Erik Katsavounidis

According to our database, Erik Katsavounidis authored at least 10 papers between 2019 and 2026.

Collaborative distances:

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

2026

MARVEL: A Multi Agent-based Research Validator and Enabler using Large Language Models.

Nikhil Mukund, Yifang Luo, Fan Zhang, Lisa Barsotti, Erik Katsavounidis

CoRR, January, 2026

2024

GWAK: gravitational-wave anomalous knowledge with recurrent autoencoders.

Ryan Raikman, Eric A. Moreno, Ekaterina Govorkova, Ethan Marx, Alec Gunny, William Benoit, Deep Chatterjee, Rafia Omer, Muhammed Saleem, Dylan S. Rankin, Michael W. Coughlin, Philip C. Harris, Erik Katsavounidis

Mach. Learn. Sci. Technol., 2024

Rapid likelihood free inference of compact binary coalescences using accelerated hardware.

Deep Chatterjee, Ethan Marx, William Benoit, Ravi Kumar, Malina Desai, Ekaterina Govorkova, Alec Gunny, Eric A. Moreno, Rafia Omer, Ryan Raikman, Muhammed Saleem, Shrey Aggarwal, Michael Coughlin, Philip C. Harris, Erik Katsavounidis

Mach. Learn. Sci. Technol., 2024

A Neural Network-Based Search for Unmodeled Transients in LIGO-Virgo-KAGRA's Third Observing Run.

Ryan Raikman, Eric A. Moreno, Katya Govorkova, Siddharth Soni, Ethan Marx, William Benoit, Alec Gunny, Deep Chatterjee, Christina Reissel, Malina Desai, Rafia Omer, Muhammed Saleem, Philip C. Harris, Erik Katsavounidis, Michael W. Coughlin, Dylan S. Rankin

CoRR, 2024

2023

Optimizing Likelihood-free Inference using Self-supervised Neural Symmetry Embeddings.

Deep Chatterjee, Philip C. Harris, Maanas Goel, Malina Desai, Michael W. Coughlin, Erik Katsavounidis

CoRR, 2023

2022

Physics Community Needs, Tools, and Resources for Machine Learning.

Philip C. Harris, Erik Katsavounidis, William Patrick McCormack, Dylan S. Rankin, Yongbin Feng, Abhijith Gandrakota, Christian Herwig, Burt Holzman, Kevin Pedro, Nhan Tran, Tingjun Yang, Jennifer Ngadiuba, Michael Coughlin, Scott Hauck, Shih-Chieh Hsu, Elham E Khoda, Deming Chen, Mark S. Neubauer, Javier M. Duarte, Georgia Karagiorgi, Mia Liu

CoRR, 2022

A Software Ecosystem for Deploying Deep Learning in Gravitational Wave Physics.

Alec Gunny, Dylan S. Rankin, Philip C. Harris, Erik Katsavounidis, Ethan Marx, Muhammed Saleem, Michael Coughlin, William Benoit

Proceedings of the FlexScience '22: Proceedings of the 12th Workshop on AI and Scientific Computing at Scale using Flexible Computing Infrastructures, 2022

2021

iDQ: Statistical inference of non-gaussian noise with auxiliary degrees of freedom in gravitational-wave detectors.

Reed Essick, Patrick Godwin, Chad Hanna, Lindy Blackburn, Erik Katsavounidis

Mach. Learn. Sci. Technol., 2021

Applications and Techniques for Fast Machine Learning in Science.

Allison McCarn Deiana, Nhan Tran, Joshua Agar, Michaela Blott, Giuseppe Di Guglielmo, Javier M. Duarte, Philip C. Harris, Scott Hauck, Mia Liu, Mark S. Neubauer, Jennifer Ngadiuba, Seda Ogrenci Memik, Maurizio Pierini, Thea Aarrestad, Steffen Bähr, Jürgen Becker, Anne-Sophie Berthold, Richard J. Bonventre, Tomás E. Müller-Bravo, Markus Diefenthaler, Zhen Dong, Nick Fritzsche, Amir Gholami, Ekaterina Govorkova, Kyle J. Hazelwood, Christian Herwig, Babar Khan, Sehoon Kim, Thomas Klijnsma, Yaling Liu, Kin Ho Lo, Tri Nguyen, Gianantonio Pezzullo, Seyedramin Rasoulinezhad, Ryan A. Rivera, Kate Scholberg, Justin Selig, Sougata Sen, Dmitri Strukov, William Tang, Savannah Thais, Kai Lukas Unger, Ricardo Vilalta, Belinavon Krosigk, Thomas K. Warburton, Maria Acosta Flechas, Anthony Aportela, Thomas Calvet, Leonardo Cristella, Daniel Diaz, Caterina Doglioni, Maria Domenica Galati, Elham E Khoda, Farah Fahim, Davide Giri, Benjamin Hawks, Duc Hoang, Burt Holzman, Shih-Chieh Hsu, Sergo Jindariani, Iris Johnson, Raghav Kansal, Ryan Kastner, Erik Katsavounidis, Jeffrey D. Krupa, Pan Li, Sandeep Madireddy, Ethan Marx, Patrick McCormack, Andres Meza, Jovan Mitrevski, Mohammed Attia Mohammed, Farouk Mokhtar, Eric A. Moreno, Srishti Nagu, Rohin Narayan, Noah Palladino, Zhiqiang Que, Sang Eon Park, Subramanian Ramamoorthy, Dylan S. Rankin, Simon Rothman, Ashish Sharma, Sioni Summers, Pietro Vischia, Jean-Roch Vlimant, Olivia Weng

CoRR, 2021

2019

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

Eliu 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