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ORCIDSavannah Thais
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2020 – today
- 2024
[j7]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, Dongning Guo, 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, Shen Wang, Thomas K. Warburton:
Corrigendum: Applications and techniques for fast machine learning in science. Frontiers Big Data 6 (2024)- [i15]Savannah Thais:
Misrepresented Technological Solutions in Imagined Futures: The Origins and Dangers of AI Hype in the Research Community. CoRR abs/2408.15244 (2024) - 2023
- [i14]Daniel Murnane, Savannah Thais, Ameya Thete:
Equivariant Graph Neural Networks for Charged Particle Tracking. CoRR abs/2304.05293 (2023) - [i13]Ayse Gizem Yasar, Andrew Chong, Evan Dong, Thomas Krendl Gilbert, Sarah Hladikova, Roland Maio, Carlos Mougan, Xudong Shen, Shubham Singh, Ana-Andreea Stoica, Savannah Thais, Miri Zilka:
AI and the EU Digital Markets Act: Addressing the Risks of Bigness in Generative AI. CoRR abs/2308.02033 (2023) - [i12]Savannah Thais, Daniel Murnane:
Equivariance Is Not All You Need: Characterizing the Utility of Equivariant Graph Neural Networks for Particle Physics Tasks. CoRR abs/2311.03094 (2023) - 2022
- [j6]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, Dongning Guo, 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, Shen Wang, Thomas K. Warburton:
Applications and Techniques for Fast Machine Learning in Science. Frontiers Big Data 5: 787421 (2022) - [j5]Abdelrahman Elabd, Vesal Razavimaleki, Shi-Yu Huang, Javier M. Duarte, Markus Atkinson, Gage DeZoort, Peter Elmer, Scott Hauck, Jin-Xuan Hu, Shih-Chieh Hsu, Bo-Cheng Lai, Mark S. Neubauer, Isobel Ojalvo, Savannah Thais, Matthew Trahms:
Graph Neural Networks for Charged Particle Tracking on FPGAs. Frontiers Big Data 5: 828666 (2022) - [c1]Savannah Thais, Shaine Leibowitz, Allie Saizan, Ashay Singh:
Understanding Historical, Socio-Economic, and Policy Contributions to COVID-19 Health Inequities. ISCRAM 2022: 481-494 - [i11]Daniel Murnane, Savannah Thais, Jason Wong:
Semi-Equivariant GNN Architectures for Jet Tagging. CoRR abs/2202.06941 (2022) - [i10]Alexander Bogatskiy, Sanmay Ganguly, Thomas Kipf, Risi Kondor, David W. Miller, Daniel Murnane, Jan T. Offermann, Mariel Pettee, Phiala Shanahan, Chase Shimmin, Savannah Thais:
Symmetry Group Equivariant Architectures for Physics. CoRR abs/2203.06153 (2022) - [i9]Savannah Thais, Paolo Calafiura, Grigorios Chachamis, Gage DeZoort, Javier M. Duarte, Sanmay Ganguly, Michael Kagan, Daniel Murnane, Mark S. Neubauer, Kazuhiro Terao:
Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges. CoRR abs/2203.12852 (2022) - [i8]Gabriele Benelli, Thomas Y. Chen, Javier M. Duarte, Matthew Feickert, Matthew J. Graham, Lindsey Gray, Dan Hackett, Philip C. Harris, Shih-Chieh Hsu, Gregor Kasieczka, Elham E Khoda, Matthias Komm, Mia Liu, Mark S. Neubauer, Scarlet Norberg, Alexx Perloff, Marcel Rieger, Claire Savard, Kazuhiro Terao, Savannah Thais, Avik Roy, Jean-Roch Vlimant, Grigorios Chachamis:
Data Science and Machine Learning in Education. CoRR abs/2207.09060 (2022) - 2021
- [j4]Christoph Ebell, Ricardo Baeza-Yates, Richard Benjamins, Hengjin Cai, Mark Coeckelbergh, Tania Duarte, Merve Hickok, Aurelie Jacquet, Angela Kim, Joris Krijger, John MacIntyre, Piyush B. Madhamshettiwar, Lauren Maffeo, Jeanna Matthews, Larry R. Medsker, Peter Smith, Savannah Thais:
Towards intellectual freedom in an AI Ethics Global Community. AI Ethics 1(2): 131-138 (2021) - [j3]Christoph Ebell, Ricardo Baeza-Yates, Richard Benjamins, Hengjin Cai, Mark Coeckelbergh, Tania Duarte, Merve Hickok, Aurelie Jacquet, Angela Kim, Joris Krijger, John MacIntyre, Piyush B. Madhamshettiwar, Lauren Mafeo, Jeanna Matthews, Larry R. Medsker, Peter Smith, Savannah Thais:
Correction to: Towards intellectual freedom in an AI Ethics Global Community. AI Ethics 1(2): 139-140 (2021) - [j2]Gage DeZoort, Savannah Thais, Javier M. Duarte, Vesal Razavimaleki, Markus Atkinson, Isobel Ojalvo, Mark S. Neubauer, Peter Elmer:
Charged Particle Tracking via Edge-Classifying Interaction Networks. Comput. Softw. Big Sci. 5(1) (2021) - [j1]Sudhir Malik, Samuel Meehan, Kilian Lieret, Meirin Oan Evans, Michel Hernández Villanueva, Daniel S. Katz, Graeme A. Stewart, Peter Elmer, Sizar Aziz, Matthew Bellis, Riccardo Maria Bianchi, Gianluca Bianco, Johan Sebastian Bonilla, Angela Burger, Jackson Burzynski, David Chamont, Matthew Feickert, Philipp Gadow, Bernhard Manfred Gruber, Daniel Guest, Stephan Hageboeck, Lukas Heinrich, Maximilian M. Horzela, Marc Huwiler, Clemens Lange, Konstantin Lehmann, Ke Li, Devdatta Majumder, Judita Mamuzic, Kevin Nelson, Robin Newhouse, Emery Nibigira, Scarlet Norberg, Arturo Sánchez Pineda, Mason Proffitt, Brendan Regnery, Amber Roepe, Stefan Roiser, Henry Schreiner, Oksana Shadura, Giordon Stark, Stephen Nicholas Swatman, Savannah Thais, Andrea Valassi, Stefan Wunsch, David Yakobovitch, Siqi Yuan:
Software Training in HEP. Comput. Softw. Big Sci. 5(1) (2021) - [i7]Savannah Thais, Gage DeZoort:
Instance Segmentation GNNs for One-Shot Conformal Tracking at the LHC. CoRR abs/2103.06509 (2021) - [i6]Xiangyang Ju, Daniel Murnane, Paolo Calafiura, Nicholas Choma, Sean Conlon, Steven Farrell, Yaoyuan Xu, Maria Spiropulu, Jean-Roch Vlimant, Adam Aurisano, Jeremy Hewes, Giuseppe Cerati, Lindsey Gray, Thomas Klijnsma, Jim Kowalkowski, Markus Atkinson, Mark S. Neubauer, Gage DeZoort, Savannah Thais, Aditi Chauhan, Alex Schuy, Shih-Chieh Hsu, Alexandra Ballow, Alina Lazar:
Physics and Computing Performance of the Exa.TrkX TrackML Pipeline. CoRR abs/2103.06995 (2021) - [i5]Gage DeZoort, Savannah Thais, Isobel Ojalvo, Peter Elmer, Vesal Razavimaleki, Javier M. Duarte, Markus Atkinson, Mark S. Neubauer:
Charged particle tracking via edge-classifying interaction networks. CoRR abs/2103.16701 (2021) - [i4]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:
Applications and Techniques for Fast Machine Learning in Science. CoRR abs/2110.13041 (2021) - [i3]Abdelrahman Elabd, Vesal Razavimaleki, Shi-Yu Huang, Javier M. Duarte, Markus Atkinson, Gage DeZoort, Peter Elmer, Jin-Xuan Hu, Shih-Chieh Hsu, Bo-Cheng Lai, Mark S. Neubauer, Isobel Ojalvo, Savannah Thais:
Graph Neural Networks for Charged Particle Tracking on FPGAs. CoRR abs/2112.02048 (2021) - 2020
- [i2]Aneesh Heintz, Vesal Razavimaleki, Javier M. Duarte, Gage DeZoort, Isobel Ojalvo, Savannah Thais, Markus Atkinson, Mark S. Neubauer, Lindsey Gray, Sergo Jindariani, Nhan Tran, Philip C. Harris, Dylan S. Rankin, Thea Aarrestad, Vladimir Loncar, Maurizio Pierini, Sioni Summers, Jennifer Ngadiuba, Mia Liu, Edward Kreinar, Zhenbin Wu:
Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs. CoRR abs/2012.01563 (2020)
2010 – 2019
- 2018
- [i1]Kim Albertsson, Piero Altoe, Dustin Anderson, Michael Andrews, Juan Pedro Araque Espinosa, Adam Aurisano, Laurent Basara, Adrian Bevan, Wahid Bhimji, Daniele Bonacorsi, Paolo Calafiura, Mario Campanelli, Louis Capps, Federico Carminati, Stefano Carrazza, Taylor Childers, Elias Coniavitis, Kyle Cranmer, Claire David, Douglas Davis, Javier M. Duarte, Martin Erdmann, Jonas Eschle, Amir Farbin, Matthew Feickert, Nuno Filipe Castro, Conor Fitzpatrick, Michele Floris, Alessandra Forti, Jordi Garra-Tico, Jochen Gemmler, Maria Girone, Paul Glaysher, Sergei Gleyzer, Vladimir V. Gligorov, Tobias Golling, Jonas Graw, Lindsey Gray, Dick Greenwood, Thomas Hacker, John Harvey, Benedikt Hegner, Lukas Heinrich, Ben Hooberman, Johannes Junggeburth, Michael Kagan, Meghan Kane, Konstantin Kanishchev, Przemyslaw Karpinski, Zahari Kassabov, Gaurav Kaul, Dorian Kcira, Thomas Keck, Alexei Klimentov, Jim Kowalkowski, Luke Kreczko, Alexander Kurepin, Rob Kutschke, Valentin Kuznetsov, Nicolas Köhler, Igor Lakomov, Kevin Lannon, Mario Lassnig, Antonio Limosani, Gilles Louppe, Aashrita Mangu, Pere Mato, Narain Meenakshi, Helge Meinhard, Dario Menasce, Lorenzo Moneta, Seth Moortgat, Mark S. Neubauer, Harvey B. Newman, Hans Pabst, Michela Paganini, Manfred Paulini, Gabriel N. Perdue, Uzziel Perez, Attilio Picazio, Jim Pivarski, Harrison Prosper, Fernanda Psihas, Alexander Radovic, Ryan Reece, Aurelius Rinkevicius, Eduardo Rodrigues, Jamal Rorie, David Rousseau, Aaron Sauers, Steven Schramm, Ariel Schwartzman, Horst Severini, Paul Seyfert, Filip Siroky, Konstantin Skazytkin, Mike Sokoloff, Graeme Andrew Stewart, Bob Stienen, Ian Stockdale, Giles Chatham Strong, Savannah Thais, Karen Tomko, Eli Upfal, Emanuele Usai, Andrey Ustyuzhanin, Martin Vala, Sofia Vallecorsa, Mauro Verzetti, Xavier Vilasís-Cardona, Jean-Roch Vlimant, Ilija Vukotic, Sean-Jiun Wang, Gordon Watts, Michael Williams, Wenjing Wu, Stefan Wunsch, Omar Zapata:
Machine Learning in High Energy Physics Community White Paper. CoRR abs/1807.02876 (2018)
Coauthor Index
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