extreme Scales. - Ian T. Foster; Mark Ainsworth; Bryce Allen; Julie Bessac; Franck Cappello; Jong Youl Choi; Emil M. Constantinescu; Philip E. Davis; Sheng Di; Zichao Wendy Di; Hanqi Guo 001; Scott Klasky; et al. (Euro-Par, 2017)

Canopus: Enabling Extreme-Scale Data Analytics on Big HPC Storage via Progressive Refactoring. - Tao Lu; Eric Suchyta; Jong Youl Choi; Norbert Podhorszki; Scott Klasky; Qing Liu 002; David Pugmire; Matthew Wolf; Mark Ainsworth (HotStorage, 2017)

Analysis and Modeling of the End-to-End I/O Performance on OLCF's Titan Supercomputer. - Lipeng Wan 001; Matthew Wolf; Feiyi Wang; Jong Youl Choi; George Ostrouchov; Scott Klasky (HPCC/SmartCity/DSS, 2017)

Predicting Output Performance of a Petascale Supercomputer. - Bing Xie; Yezhou Huang; Jeffrey S. Chase; Jong Youl Choi; Scott Klasky; Jay F. Lofstead; Sarp Oral (HPDC, 2017)

StoreRush: An Application-Level Approach to Harvesting Idle Storage in a Best Effort Environment. - Qing Liu 002; Norbert Podhorszki; Jong Youl Choi; Jeremy Logan; Matthew Wolf; Scott Klasky; Tahsin M. Kurç; Xubin He (ICCS, 2017)

Exacution: Enhancing Scientific Data Management for Exascale. - Scott Klasky; Eric Suchyta; Mark Ainsworth; Qing Liu 002; Ben Whitney; Matthew Wolf; Jong Youl Choi; Ian T. Foster; Mark Kim; Jeremy Logan; Kshitij Mehta; Todd S. Munson; et al. (ICDCS, 2017)

Comprehensive Measurement and Analysis of the User-Perceived I/O Performance in a Production Leadership-Class Storage System. - Lipeng Wan 001; Matthew Wolf; Feiyi Wang; Jong Youl Choi; George Ostrouchov; Scott Klasky (ICDCS, 2017)

On the Scalability of Data Reduction Techniques in Current and Upcoming HPC Systems from an Application Perspective. - Axel Huebl; René Widera; Felix Schmitt 004; Alexander Matthes; Norbert Podhorszki; Jong Youl Choi; Scott Klasky; Michael Bussmann (ISC Workshops, 2017)

Personalized Search Inspired Fast Interactive Estimation of Distribution Algorithm and Its Application. - Yang Chen 007; Xiaoyan Sun 002; Dunwei Gong; Yong Zhang 016; Jong Choi 001; Scott Klasky (IEEE Trans. Evol. Comput., 2017)

Coupling Exascale Multiphysics Applications: Methods and Lessons Learned. - Jong Youl Choi; Choong-Seock Chang; Julien Dominski; Scott Klasky; Gabriele Merlo; Eric Suchyta; Mark Ainsworth; Bryce Allen; Franck Cappello; Michael Churchill; Philip E. Davis; Sheng Di; et al. (eScience, 2018)

A View from ORNL: Scientific Data Research Opportunities in the Big Data Age. - Scott Klasky; Matthew Wolf; Mark Ainsworth; Chuck Atkins; Jong Choi 001; Greg Eisenhauer; Berk Geveci; William F. Godoy; Mark Kim; James Kress; Tahsin M. Kurç; Qing Liu 002; et al. (ICDCS, 2018)

Understanding and Modeling Lossy Compression Schemes on HPC Scientific Data. - Tao Lu; Qing Liu 002; Xubin He; Huizhang Luo; Eric Suchyta; Jong Choi 001; Norbert Podhorszki; Scott Klasky; Matthew Wolf; Tong Liu; Zhenbo Qiao (IPDPS, 2018)

In Situ Analysis and Visualization of Fusion Simulations: Lessons Learned. - Mark Kim; James Kress; Jong Choi 001; Norbert Podhorszki; Scott Klasky; Matthew Wolf; Kshitij Mehta; Kevin A. Huck; Berk Geveci; Sujin Phillip; Robert Maynard; Hanqi Guo 001; et al. (ISC Workshops, 2018)

