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ORCIDE. A. Huerta
Eliu A. Huerta
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Journal Articles
- 2023
[j6]Shawn G. Rosofsky
, E. A. Huerta:
Magnetohydrodynamics with physics informed neural operators. Mach. Learn. Sci. Technol. 4(3): 35002 (2023)- [j5]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): 45062 (2023) - 2022
- [j4]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: 828672 (2022) - [j3]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): 15007 (2022) - 2020
- [j2]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): 88 (2020) - 2019
- [j1]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)
Conference and Workshop Papers
- 2023
- [c4]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: 1-4 - 2019
- [c3]Hongyu Shen, Daniel George, Eliu A. Huerta, Zhizhen Zhao:
Denoising Gravitational Waves with Enhanced Deep Recurrent Denoising Auto-encoders. ICASSP 2019: 3237-3241 - 2018
- [c2]Maxim Belkin, Roland Haas, Galen Wesley Arnold, Hon Wai Leong, Eliu A. Huerta, David Lesny, Mark S. Neubauer:
Container solutions for HPC Systems: A Case Study of Using Shifter on Blue Waters. PEARC 2018: 43:1-43:8 - 2017
- [c1]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: 335-344
Informal and Other Publications
- 2023
- [i42]Shawn G. Rosofsky, E. A. Huerta:
Magnetohydrodynamics with Physics Informed Neural Operators. CoRR abs/2302.08332 (2023) - [i41]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 abs/2306.08695 (2023) - [i40]Minyang Tian, E. A. Huerta, Huihuo Zheng:
Physics-inspired spatiotemporal-graph AI ensemble for gravitational wave detection. CoRR abs/2306.15728 (2023) - [i39]Hyun Park, Parth Patel, Roland Haas, E. A. Huerta:
APACE: AlphaFold2 and advanced computing as a service for accelerated discovery in biophysics. CoRR abs/2308.07954 (2023) - [i38]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 abs/2308.08786 (2023) - [i37]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 abs/2309.14675 (2023) - [i36]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 abs/2310.00052 (2023) - [i35]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 abs/2312.08701 (2023) - 2022
- [i34]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 abs/2201.11133 (2022) - [i33]Mohammadtaher Safarzadeh, Asad Khan, E. A. Huerta, Martin Wattenberg:
Interpreting a Machine Learning Model for Detecting Gravitational Waves. CoRR abs/2202.07399 (2022) - [i32]Shawn Rosofsky, Eliu A. Huerta:
Applications of physics informed neural operators. CoRR abs/2203.12634 (2022) - [i31]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 abs/2207.00611 (2022) - [i30]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 abs/2209.11146 (2022) - [i29]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 abs/2210.08973 (2022) - [i28]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 abs/2212.05081 (2022) - [i27]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 abs/2212.11317 (2022) - 2021
- [i26]E. A. Huerta, Zhizhen Zhao:
Advances in Machine and Deep Learning for Modeling and Real-time Detection of Multi-Messenger Sources. CoRR abs/2105.06479 (2021) - [i25]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 abs/2108.02214 (2021) - [i24]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 abs/2110.06968 (2021) - [i23]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 abs/2112.07669 (2021) - 2020
- [i22]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 abs/2003.08394 (2020) - [i21]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 abs/2004.09524 (2020) - [i20]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 abs/2012.08545 (2020) - 2019
- [i19]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 abs/1901.07038 (2019) - [i18]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:
Deep Learning for Multi-Messenger Astrophysics: A Gateway for Discovery in the Big Data Era. CoRR abs/1902.00522 (2019) - [i17]Hongyu Shen, E. A. Huerta, Zhizhen Zhao:
Deep Learning at Scale for Gravitational Wave Parameter Estimation of Binary Black Hole Mergers. CoRR abs/1903.01998 (2019) - [i16]Hongyu Shen, Daniel George, E. A. Huerta, Zhizhen Zhao:
Denoising Gravitational Waves with Enhanced Deep Recurrent Denoising Auto-Encoders. CoRR abs/1903.03105 (2019) - [i15]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:
Enabling real-time multi-messenger astrophysics discoveries with deep learning. CoRR abs/1911.11779 (2019) - [i14]Arjun Gupta, E. A. Huerta, Zhizhen Zhao, Issam Moussa:
Deep Learning for Cardiologist-level Myocardial Infarction Detection in Electrocardiograms. CoRR abs/1912.07618 (2019) - 2018
- [i13]E. A. Huerta, Daniel George, Zhizhen Zhao, Gabrielle Allen:
Real-time regression analysis with deep convolutional neural networks. CoRR abs/1805.02716 (2018) - [i12]Maxim Belkin, Roland Haas, Galen Wesley Arnold, Hon Wai Leong, Eliu A. Huerta, David Lesny, Mark S. Neubauer:
Container solutions for HPC Systems: A Case Study of Using Shifter on Blue Waters. CoRR abs/1808.00556 (2018) - [i11]E. A. Huerta, Roland Haas, Shantenu Jha, Mark S. Neubauer, Daniel S. Katz:
Supporting High-Performance and High-Throughput Computing for Experimental Science. CoRR abs/1810.03056 (2018) - [i10]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 abs/1812.02183 (2018) - 2017
- [i9]Daniel George, E. A. Huerta:
Deep Neural Networks to Enable Real-time Multimessenger Astrophysics. CoRR abs/1701.00008 (2017) - [i8]Daniel George, Hongyu Shen, E. A. Huerta:
Deep Transfer Learning: A new deep learning glitch classification method for advanced LIGO. CoRR abs/1706.07446 (2017) - [i7]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 abs/1708.02941 (2017) - [i6]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 abs/1709.08767 (2017) - [i5]Daniel George, E. A. Huerta:
Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation: Results with Advanced LIGO Data. CoRR abs/1711.03121 (2017) - [i4]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 abs/1711.06276 (2017) - [i3]Daniel George, Hongyu Shen, E. A. Huerta:
Glitch Classification and Clustering for LIGO with Deep Transfer Learning. CoRR abs/1711.07468 (2017) - [i2]Daniel George, E. A. Huerta:
Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation with Advanced LIGO Data. CoRR abs/1711.07966 (2017) - [i1]Hongyu Shen, Daniel George, E. A. Huerta, Zhizhen Zhao:
Denoising Gravitational Waves using Deep Learning with Recurrent Denoising Autoencoders. CoRR abs/1711.09919 (2017)
Coauthor Index
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