Kevin Pedro

According to our database, Kevin Pedro authored at least 22 papers between 2018 and 2024.

Collaborative distances:

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

2024

Optimizing High-Throughput Inference on Graph Neural Networks at Shared Computing Facilities with the NVIDIA Triton Inference Server.

Claire Savard, Nicholas Manganelli, Burt Holzman, Lindsey Gray, Alexx Perloff, Kevin Pedro, Kevin Stenson, Keith Ulmer

Comput. Softw. Big Sci., December, 2024

2023

Accelerating Machine Learning Inference with GPUs in ProtoDUNE Data Processing.

Tejin Cai, Kenneth Herner, Tingjun Yang, Michael Wang, Maria Acosta Flechas, Philip C. Harris, Burt Holzman, Kevin Pedro, Nhan Tran

Comput. Softw. Big Sci., December, 2023

DeepAstroUDA: semi-supervised universal domain adaptation for cross-survey galaxy morphology classification and anomaly detection.

Aleksandra Ciprijanovic, Ashia Lewis, Kevin Pedro, Sandeep Madireddy, Brian Nord, Gabriel N. Perdue, Stefan M. Wild

Mach. Learn. Sci. Technol., June, 2023

2022

DeepAdversaries: examining the robustness of deep learning models for galaxy morphology classification.

Aleksandra Ciprijanovic, Diana Kafkes, Gregory F. Snyder, F. Javier Sánchez, Gabriel Nathan Perdue, Kevin Pedro, Brian Nord, Sandeep Madireddy, Stefan M. Wild

Mach. Learn. Sci. Technol., 2022

Semi-Supervised Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection.

Aleksandra Ciprijanovic, Ashia Lewis, Kevin Pedro, Sandeep Madireddy, Brian Nord, Gabriel N. Perdue, Stefan M. Wild

CoRR, 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

New directions for surrogate models and differentiable programming for High Energy Physics detector simulation.

Andreas Adelmann, Walter Hopkins, Evangelos Kourlitis, Michael Kagan, Gregor Kasieczka, Claudius Krause, David Shih, Vinicius Mikuni, Benjamin Nachman, Kevin Pedro, Daniel Winklehner

CoRR, 2022

2021

Compressing deep neural networks on FPGAs to binary and ternary precision with hls4ml.

Jennifer Ngadiuba, Vladimir Loncar, Maurizio Pierini, Sioni Summers, Giuseppe Di Guglielmo, Javier M. Duarte, Philip C. Harris, Dylan S. Rankin, Sergo Jindariani, Mia Liu, Kevin Pedro, Nhan Tran, Edward Kreinar, Sheila Sagear, Zhenbin Wu, Duc Hoang

Mach. Learn. Sci. Technol., 2021

GPU coprocessors as a service for deep learning inference in high energy physics.

Jeffrey D. Krupa, Kelvin Lin, Maria Acosta Flechas, Jack Dinsmore, Javier M. Duarte, Philip C. Harris, Scott Hauck, Burt Holzman, Shih-Chieh Hsu, Thomas Klijnsma, Mia Liu, Kevin Pedro, Dylan S. Rankin, Natchanon Suaysom, Matt Trahms, Nhan Tran

Mach. Learn. Sci. Technol., 2021

Fast convolutional neural networks on FPGAs with hls4ml.

Thea Aarrestad, Vladimir Loncar, Nicolò Ghielmetti, Maurizio Pierini, Sioni Summers, Jennifer Ngadiuba, Christoffer Petersson, Hampus Linander, Yutaro Iiyama, Giuseppe Di Guglielmo, Javier M. Duarte, Philip C. Harris, Dylan S. Rankin, Sergo Jindariani, Kevin Pedro, Nhan Tran, Mia Liu, Edward Kreinar, Zhenbin Wu, Duc Hoang

Mach. Learn. Sci. Technol., 2021

GeantV.

Guilherme Amadio, A. Ananya, John Apostolakis, Marilena Bandieramonte, S. Banerjee, Abhijit Bhattacharyya, Calebe P. Bianchini, G. Bitzes, Philippe Canal, Federico Carminati, Oscar Roberto Chaparro-Amaro, Gabriele Cosmo, J. C. De Fine Licht, V. Drogan, Laurent Duhem, Daniel Elvira, J. Fuentes, Andrei Gheata, Mihaela Gheata, Mathieu Gravey, Ilias Goulas, Farah Hariri, Soon Yung Jun, Dmitri Konstantinov, Harphool Kumawat, J. G. Lima, Alberto Maldonado-Romo, Jesús Alberto Martínez-Castro, Pere Mato, T. Nikitina, S. Novaes, Mihály Novák, Kevin Pedro, Witold Pokorski, Alberto Ribon, Ryan Schmitz, Raman Seghal, Oksana Shadura, E. Tcherniaev, Sofia Vallecorsa, Sandro Wenzel, Y. Zhang

Comput. Softw. Big Sci., 2021

Autoencoders for Semivisible Jet Detection.