Binning Based Data Reduction for Vector Field Data of a Particle-In-Cell Fusion Simulation. - James Kress; Jong Choi 001; Scott Klasky; Michael Churchill; Hank Childs; David Pugmire (ISC Workshops, 2018)

Scalable Performance Awareness for In Situ Scientific Applications. - Matthew Wolf; Julien Dominski; Gabriele Merlo; Jong Choi 001; Greg Eisenhauer; Stéphane Ethier; Kevin A. Huck; Scott Klasky; Jeremy Logan; Allen D. Malony; Chad Wood (eScience, 2019)

A Codesign Framework for Online Data Analysis and Reduction. - Kshitij Mehta; Ian T. Foster; Scott Klasky; Bryce Allen; Matthew Wolf; Jeremy Logan; Eric Suchyta; Jong Choi 001; Keichi Takahashi; Igor Yakushin; Todd S. Munson (WORKS@SC, 2019)

Comparing the Efficiency of In Situ Visualization Paradigms at Scale. - James Kress; Matthew Larsen; Jong Choi 001; Mark Kim; Matthew Wolf; Norbert Podhorszki; Scott Klasky; Hank Childs; David Pugmire (ISC, 2019)

Can I/O Variability Be Reduced on QoS-Less HPC Storage Systems? - Dan Huang 001; Qing Liu 002; Jong Choi 001; Norbert Podhorszki; Scott Klasky; Jeremy Logan; George Ostrouchov; Xubin He; Matthew Wolf (IEEE Trans. Computers, 2019)

Harnessing Data Movement in Virtual Clusters for In-Situ Execution. - Dan Huang 001; Qing Liu 002; Scott Klasky; Jun Wang 001; Jong Youl Choi; Jeremy Logan; Norbert Podhorszki (IEEE Trans. Parallel Distributed Syst., 2019)

Comparing Time-to-Solution for In Situ Visualization Paradigms at Scale. - James Kress; Matthew Larsen; Jong Choi 001; Mark Kim; Matthew Wolf; Norbert Podhorszki; Scott Klasky; Hank Childs; David Pugmire (LDAV, 2020)

Near real-time analysis of big fusion data on HPC systems. - Ralph Kube; Randy Michael Churchill; Jong Choi 001; Ruonan Wang; Minjun Choi; Scott Klasky; Choong-Seock Chang (UrgentHPC@SC, 2020)

Leading magnetic fusion energy science into the big-and-fast data lane. - Ralph Kube; Randy Michael Churchill; Jong Youl Choi; Ruonan Wang; Scott Klasky; CS Chang; Minjun J. Choi; Jinseop Park (SciPy, 2020)

Data Federation Challenges in Remote Near-Real-Time Fusion Experiment Data Processing. - Jong Choi 001; Ruonan Wang; Randy Michael Churchill; Ralph Kube; Minjun Choi; Jinseop Park; Jeremy Logan; Kshitij Mehta; Greg Eisenhauer; Norbert Podhorszki; Matthew Wolf; Choong-Seock Chang; et al. (SMC, 2020)

Machine Learning for the Complex, Multi-scale Datasets in Fusion Energy. - Randy Michael Churchill; Jong Choi 001; Ralph Kube; Choong-Seock Chang; Scott Klasky (SMC, 2020)

Visualization as a Service for Scientific Data. - David Pugmire; James Kress; Jieyang Chen; Hank Childs; Jong Choi 001; Dmitry Ganyushin; Berk Geveci; Mark Kim; Scott Klasky; Xin Liang 001; Jeremy Logan; Nicole Marsaglia; et al. (SMC, 2020)

Opportunities for Cost Savings with In-Transit Visualization. - James Kress; Matthew Larsen; Jong Choi 001; Mark Kim; Matthew Wolf; Norbert Podhorszki; Scott Klasky; Hank Childs; David Pugmire (ISC, 2020)

Chimbuko: A Workflow-Level Scalable Performance Trace Analysis Tool. - Sungsoo Ha; Wonyong Jeong; Gyorgy Matyasfalvi; Cong Xie; Kevin A. Huck; Jong Youl Choi; Abid Malik; Li Tang 007; Hubertus Van Dam; Line Pouchard; Wei Xu 020; Shinjae Yoo; et al. (CoRR, 2020)

