Erik Katsavounidis

Publication Activity (10 Years)

Years Active: 2019-2024
Publications (10 Years): 8

Top Topics

Statistical Inference

Symmetry Detection

Machine Learning

Knowledge Resources

Top Venues

Mach. Learn. Sci. Technol.

FlexScience@HPDC

Publications

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
GWAK: gravitational-wave anomalous knowledge with recurrent autoencoders. Mach. Learn. Sci. Technol. 5 (2) (2024)
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 W. Coughlin, Philip C. Harris, Erik Katsavounidis
Rapid Likelihood Free Inference of Compact Binary Coalescences using Accelerated Hardware. CoRR (2024)
Deep Chatterjee, Philip C. Harris, Maanas Goel, Malina Desai, Michael W. Coughlin, Erik Katsavounidis
Optimizing Likelihood-free Inference using Self-supervised Neural Symmetry Embeddings. CoRR (2023)
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
Physics Community Needs, Tools, and Resources for Machine Learning. CoRR (2022)
Alec Gunny, Dylan S. Rankin, Philip C. Harris, Erik Katsavounidis, Ethan Marx, Muhammed Saleem, Michael Coughlin, William Benoit
A Software Ecosystem for Deploying Deep Learning in Gravitational Wave Physics. FlexScience@HPDC (2022)
Reed Essick, Patrick Godwin, Chad Hanna, Lindy Blackburn, Erik Katsavounidis
iDQ: Statistical inference of non-gaussian noise with auxiliary degrees of freedom in gravitational-wave detectors. Mach. Learn. Sci. Technol. 2 (1) (2021)
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, Tomas E. Muller 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 Krupa, Pan Li, Sandeep Madireddy, Ethan Marx, Patrick McCormack, Andres Meza, Jovan Mitrevski, Mohammed Attia Mohammed, Farouk Mokhtar, Eric Moreno, Srishti Nagu, Rohin Narayan, Noah Palladino, Zhiqiang Que, Sang Eon Park, Subramanian Ramamoorthy, Dylan Rankin, Simon Rothman, Ashish Sharma, Sioni Summers, Pietro Vischia, Jean-Roch Vlimant, Olivia Weng
Applications and Techniques for Fast Machine Learning in Science. CoRR (2021)
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)