E. A. Huerta

According to our database, E. A. Huerta authored at least 43 papers between 2017 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

FAIR AI models in high energy physics.

Javier M. Duarte, Haoyang Li, Avik Roy, Ruike Zhu, E. A. Huerta, Daniel Diaz, Philip C. Harris, Raghav Kansal, Daniel S. Katz, Ishaan H. Kavoori, Volodymyr V. Kindratenko, Farouk Mokhtar, Mark S. Neubauer, Sang Eon Park, Melissa Quinnan, Roger Rusack, Zhizhen Zhao

Mach. Learn. Sci. Technol., December, 2023

Magnetohydrodynamics with physics informed neural operators.

Shawn G. Rosofsky, E. A. Huerta

Mach. Learn. Sci. Technol., September, 2023

Enabling End-to-End Secure Federated Learning in Biomedical Research on Heterogeneous Computing Environments with APPFLx.

Trung-Hieu Hoang, Jordan D. Fuhrman, Ravi K. Madduri, Miao Li, Pranshu Chaturvedi, Zilinghan Li, Kibaek Kim, Minseok Ryu, Ryan Chard, E. A. Huerta, Maryellen L. Giger

CoRR, 2023

AI ensemble for signal detection of higher order gravitational wave modes of quasi-circular, spinning, non-precessing binary black hole mergers.

Minyang Tian, E. A. Huerta, Huihuo Zheng

CoRR, 2023

FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices using a Computing Power Aware Scheduler.

Zilinghan Li, Pranshu Chaturvedi, Shilan He, Han Chen, Gagandeep Singh, Volodymyr V. Kindratenko, E. A. Huerta, Kibaek Kim, Ravi K. Madduri

CoRR, 2023

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

Hyun Park, Parth Patel, Roland Haas, E. A. Huerta

CoRR, 2023

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

Minyang Tian, E. A. Huerta, Huihuo Zheng

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.

Hyun Park, Xiaoli Yan, Ruijie Zhu, E. A. Huerta, Santanu Chaudhuri, Donny Cooper, Ian T. Foster, Emad Tajkhorshid

CoRR, 2023

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

Zilinghan Li, Shilan He, Pranshu Chaturvedi, Trung-Hieu Hoang, Minseok Ryu, E. A. Huerta, Volodymyr V. Kindratenko, Jordan D. Fuhrman, Maryellen L. Giger, Ryan Chard, Kibaek Kim, Ravi K. Madduri

Proceedings of the 19th IEEE International Conference on e-Science, 2023

2022

Statistically-informed deep learning for gravitational wave parameter estimation.

Hongyu Shen, E. A. Huerta, Eamonn O'Shea, Prayush Kumar, Zhizhen Zhao

Mach. Learn. Sci. Technol., 2022

Inference-Optimized AI and High Performance Computing for Gravitational Wave Detection at Scale.

Pranshu Chaturvedi, Asad Khan, Minyang Tian, E. A. Huerta, Huihuo Zheng

Frontiers Artif. Intell., 2022

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

Hyun Park, Ruijie Zhu, E. A. Huerta, Santanu Chaudhuri, Emad Tajkhorshid, Donny Cooper

CoRR, 2022

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

E. A. Huerta, Ben Blaiszik, L. Catherine Brinson, Kristofer E. Bouchard, Daniel Diaz, Caterina Doglioni, Javier M. Duarte, Murali Emani, Ian T. Foster, Geoffrey C. Fox, Philip C. Harris, Lukas Heinrich, Shantenu Jha, Daniel S. Katz, Volodymyr V. Kindratenko, Christine R. Kirkpatrick, Kati Lassila-Perini, Ravi K. Madduri, Mark S. Neubauer, Fotis E. Psomopoulos, Avik Roy, Oliver Rübel, Zhizhen Zhao, Ruike Zhu

CoRR, 2022

MLGWSC-1: The first Machine Learning Gravitational-Wave Search Mock Data Challenge.