Extending the Publish/Subscribe Abstraction for High-Performance I/O and Data Management at Extreme Scale. - Jeremy Logan; Mark Ainsworth; Chuck Atkins; Jieyang Chen; Jong Choi 001; Junmin Gu; James Kress; Greg Eisenhauer; Berk Geveci; William F. Godoy; Mark Kim; Tahsin M. Kurç; et al. (IEEE Data Eng. Bull., 2020)

ADIOS 2: The Adaptable Input Output System. A framework for high-performance data management. - William F. Godoy; Norbert Podhorszki; Ruonan Wang; Chuck Atkins; Greg Eisenhauer; Junmin Gu; Philip E. Davis; Jong Choi 001; Kai Germaschewski; Kevin A. Huck; Axel Huebl; Mark Kim; et al. (SoftwareX, 2020)

Characterizing Output Bottlenecks of a Production Supercomputer: Analysis and Implications. - Bing Xie; Sarp Oral; Christopher Zimmer 001; Jong Youl Choi; David Dillow; Scott Klasky; Jay F. Lofstead; Norbert Podhorszki; Jeffrey S. Chase (ACM Trans. Storage, 2020)

DYFLOW: A flexible framework for orchestrating scientific workflows on supercomputers. - Swati Singhal; Alan Sussman; Matthew Wolf; Kshitij Mehta; Jong Youl Choi (ICPP Workshops, 2021)

Accelerating Multigrid-based Hierarchical Scientific Data Refactoring on GPUs. - Jieyang Chen; Lipeng Wan 001; Xin Liang 001; Ben Whitney; Qing Liu 002; David Pugmire; Nicholas Thompson; Jong Youl Choi; Matthew Wolf; Todd S. Munson; Ian T. Foster; Scott Klasky (IPDPS, 2021)

Maintaining Trust in Reduction: Preserving the Accuracy of Quantities of Interest for Lossy Compression. - Qian Gong; Xin Liang 001; Ben Whitney; Jong Youl Choi; Jieyang Chen; Lipeng Wan 001; Stéphane Ethier; Seung-Hoe Ku; Randy Michael Churchill; Choong-Seock Chang; Mark Ainsworth; Ozan Tugluk; et al. (SMC, 2021)

Scalable Multigrid-based Hierarchical Scientific Data Refactoring on GPUs. - Jieyang Chen; Lipeng Wan 001; Xin Liang 001; Ben Whitney; Qing Liu 002; Qian Gong; David Pugmire; Nicholas Thompson; Jong Youl Choi; Matthew Wolf; Todd S. Munson; Ian T. Foster; et al. (CoRR, 2021)

Co-design Center for Exascale Machine Learning Technologies (ExaLearn). - Francis J. Alexander; James A. Ang; Jenna A. Bilbrey; Jan Balewski; Tiernan Casey; Ryan Chard; Jong Choi 001; Sutanay Choudhury; Bert J. Debusschere; Anthony M. DeGennaro; Nikoli Dryden; J. Austin Ellis; et al. (Int. J. High Perform. Comput. Appl., 2021)

Online data analysis and reduction: An important Co-design motif for extreme-scale computers. - Ian T. Foster; Mark Ainsworth; Julie Bessac; Franck Cappello; Jong Choi 001; Sheng Di; Zichao Wendy Di; Ali Murat Gok; Hanqi Guo 001; Kevin A. Huck; Christopher Kelly; Scott Klasky; et al. (Int. J. High Perform. Comput. Appl., 2021)

Hybrid Analysis of Fusion Data for Online Understanding of Complex Science on Extreme Scale Computers. - Eric Suchyta; Jong Youl Choi; Seung-Hoe Ku; David Pugmire; Ana Gainaru; Kevin A. Huck; Ralph Kube; Aaron Scheinberg; Frédéric Suter; Choong-Seock Chang; Todd S. Munson; Norbert Podhorszki; et al. (CLUSTER, 2022)

