Publications

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[1] G. Fox, P. Beckman, S. Jha, P. Luszczek, and V. Jadhao, “Surrogate benchmark initiative SBI: FAIR surrogate benchmarks supporting AI and simulation research,” in ASCR computer science (CS) principal investigators (PI) meeting, Atlanta, GA: U.S. Department of Energy (DOE), Office of Science (SC), Feb. 2024, p. 1. Available: https://github.com/sbi-fair/sbi-fair.github.io/raw/main/pub/doe_abstract.pdf

[2] T. Zhong, J. Zhao, X. Guo, Q. Su, and G. Fox, “RINAS: Training with dataset shuffling can be general and fast.” 2023. Available: https://arxiv.org/abs/2312.02368

[3] C. Luo, T. Zhong, and G. Fox, “RTP: Rethinking tensor parallelism with memory deduplication.” 2023. Available: https://arxiv.org/abs/2311.01635

[4] “Quadri-partite quantum-assisted VAE as a calorimeter surrogate,” in Bulletin of the american physical society, in APS march meeting. American Physical Society Sites. Available: https://meetings.aps.org/Meeting/MAR24/Session/Y50.5

[5] J. Q. Toledo-Marín, G. Fox, J. P. Sluka, and J. A. Glazier, “Deep learning approaches to surrogates for solving the diffusion equation for mechanistic real-world simulations.” 2021. Available: https://arxiv.org/abs/2102.05527

[6] J. Q. Toledo-Marín, G. Fox, J. P. Sluka, and J. A. Glazier, “Deep learning approaches to surrogates for solving the diffusion equation for mechanistic real-world simulations,” Frontiers in Physiology, vol. 12, 2021, doi: 10.3389/fphys.2021.667828.

[7] J. Kadupitiya, F. Sun, G. Fox, and V. Jadhao, “Machine learning surrogates for molecular dynamics simulations of soft materials,” Journal of Computational Science, vol. 42, p. 101107, 2020, Available: https://par.nsf.gov/servlets/purl/10188151

[8] V. Jadhao and J. Kadupitiya, “Integrating machine learning with hpc-driven simulations for enhanced student learning,” in 2020 IEEE/ACM workshop on education for high-performance computing (EduHPC), IEEE, 2020, pp. 25–34. Available: https://api.semanticscholar.org/CorpusID:221376417

[9] A. Clyde et al., “Protein-ligand docking surrogate models: A SARS-CoV-2 benchmark for deep learning accelerated virtual screening.” 2021. Available: https://arxiv.org/abs/2106.07036

[10] E. A. Huerta et al., “FAIR for AI: An interdisciplinary and international community building perspective,” Scientific Data, vol. 10, no. 1, p. 487, 2023, Available: https://doi.org/10.1038/s41597-023-02298-6

[11] G. von Laszewski, J. P. Fleischer, and G. C. Fox, “Hybrid reusable computational analytics workflow management with cloudmesh.” 2022. Available: https://arxiv.org/abs/2210.16941

[12] V. Chennamsetti et al., “MLCommons cloud masking benchmark with early stopping.” 2023. Available: https://arxiv.org/abs/2401.08636

[13] G. von Laszewski and R. Gu, “An overview of MLCommons cloud mask benchmark: Related research and data.” 2023. Available: https://arxiv.org/abs/2312.04799

[14] G. von Laszewski et al., “Whitepaper on reusable hybrid and multi-cloud analytics service framework.” 2023. Available: https://arxiv.org/abs/2310.17013

[15] G. von Laszewski, J. P. Fleischer, G. C. Fox, J. Papay, S. Jackson, and J. Thiyagalingam, “Templated hybrid reusable computational analytics workflow management with cloudmesh, applied to the deep learning MLCommons cloudmask application,” in eScience’23, Limassol, Cyprus: Second Workshop on Reproducible Workflows, Data,; Security (ReWorDS 2022), 2023. Available: https://github.com/cyberaide/paper-cloudmesh-cc-ieee-5-pages/raw/main/vonLaszewski-cloudmesh-cc.pdf

[16] G. von Laszewski et al., “Opportunities for enhancing MLCommons efforts while leveraging insights from educational MLCommons earthquake benchmarks efforts,” Frontiers in High Performance Computing, vol. 1, no. 1233877, p. 31, 2023, Available: https://doi.org/10.3389/fhpcp.2023.1233877

[17] G. von Laszewski, “Cloudmesh.” Web Page, Jan. 2024. Available: https://github.com/orgs/cloudmesh/repositories

[18] G. von Laszewski, “Reusable hybrid and multi-cloud analytics service framework,” in 4th international conference on big data, IoT, and cloud computing (ICBICC 2022), Chengdu, China: IASED, 2022. Available: www.icbicc.org