Marlin B. Schäfer, Ondrej Zelenka, Alexander H. Nitz, He Wang, Shichao Wu, Zong-Kuan Guo, Zhoujian Cao, Zhixiang Ren, Paraskevi Nousi, Nikolaos Stergioulas, Panagiotis Iosif, Alexandra E. Koloniari, Anastasios Tefas, Nikolaos Passalis, Francesco Salemi, Gabriele Vedovato, Sergey Klimenko, Tanmaya Mishra, Bernd Brügmann, Elena Cuoco, E. A. Huerta, Chris Messenger, Frank Ohme

CoRR, 2022

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

Nikil Ravi, Pranshu Chaturvedi, E. A. Huerta, Zhengchun Liu, Ryan Chard, Aristana Scourtas, K. J. Schmidt, Kyle Chard, Ben Blaiszik, Ian T. Foster

CoRR, 2022

Applications of physics informed neural operators.

Shawn Rosofsky, Eliu A. Huerta

CoRR, 2022

Interpreting a Machine Learning Model for Detecting Gravitational Waves.

Mohammadtaher Safarzadeh, Asad Khan, E. A. Huerta, Martin Wattenberg

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.

Asad Khan, E. A. Huerta

CoRR, 2021

Interpretable AI forecasting for numerical relativity waveforms of quasi-circular, spinning, non-precessing binary black hole mergers.

Asad Khan, E. A. Huerta, Huihuo Zheng

CoRR, 2021

A FAIR and AI-ready Higgs Boson Decay Dataset.

Yifan Chen, E. A. Huerta, Javier M. Duarte, Philip C. Harris, Daniel S. Katz, Mark S. Neubauer, Daniel Diaz, Farouk Mokhtar, Raghav Kansal, Sang Eon Park, Volodymyr V. Kindratenko, Zhizhen Zhao, Roger Rusack

CoRR, 2021

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

E. A. Huerta, Zhizhen Zhao

CoRR, 2021

2020

Convergence of artificial intelligence and high performance computing on NSF-supported cyberinfrastructure.

E. A. Huerta, Asad Khan, Edward Davis, Colleen Bushell, William D. Gropp, Daniel S. Katz, Volodymyr V. Kindratenko, Seid Koric, William T. C. Kramer, Brendan McGinty, Kenton McHenry, Aaron Saxton

J. Big Data, 2020

Confluence of Artificial Intelligence and High Performance Computing for Accelerated, Scalable and Reproducible Gravitational Wave Detection.

E. A. Huerta, Asad Khan, Xiaobo Huang, Minyang Tian, Maksim Levental, Ryan Chard, Wei Wei, Maeve Heflin, Daniel S. Katz, Volodymyr V. Kindratenko, Dawei Mu, Ben Blaiszik, Ian T. Foster

CoRR, 2020

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

Asad Khan, E. A. Huerta, Arnav Das

CoRR, 2020

2019

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

E. A. Huerta, Roland Haas, Shantenu Jha, Mark S. Neubauer, Daniel S. Katz

Comput. Softw. Big Sci., December, 2019

Deep Learning for Cardiologist-level Myocardial Infarction Detection in Electrocardiograms.

Arjun Gupta, E. A. Huerta, Zhizhen Zhao, Issam Moussa

CoRR, 2019

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

E. A. Huerta, Gabrielle Allen, Igor Andreoni, Javier Mauricio Antelis, Etienne Bachelet, G. Bruce Berriman, Federica B. Bianco, Rahul Biswas, Matias Carrasco Kind, Kyle Chard, Minsik Cho, Philip S. Cowperthwaite, Zachariah B. Etienne, Maya Fishbach, Francisco Förster, Daniel George, Tom Gibbs, Matthew J. Graham, William Gropp, Robert A. Gruendl, Anushri Gupta, Roland Haas, Sarah Habib, Elise Jennings, Margaret W. G. Johnson, Erik Katsavounidis, Daniel S. Katz, Asad Khan, Volodymyr V. Kindratenko, William T. C. Kramer, Xin Liu, Ashish Mahabal, Zsuzsa Márka, Kenton McHenry, Jonah Miller, Claudia Moreno, Mark S. Neubauer, Steve Oberlin, Alexander R. Olivas, Donald Petravick, Adam Rebei, Shawn Rosofsky, Milton Ruiz, Aaron Saxton, Bernard F. Schutz, Alexander G. Schwing, Ed Seidel, Stuart L. Shapiro, Hongyu Shen, Yue Shen, Leo Singer, Brigitta M. Sipocz, Lunan Sun, John Towns, Antonios Tsokaros, Wei Wei, Jack Wells, Timothy J. Williams, Jinjun Xiong, Zhizhen Zhao