An Algorithmic and Software Pipeline for Very Large Scale Scientific Data Compression with Error Guarantees. - Tania Banerjee; Jong Choi 001; Jaemoon Lee; Qian Gong; Ruonan Wang; Scott Klasky; Anand Rangarajan 001; Sanjay Ranka (HIPC, 2022)

Machine Learning Assisted HPC Workload Trace Generation for Leadership Scale Storage Systems. - Arnab K. Paul; Jong Youl Choi; Ahmad Maroof Karimi; Feiyi Wang (HPDC, 2022)

A codesign framework for online data analysis and reduction. - Kshitij Mehta; Bryce Allen; Matthew Wolf; Jeremy Logan; Eric Suchyta; Swati Singhal; Jong Youl Choi; Keichi Takahashi; Kevin A. Huck; Igor Yakushin; Alan Sussman; Todd S. Munson; et al. (Concurr. Comput. Pract. Exp., 2022)

Multi-task graph neural networks for simultaneous prediction of global and atomic properties in ferromagnetic systems. - Massimiliano Lupo Pasini; Pei Zhang; Samuel Temple Reeve; Jong Youl Choi (CoRR, 2022)

Scalable Hybrid Learning Techniques for Scientific Data Compression. - Tania Banerjee; Jong Choi 001; Jaemoon Lee; Qian Gong; Jieyang Chen; Scott Klasky; Anand Rangarajan 001; Sanjay Ranka (CoRR, 2022)

The Exascale Framework for High Fidelity coupled Simulations (EFFIS): Enabling whole device modeling in fusion science. - Eric Suchyta; Scott Klasky; Norbert Podhorszki; Matthew Wolf; Abolaji D. Adesoji; Choong-Seock Chang; Jong Choi 001; Philip E. Davis; Julien Dominski; Stéphane Ethier; Ian T. Foster; Kai Germaschewski; et al. (Int. J. High Perform. Comput. Appl., 2022)

Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules. - Jong Youl Choi; Pei Zhang; Kshitij Mehta; Andrew E. Blanchard; Massimiliano Lupo Pasini (J. Cheminformatics, 2022)

Multi-task graph neural networks for simultaneous prediction of global and atomic properties in ferromagnetic systems *. - Massimiliano Lupo Pasini; Pei Zhang; Samuel Temple Reeve; Jong Youl Choi (Mach. Learn. Sci. Technol., 2022)

Online and Scalable Data Compression Pipeline with Guarantees on Quantities of Interest. - Tania Banerjee; Jaemoon Lee; Jong Choi 001; Qian Gong; Jieyang Chen; Choong-Seock Chang; Scott Klasky; Anand Rangarajan 001; Sanjay Ranka (e-Science, 2023)

Fast Algorithms for Scientific Data Compression. - Tania Banerjee; Jaemoon Lee; Jong Choi 001; Qian Gong; Jieyang Chen; Scott Klasky; Anand Rangarajan 001; Sanjay Ranka (HiPC, 2023)

Predicting Power Outage During Extreme Weather Events with EAGLE-I and NWS Datasets. - Sangkeun Lee 002; Jong Choi 001; Gs Jung; Anika Tabassum; Nils M. Stenvig; Supriya Chinthavali (IRI, 2023)

Analyzing File Access Patterns on Large-Scale HPC Systems: Opportunities for File Prefetching. - Ahmad Maroof Karimi; Arnab K. Paul; Jong Youl Choi; Lipeng Wan 001; Feiyi Wang (MASCOTS, 2023)

DDStore: Distributed Data Store for Scalable Training of Graph Neural Networks on Large Atomistic Modeling Datasets. - Jong Youl Choi; Massimiliano Lupo Pasini; Pei Zhang; Kshitij Mehta; Frank Liu 001; Jonghyun Bae; Khaled Ibrahim (SC Workshops, 2023)

Unraveling Diffusion in Fusion Plasma: A Case Study of In Situ Processing and Particle Sorting. - Junmin Gu; Paul Lin; Kesheng Wu; Seung-Hoe Ku; Choong-Seock Chang; Randy Michael Churchill; Jong Choi 001; Norbert Podhorszki; Scott Klasky (CoRR, 2023)

