b'E. A. Huerta'

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Featured Co-authors

Xin Liu
216 publications
Wei Wei
156 publications
Jinjun Xiong
93 publications
Ian Foster
68 publications
Shantenu Jha
51 publications
Kyle Chard
46 publications
Daniel S Katz
44 publications
Zhizhen Zhao
37 publications
Javier Duarte
23 publications
Ryan Chard
23 publications
Geoffrey Fox
21 publications

research

∙ 08/17/2023

APPFLx: Providing Privacy-Preserving Cross-Silo Federated Learning as a Service

Cross-silo privacy-preserving federated learning (PPFL) is a powerful to...

0 Zilinghan Li, et al. ∙

research

∙ 08/15/2023

APACE: AlphaFold2 and advanced computing as a service for accelerated discovery in biophysics

The prediction of protein 3D structure from amino acid sequence is a com...

0 Hyun Park, et al. ∙

research

∙ 06/27/2023

Physics-inspired spatiotemporal-graph AI ensemble for gravitational wave detection

We introduce a novel method for gravitational wave detection that combin...

0 Minyang Tian, et al. ∙

research

∙ 06/14/2023

GHP-MOFassemble: Diffusion modeling, high throughput screening, and molecular dynamics for rational discovery of novel metal-organic frameworks for carbon capture at scale

We introduce GHP-MOFassemble, a Generative artificial intelligence (AI),...

0 Hyun Park, et al. ∙

research

∙ 02/13/2023

Magnetohydrodynamics with Physics Informed Neural Operators

We present the first application of physics informed neural operators, w...

0 Shawn G. Rosofsky, et al. ∙

research

∙ 12/21/2022

End-to-end AI Framework for Hyperparameter Optimization, Model Training, and Interpretable Inference for Molecules and Crystals

We introduce an end-to-end computational framework that enables hyperpar...

0 Hyun Park, et al. ∙

research

∙ 12/09/2022

FAIR AI Models in High Energy Physics

The findable, accessible, interoperable, and reusable (FAIR) data princi...

0 Javier Duarte, et al. ∙

research

∙ 09/30/2022

FAIR for AI: An interdisciplinary, international, inclusive, and diverse community building perspective

A foundational set of findable, accessible, interoperable, and reusable ...

0 E. A. Huerta, et al. ∙

research

∙ 07/01/2022

FAIR principles for AI models, with a practical application for accelerated high energy diffraction microscopy

A concise and measurable set of FAIR (Findable, Accessible, Interoperabl...

19 Nikil Ravi, et al. ∙

research

∙ 03/23/2022

Applications of physics informed neural operators

We present an end-to-end framework to learn partial differential equatio...

5 Shawn G. Rosofsky, et al. ∙

research

∙ 05/13/2021

Advances in Machine and Deep Learning for Modeling and Real-time Detection of Multi-Messenger Sources

We live in momentous times. The science community is empowered with an a...

0 E. A. Huerta, et al. ∙

research

∙ 04/20/2020

Physics-inspired deep learning to characterize the signal manifold of quasi-circular, spinning, non-precessing binary black hole mergers

The spin distribution of binary black hole mergers contains key informat...

5 Asad Khan, et al. ∙

research

∙ 03/18/2020

Convergence of Artificial Intelligence and High Performance Computing on NSF-supported Cyberinfrastructure

Significant investments to upgrade or construct large-scale scientific f...

0 E. A. Huerta, et al. ∙

research

∙ 12/16/2019

Deep Learning for Cardiologist-level Myocardial Infarction Detection in Electrocardiograms

Heart disease is the leading cause of death worldwide. Amongst patients ...

33 Arjun Gupta, et al. ∙

research

∙ 11/26/2019

Enabling real-time multi-messenger astrophysics discoveries with deep learning

Multi-messenger astrophysics is a fast-growing, interdisciplinary field ...

0 E. A. Huerta, et al. ∙

research

∙ 03/06/2019

Denoising Gravitational Waves with Enhanced Deep Recurrent Denoising Auto-Encoders

Denoising of time domain data is a crucial task for many applications su...

0 Hongyu Shen, et al. ∙

research

∙ 03/05/2019

Deep Learning at Scale for Gravitational Wave Parameter Estimation of Binary Black Hole Mergers

We present the first application of deep learning at scale to do gravita...

12 Hongyu Shen, et al. ∙

research

∙ 02/01/2019

Deep Learning for Multi-Messenger Astrophysics: A Gateway for Discovery in the Big Data Era

This report provides an overview of recent work that harnesses the Big D...

0 Gabrielle Allen, et al. ∙

research

∙ 01/21/2019

The Physics of Eccentric Binary Black Hole Mergers. A Numerical Relativity Perspective

Gravitational wave observations of eccentric binary black hole mergers w...

0 E. A. Huerta, et al. ∙

research

∙ 12/05/2018

Unsupervised learning and data clustering for the construction of Galaxy Catalogs in the Dark Energy Survey

Large scale astronomical surveys continue to increase their depth and sc...

0 Asad Khan, et al. ∙

research

∙ 10/06/2018

Supporting High-Performance and High-Throughput Computing for Experimental Science

The advent of experimental science facilities, instruments and observato...

0 E. A. Huerta, et al. ∙

research

∙ 05/07/2018

Real-time regression analysis with deep convolutional neural networks

We discuss the development of novel deep learning algorithms to enable r...

0 E. A. Huerta, et al. ∙

research

∙ 11/27/2017

Denoising Gravitational Waves using Deep Learning with Recurrent Denoising Autoencoders

Gravitational wave astronomy is a rapidly growing field of modern astrop...

0 Hongyu Shen, et al. ∙

research

∙ 11/21/2017

Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation with LIGO Data

The recent Nobel-prize-winning detections of gravitational waves from me...

0 Daniel George, et al. ∙

research

∙ 11/20/2017

Glitch Classification and Clustering for LIGO with Deep Transfer Learning

The detection of gravitational waves with LIGO and Virgo requires a deta...

0 Daniel George, et al. ∙

research

∙ 11/16/2017

Eccentric, nonspinning, inspiral, Gaussian-process merger approximant for the detection and characterization of eccentric binary black hole mergers

We present ENIGMA, a time domain, inspiral-merger-ringdown waveform mode...

0 E. A. Huerta, et al. ∙

research

∙ 11/16/2017

ENIGMA: Eccentric, Non-spinning, Inspiral Gaussian-process Merger Approximant for the characterization of eccentric binary black hole mergers

We present ENIGMA, a time domain, inspiral-merger-ringdown waveform mode...

0 E. A. Huerta, et al. ∙

research

∙ 11/08/2017

Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation: Results with Advanced LIGO Data

The recent Nobel-prize-winning detections of gravitational waves from me...

0 Daniel George, et al. ∙

research

∙ 08/09/2017

Python Open Source Waveform Extractor (POWER): An open source, Python package to monitor and post-process numerical relativity simulations

Numerical simulations of Einstein's field equations provide unique insig...

0 Daniel Johnson, et al. ∙

research

∙ 06/22/2017

Deep Transfer Learning: A new deep learning glitch classification method for advanced LIGO

The exquisite sensitivity of the advanced LIGO detectors has enabled the...

0 Daniel George, et al. ∙

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