Florencia Canelli, Annapaola de Cosa, Luc Le Pottier, Jeremi Niedziela, Kevin Pedro, Maurizio Pierini

CoRR, 2021

Robustness of deep learning algorithms in astronomy - galaxy morphology studies.

Aleksandra Ciprijanovic, Diana Kafkes, Gabriel N. Perdue, Kevin Pedro, Gregory F. Snyder, F. Javier Sánchez, Sandeep Madireddy, Stefan M. Wild, Brian Nord

CoRR, 2021

Fast convolutional neural networks on FPGAs with hls4ml.

Thea Aarrestad, Vladimir Loncar, Maurizio Pierini, Sioni Summers, Jennifer Ngadiuba, Christoffer Petersson, Hampus Linander, Yutaro Iiyama, Giuseppe Di Guglielmo, Javier M. Duarte, Philip C. Harris, Dylan S. Rankin, Sergo Jindariani, Kevin Pedro, Nhan Tran, Mia Liu, Edward Kreinar, Zhenbin Wu, Duc Hoang

CoRR, 2021

2020

GPU-Accelerated Machine Learning Inference as a Service for Computing in Neutrino Experiments.

Michael Wang, Tingjun Yang, Maria Acosta Flechas, Philip C. Harris, Benjamin Hawks, Burt Holzman, Kyle Knoepfel, Jeffrey D. Krupa, Kevin Pedro, Nhan Tran

Frontiers Big Data, 2020

Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics.

Yutaro Iiyama, Gianluca Cerminara, Abhijay Gupta, Jan Kieseler, Vladimir Loncar, Maurizio Pierini, Shah Rukh Qasim, Marcel Rieger, Sioni Summers, Gerrit Van Onsem, Kinga Anna Wozniak, Jennifer Ngadiuba, Giuseppe Di Guglielmo, Javier M. Duarte, Philip C. Harris, Dylan S. Rankin, Sergo Jindariani, Mia Liu, Kevin Pedro, Nhan Tran, Edward Kreinar, Zhenbin Wu

Frontiers Big Data, 2020

Coffea - Columnar Object Framework For Effective Analysis.

Nicholas Smith, Lindsey Gray, Matteo Cremonesi, Bo Jayatilaka, Oliver Gutsche, Allison Hall, Kevin Pedro, Maria Acosta Flechas, Andrew Melo, Stefano Belforte, Jim Pivarski

CoRR, 2020

GPU coprocessors as a service for deep learning inference in high energy physics.

Jeffrey D. Krupa, Kelvin Lin, Maria Acosta Flechas, Jack Dinsmore, Javier M. Duarte, Philip C. Harris, Scott Hauck, Burt Holzman, Shih-Chieh Hsu, Thomas Klijnsma, Mia Liu, Kevin Pedro, Natchanon Suaysom, Matt Trahms, Nhan Tran

CoRR, 2020

FPGAs-as-a-Service Toolkit (FaaST).

Dylan S. Rankin, Jeffrey D. Krupa, Philip C. Harris, Maria Acosta Flechas, Burt Holzman, Thomas Klijnsma, Kevin Pedro, Nhan Tran, Scott Hauck, Shih-Chieh Hsu, Matthew Trahms, Kelvin Lin, Yu Lou, Ta-Wei Ho, Javier M. Duarte, Mia Liu

Proceedings of the 2020 IEEE/ACM International Workshop on Heterogeneous High-performance Reconfigurable Computing, 2020

2019

FPGA-Accelerated Machine Learning Inference as a Service for Particle Physics Computing.

Javier M. Duarte, Philip C. Harris, Scott Hauck, Burt Holzman, Shih-Chieh Hsu, Sergo Jindariani, Suffian Khan, Benjamin Kreis, Brian Lee, Mia Liu, Vladimir Loncar, Jennifer Ngadiuba, Kevin Pedro, Brandon Perez, Maurizio Pierini, Dylan S. Rankin, Nhan Tran, Matthew Trahms, Aristeidis Tsaris, Colin Versteeg, Ted W. Way, Dustin Werran, Zhenbin Wu

Comput. Softw. Big Sci., December, 2019

Response to NITRD, NCO, NSF Request for Information on "Update to the 2016 National Artificial Intelligence Research and Development Strategic Plan".

James F. Amundson, James Annis, Camille Avestruz, D. Bowring, João Caldeira, Giuseppe Cerati, Chihway L. Chang, Scott Dodelson, D. Elvira, A. Farahi, Krzysztof L. Genser, Lindsey Gray, Oliver Gutsche, Philip C. Harris, Jamie Kinney, James B. Kowalkowski, Rob Kutschke, S. Mrenna, Brian Nord, A. Para, Kevin Pedro, Gabriel N. Perdue, Alexander Scheinker, Panagiotis Spentzouris, J. St. John, Nhan Tran, Shubhendu Trivedi, Laura Trouille, W. L. K. Wu, C. R. Bom

CoRR, 2019

2018

Strategies for Modeling Extreme Luminosities in the CMS Simulation.

Michael D. Hildreth, Elizabeth Sexton-Kennedy, Kevin Pedro, Matti Kortelainen

Proceedings of the 14th IEEE International Conference on e-Science, 2018