Data Distillation for Neural Network Potentials toward Foundational Dataset. - Gang Seob Jung; Sangkeun Lee 001; Jong Youl Choi (CoRR, 2023)

MDLoader: A Hybrid Model-driven Data Loader for Distributed Deep Neural Networks Training. - Jonghyun Bae; Jong Youl Choi; Massimiliano Lupo Pasini; Kshitij Mehta; Khaled Z. Ibrahim (IPDPS (Workshops), 2024)

MDLoader: A Hybrid Model-Driven Data Loader for Distributed Graph Neural Network Training. - Jonghyun Bae; Jong Youl Choi; Massimiliano Lupo Pasini; Kshitij Mehta; Pei Zhang; Khaled Z. Ibrahim (SC Workshops, 2024)

Performance Improvements of Poincaré Analysis for Exascale Fusion Simulations. - David Pugmire; Jong Youl Choi; Scott Klasky; Kenneth Moreland; Eric Suchyta; Tushar M. Athawale; Zhe Wang 059; Choong-Seock Chang; Seung-Hoe Ku; Robert Hager (VisGap@EuroVis, 2024)

Scalable Training of Graph Foundation Models for Atomistic Materials Modeling: A Case Study with HydraGNN. - Massimiliano Lupo Pasini; Jong Youl Choi; Kshitij Mehta; Pei Zhang; David M. Rogers 001; Jonghyun Bae; Khaled Z. Ibrahim; Ashwin M. Aji; Karl W. Schulz; Jorda Polo; Prasanna Balaprakash (CoRR, 2024)

How does ion temperature gradient turbulence depend on magnetic geometry? Insights from data and machine learning. - Matt Landreman; Jong Youl Choi; Caio Alves; Prasanna Balaprakash; Randy Michael Churchill; Rory Conlin; Gareth Roberg-Clark (CoRR, 2025)

Scalable training of trustworthy and energy-efficient predictive graph foundation models for atomistic materials modeling: a case study with HydraGNN. - Massimiliano Lupo Pasini; Jong Youl Choi; Kshitij Mehta; Pei Zhang; David M. Rogers 001; Jonghyun Bae; Khaled Z. Ibrahim; Ashwin M. Aji; Karl W. Schulz; Jordà Polo; Prasanna Balaprakash (J. Supercomput., 2025)

Scaling Laws of Graph Neural Networks for Atomistic Materials Modeling. - Chaojian Li; Zhifan Ye; Massimiliano Lupo Pasini; Jong Youl Choi; Cheng Wan 005; Yingyan Celine Lin; Prasanna Balaprakash (CoRR, 2025)

Exploring the Capabilities of the Frontier Large Language Models for Nuclear Energy Research. - Ahmed Almeldein; Mohammed Alnaggar; Rick Archibald; Tom Beck; Arpan Biswas; Rike Bostelmann; Wes Brewer; Chris Bryan; Christopher Calle; Cihangir Celik; Rajni Chahal; Jong Youl Choi; et al. (CoRR, 2025)

Multi-task parallelism for robust pre-training of graph foundation models on multi-source, multi-fidelity atomistic modeling data. - Massimiliano Lupo Pasini; Jong Youl Choi; Pei Zhang; Kshitij Mehta; Rylie Weaver; Ashwin M. Aji; Karl W. Schulz; Jorda Polo; Prasanna Balaprakash (CoRR, 2025)

Intelligent Sampling of Extreme-Scale Turbulence Datasets for Accurate and Efficient Spatiotemporal Model Training. - Wesley Brewer; Muralikrishnan Gopalakrishnan Meena; Matthias Maiterth; Aditya Kashi; Jong Youl Choi; Pei Zhang; Stephen Nichols; Riccardo Balin; Miles Couchman; Stephen M. de Bruyn Kops; P. K. Yeung; Daniel Dotson; et al. (CoRR, 2025)

Lustre Unveiled: Evolution, Design, Advancements, and Current Trends. - Anjus George; Andreas Dilger; Michael J. Brim; Rick Mohr; Amir Shehata; Jong Youl Choi; Ahmad Maroof Karimi; Jesse Hanley; James Simmons; Dominic Manno; Verónica Melesse Vergara; Sarp Oral; et al. (ACM Trans. Storage, 2025)