E. A. Huerta

Publication Activity (10 Years)

Years Active: 2017-2023
Publications (10 Years): 48

Top Topics

Parameter Estimation

High Performance Computing

Top Venues

Mach. Learn. Sci. Technol.

Publications

Hyun Park, Xiaoli Yan, Ruijie Zhu, E. A. Huerta, Santanu Chaudhuri, Donny Cooper, Ian T. Foster, Emad Tajkhorshid
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)
Minyang Tian, E. A. Huerta, Huihuo Zheng
Physics-inspired spatiotemporal-graph AI ensemble for gravitational wave detection. CoRR (2023)
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
APPFLx: Providing Privacy-Preserving Cross-Silo Federated Learning as a Service. e-Science (2023)
Minyang Tian, E. A. Huerta, Huihuo Zheng
AI ensemble for signal detection of higher order gravitational wave modes of quasi-circular, spinning, non-precessing binary black hole mergers. CoRR (2023)
Shawn G. Rosofsky, E. A. Huerta
Magnetohydrodynamics with physics informed neural operators. Mach. Learn. Sci. Technol. 4 (3) (2023)
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
Enabling End-to-End Secure Federated Learning in Biomedical Research on Heterogeneous Computing Environments with APPFLx. CoRR (2023)
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
APPFLx: Providing Privacy-Preserving Cross-Silo Federated Learning as a Service. CoRR (2023)
Hyun Park, Parth Patel, Roland Haas, E. A. Huerta
APACE: AlphaFold2 and advanced computing as a service for accelerated discovery in biophysics. CoRR (2023)
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
FAIR AI models in high energy physics. Mach. Learn. Sci. Technol. 4 (4) (2023)
Shawn G. Rosofsky, E. A. Huerta
Magnetohydrodynamics with Physics Informed Neural Operators. CoRR (2023)
Zilinghan Li, Pranshu Chaturvedi, Shilan He, Han Chen, Gagandeep Singh, Volodymyr V. Kindratenko, E. A. Huerta, Kibaek Kim, Ravi K. Madduri
FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices using a Computing Power Aware Scheduler. CoRR (2023)
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
FAIR AI Models in High Energy Physics. CoRR (2022)
Mohammadtaher Safarzadeh, Asad Khan, E. A. Huerta, Martin Wattenberg
Interpreting a Machine Learning Model for Detecting Gravitational Waves. CoRR (2022)
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
FAIR for AI: An interdisciplinary, international, inclusive, and diverse community building perspective. CoRR (2022)
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
MLGWSC-1: The first Machine Learning Gravitational-Wave Search Mock Data Challenge. CoRR (2022)
Pranshu Chaturvedi, Asad Khan, Minyang Tian, E. A. Huerta, Huihuo Zheng
Inference-Optimized AI and High Performance Computing for Gravitational Wave Detection at Scale. Frontiers Artif. Intell. 5 (2022)
Nikil Ravi, Pranshu Chaturvedi, E. A. Huerta, Zhengchun Liu, Ryan Chard, Aristana Scourtas, K. J. Schmidt, Kyle Chard, Ben Blaiszik, Ian T. Foster
FAIR principles for AI models, with a practical application for accelerated high energy diffraction microscopy. CoRR (2022)
Hongyu Shen, E. A. Huerta, Eamonn O'Shea, Prayush Kumar, Zhizhen Zhao
Statistically-informed deep learning for gravitational wave parameter estimation. Mach. Learn. Sci. Technol. 3 (1) (2022)
Hyun Park, Ruijie Zhu, E. A. Huerta, Santanu Chaudhuri, Emad Tajkhorshid, Donny Cooper
End-to-end AI Framework for Hyperparameter Optimization, Model Training, and Interpretable Inference for Molecules and Crystals. CoRR (2022)
Pranshu Chaturvedi, Asad Khan, Minyang Tian, E. A. Huerta, Huihuo Zheng
Inference-optimized AI and high performance computing for gravitational wave detection at scale. CoRR (2022)
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
A FAIR and AI-ready Higgs Boson Decay Dataset. CoRR (2021)
Asad Khan, E. A. Huerta, Huihuo Zheng
Interpretable AI forecasting for numerical relativity waveforms of quasi-circular, spinning, non-precessing binary black hole mergers. CoRR (2021)
Asad Khan, E. A. Huerta
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)
