Search Results - "Khoda, Elham E"

Ultra-low latency recurrent neural network inference on FPGAs for physics applications with hls4ml by Khoda, Elham E, Rankin, Dylan, Teixeira de Lima, Rafael, Harris, Philip, Hauck, Scott, Hsu, Shih-Chieh, Kagan, Michael, Loncar, Vladimir, Paikara, Chaitanya, Rao, Richa, Summers, Sioni, Vernieri, Caterina, Wang, Aaron

“…Abstract Recurrent neural networks have been shown to be effective architectures for many tasks in high energy physics, and thus have been widely adopted…”
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Probing Electroweak Phase Transition at CEPC via Exotic Higgs Decays with 4b Final States by Wang, Zhen, Zhu, Xuliang, E Khoda, Elham, Hsu, Shih-Chieh, Konstantinidis, Nikolaos, Li, Ke, Li, Shu, J. Ramsey-Musolf, Michael, Wu, Yanda, E. Zhang, Yuwen

“…A strong first-order electroweak phase transition (EWPT) can be induced by light new physics weakly coupled to the Higgs. This study focuses on a scenario in…”
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Applications and Techniques for Fast Machine Learning in Science by Deiana, Allison McCarn, Tran, Nhan, Agar, Joshua, Blott, Michaela, Di Guglielmo, Giuseppe, Duarte, Javier, Harris, Philip, Hauck, Scott, Liu, Mia, Neubauer, Mark S, Ngadiuba, Jennifer, Ogrenci-Memik, Seda, Pierini, Maurizio, Aarrestad, Thea, Bähr, Steffen, Becker, Jürgen, Berthold, Anne-Sophie, Bonventre, Richard J, Müller Bravo, Tomás E, Diefenthaler, Markus, Dong, Zhen, Fritzsche, Nick, Gholami, Amir, Govorkova, Ekaterina, Guo, Dongning, Hazelwood, Kyle J, Herwig, Christian, Khan, Babar, Kim, Sehoon, Klijnsma, Thomas, Liu, Yaling, Lo, Kin Ho, Nguyen, Tri, Pezzullo, Gianantonio, Rasoulinezhad, Seyedramin, Rivera, Ryan A, Scholberg, Kate, Selig, Justin, Sen, Sougata, Strukov, Dmitri, Tang, William, Thais, Savannah, Unger, Kai Lukas, Vilalta, Ricardo, von Krosigk, Belina, Wang, Shen, Warburton, Thomas K

“…In this community review report, we discuss applications and techniques for machine learning (ML) in science-the concept of integrating powerful ML methods…”
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Ultra-low latency recurrent neural network inference on FPGAs for physics applications with hls4ml by Khoda, Elham E., Rankin, Dylan, Teixeira de Lima, Rafael, Harris, Philip, Hauck, Scott, Hsu, Shih-Chieh, Kagan, Michael, Loncar, Vladimir, Paikara, Chaitanya, Rao, Richa, Summers, Sioni, Vernieri, Caterina, Wang, Aaron

“…Abstract Recurrent neural networks have been shown to be effective architectures for many tasks in high energy physics, and thus have been widely adopted…”
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Ultra Fast Transformers on FPGAs for Particle Physics Experiments by Jiang, Zhixing, Yin, Dennis, Khoda, Elham E, Loncar, Vladimir, Govorkova, Ekaterina, Moreno, Eric, Harris, Philip, Hauck, Scott, Hsu, Shih-Chieh

“…Machine Learning and the Physical Sciences Workshop, NeurIPS 2023 This work introduces a highly efficient implementation of the transformer architecture on a…”
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Low Latency Transformer Inference on FPGAs for Physics Applications with hls4ml by Jiang, Zhixing, Yin, Dennis, Chen, Yihui, Khoda, Elham E, Hauck, Scott, Hsu, Shih-Chieh, