Eliu A. Huerta
Orcid: 0000-0002-9682-3604Affiliations:
- University of Chicago, IL, USA
According to our database1,
Eliu A. Huerta
authored at least 46 papers
between 2017 and 2024.
Collaborative distances:
Collaborative distances:
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on orcid.org
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Bibliography
2024
Secure Federated Learning Across Heterogeneous Cloud and High-Performance Computing Resources - A Case Study on Federated Fine-tuning of LLaMA 2.
CoRR, 2024
FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices Using a Computing Power-Aware Scheduler.
Proceedings of the Twelfth International Conference on Learning Representations, 2024
Proceedings of the 20th IEEE International Conference on e-Science, 2024
2023
Mach. Learn. Sci. Technol., September, 2023
Enabling End-to-End Secure Federated Learning in Biomedical Research on Heterogeneous Computing Environments with APPFLx.
CoRR, 2023
AI ensemble for signal detection of higher order gravitational wave modes of quasi-circular, spinning, non-precessing binary black hole mergers.
CoRR, 2023
APACE: AlphaFold2 and advanced computing as a service for accelerated discovery in biophysics.
CoRR, 2023
CoRR, 2023
GHP-MOFassemble: Diffusion modeling, high throughput screening, and molecular dynamics for rational discovery of novel metal-organic frameworks for carbon capture at scale.
CoRR, 2023
Proceedings of the 19th IEEE International Conference on e-Science, 2023
2022
Mach. Learn. Sci. Technol., 2022
Inference-Optimized AI and High Performance Computing for Gravitational Wave Detection at Scale.
Frontiers Artif. Intell., 2022
End-to-end AI Framework for Hyperparameter Optimization, Model Training, and Interpretable Inference for Molecules and Crystals.
CoRR, 2022
FAIR for AI: An interdisciplinary, international, inclusive, and diverse community building perspective.
CoRR, 2022
CoRR, 2022
FAIR principles for AI models, with a practical application for accelerated high energy diffraction microscopy.
CoRR, 2022
2021
AI and extreme scale computing to learn and infer the physics of higher order gravitational wave modes of quasi-circular, spinning, non-precessing binary black hole mergers.
CoRR, 2021
Interpretable AI forecasting for numerical relativity waveforms of quasi-circular, spinning, non-precessing binary black hole mergers.
CoRR, 2021
Advances in Machine and Deep Learning for Modeling and Real-time Detection of Multi-Messenger Sources.
CoRR, 2021
2020
Convergence of artificial intelligence and high performance computing on NSF-supported cyberinfrastructure.
J. Big Data, 2020
Confluence of Artificial Intelligence and High Performance Computing for Accelerated, Scalable and Reproducible Gravitational Wave Detection.
CoRR, 2020
Physics-inspired deep learning to characterize the signal manifold of quasi-circular, spinning, non-precessing binary black hole mergers.
CoRR, 2020
2019
Comput. Softw. Big Sci., December, 2019
Deep Learning for Cardiologist-level Myocardial Infarction Detection in Electrocardiograms.
CoRR, 2019
CoRR, 2019
Deep Learning at Scale for Gravitational Wave Parameter Estimation of Binary Black Hole Mergers.
CoRR, 2019
Deep Learning for Multi-Messenger Astrophysics: A Gateway for Discovery in the Big Data Era.
CoRR, 2019
The Physics of Eccentric Binary Black Hole Mergers. A Numerical Relativity Perspective.
CoRR, 2019
Proceedings of the IEEE International Conference on Acoustics, 2019
2018
Unsupervised learning and data clustering for the construction of Galaxy Catalogs in the Dark Energy Survey.
CoRR, 2018
Proceedings of the Practice and Experience on Advanced Research Computing, 2018
2017
Denoising Gravitational Waves using Deep Learning with Recurrent Denoising Autoencoders.
CoRR, 2017
Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation with Advanced LIGO Data.
CoRR, 2017
CoRR, 2017
ENIGMA: Eccentric, Non-spinning, Inspiral Gaussian-process Merger Approximant for the characterization of eccentric binary black hole mergers.
CoRR, 2017
Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation: Results with Advanced LIGO Data.
CoRR, 2017
Python Open Source Waveform Extractor (POWER): An open source, Python package to monitor and post-process numerical relativity simulations.
CoRR, 2017
Deep Transfer Learning: A new deep learning glitch classification method for advanced LIGO.
CoRR, 2017
BOSS-LDG: A Novel Computational Framework That Brings Together Blue Waters, Open Science Grid, Shifter and the LIGO Data Grid to Accelerate Gravitational Wave Discovery.
Proceedings of the 13th IEEE International Conference on e-Science, 2017