CoRR, 2019

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

Hongyu Shen, E. A. Huerta, Zhizhen Zhao

CoRR, 2019

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

Gabrielle Allen, Igor Andreoni, Etienne Bachelet, G. Bruce Berriman, Federica B. Bianco, Rahul Biswas, Matias Carrasco Kind, Kyle Chard, Minsik Cho, Philip S. Cowperthwaite, Zachariah B. Etienne, Daniel George, Tom Gibbs, Matthew J. Graham, William Gropp, Anushri Gupta, Roland Haas, E. A. Huerta, Elise Jennings, Daniel S. Katz, Asad Khan, Volodymyr V. Kindratenko, William T. C. Kramer, Xin Liu, Ashish Mahabal, Kenton McHenry, J. M. Miller, Mark S. Neubauer, Steve Oberlin, Alexander Reinefeld, Shawn Rosofsky, Milton Ruiz, Aaron Saxton, Bernard F. Schutz, Alexander G. Schwing, Ed Seidel, Stuart L. Shapiro, Hongyu Shen, Yue Shen, Brigitta Miksztai-Réthey, Lunan Sun, John Towns, Antonios Tsokaros, Wei Wei, Jack Wells, Timothy J. Williams, Jinjun Xiong, Zhizhen Zhao

CoRR, 2019

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

E. A. Huerta, Roland Haas, Sarah Habib, Anushri Gupta, Adam Rebei, Vishnu Chavva, Daniel Johnson, Shawn Rosofsky, Erik Wessel, Bhanu Agarwal, Diyu Luo, Wei Ren

CoRR, 2019

Denoising Gravitational Waves with Enhanced Deep Recurrent Denoising Auto-encoders.

Hongyu Shen, Daniel George, Eliu A. Huerta, Zhizhen Zhao

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.

Asad Khan, E. A. Huerta, Sibo Wang, Robert A. Gruendl

CoRR, 2018

Real-time regression analysis with deep convolutional neural networks.

E. A. Huerta, Daniel George, Zhizhen Zhao, Gabrielle Allen

CoRR, 2018

Container solutions for HPC Systems: A Case Study of Using Shifter on Blue Waters.

Maxim Belkin, Roland Haas, Galen Wesley Arnold, Hon Wai Leong, Eliu A. Huerta, David Lesny, Mark S. Neubauer

Proceedings of the Practice and Experience on Advanced Research Computing, 2018

2017

Denoising Gravitational Waves using Deep Learning with Recurrent Denoising Autoencoders.

Hongyu Shen, Daniel George, E. A. Huerta, Zhizhen Zhao

CoRR, 2017

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

Daniel George, E. A. Huerta

CoRR, 2017

Glitch Classification and Clustering for LIGO with Deep Transfer Learning.

Daniel George, Hongyu Shen, E. A. Huerta

CoRR, 2017

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

E. A. Huerta, C. J. Moore, Prayush Kumar, Daniel George, Alvin J. K. Chua, Roland Haas, Erik Wessel, Daniel Johnson, Derek Glennon, Adam Rebei, A. Miguel Holgado, Jonathan R. Gair, Harald P. Pfeiffer

CoRR, 2017

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

Daniel George, E. A. Huerta

CoRR, 2017

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

Daniel Johnson, E. A. Huerta, Roland Haas

CoRR, 2017

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

Daniel George, Hongyu Shen, E. A. Huerta

CoRR, 2017

Deep Neural Networks to Enable Real-time Multimessenger Astrophysics.

Daniel George, E. A. Huerta

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.

E. A. Huerta, Roland Haas, Edgar Fajardo Hernandez, Daniel S. Katz, Stuart Anderson, Peter Couvares, Josh Willis, Timothy Bouvet, Jeremy Enos, William T. C. Kramer, Hon Wai Leong, David Wheeler

Proceedings of the 13th IEEE International Conference on e-Science, 2017