E. A. Huerta, Zhizhen Zhao
Advances in Machine and Deep Learning for Modeling and Real-time Detection of Multi-Messenger Sources. CoRR (2021)
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
Confluence of Artificial Intelligence and High Performance Computing for Accelerated, Scalable and Reproducible Gravitational Wave Detection. CoRR (2020)
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
Convergence of Artificial Intelligence and High Performance Computing on NSF-supported Cyberinfrastructure. CoRR (2020)
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
Convergence of artificial intelligence and high performance computing on NSF-supported cyberinfrastructure. J. Big Data 7 (1) (2020)
Asad Khan, E. A. Huerta, Arnav Das
Physics-inspired deep learning to characterize the signal manifold of quasi-circular, spinning, non-precessing binary black hole mergers. CoRR (2020)
Hongyu Shen, E. A. Huerta, Zhizhen Zhao
Deep Learning at Scale for Gravitational Wave Parameter Estimation of Binary Black Hole Mergers. CoRR (2019)
Arjun Gupta, E. A. Huerta, Zhizhen Zhao, Issam Moussa
Deep Learning for Cardiologist-level Myocardial Infarction Detection in Electrocardiograms. CoRR (2019)
E. A. Huerta, Roland Haas, Shantenu Jha, Mark S. Neubauer, Daniel S. Katz
Supporting High-Performance and High-Throughput Computing for Experimental Science. Comput. Softw. Big Sci. 3 (1) (2019)
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
The Physics of Eccentric Binary Black Hole Mergers. A Numerical Relativity Perspective. CoRR (2019)
Hongyu Shen, Daniel George, E. A. Huerta, Zhizhen Zhao
Denoising Gravitational Waves with Enhanced Deep Recurrent Denoising Auto-Encoders. CoRR (2019)
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 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
Enabling real-time multi-messenger astrophysics discoveries with deep learning. CoRR (2019)
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 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
Deep Learning for Multi-Messenger Astrophysics: A Gateway for Discovery in the Big Data Era. CoRR (2019)
E. A. Huerta, Roland Haas, Shantenu Jha, Mark Neubauer, Daniel S. Katz
Supporting High-Performance and High-Throughput Computing for Experimental Science. CoRR (2018)
Asad Khan, E. A. Huerta, Sibo Wang, Robert A. Gruendl
Unsupervised learning and data clustering for the construction of Galaxy Catalogs in the Dark Energy Survey. CoRR (2018)
E. A. Huerta, Daniel George, Zhizhen Zhao, Gabrielle Allen
Real-time regression analysis with deep convolutional neural networks. CoRR (2018)
Daniel Johnson, E. A. Huerta, Roland Haas
Python Open Source Waveform Extractor (POWER): An open source, Python package to monitor and post-process numerical relativity simulations. CoRR (2017)
Daniel George, E. A. Huerta
Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation with Advanced LIGO Data. CoRR (2017)
Daniel George, E. A. Huerta
Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation: Results with Advanced LIGO Data. CoRR (2017)
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
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. eScience (2017)
Daniel George, Hongyu Shen, E. A. Huerta
Deep Transfer Learning: A new deep learning glitch classification method for advanced LIGO. CoRR (2017)
Daniel George, E. A. Huerta
Deep Neural Networks to Enable Real-time Multimessenger Astrophysics. CoRR (2017)
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
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. CoRR (2017)
Hongyu Shen, Daniel George, E. A. Huerta, Zhizhen Zhao
Denoising Gravitational Waves using Deep Learning with Recurrent Denoising Autoencoders. CoRR (2017)
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
ENIGMA: Eccentric, Non-spinning, Inspiral Gaussian-process Merger Approximant for the characterization of eccentric binary black hole mergers. CoRR (2017)
Daniel George, Hongyu Shen, E. A. Huerta
Glitch Classification and Clustering for LIGO with Deep Transfer Learning. CoRR (2017)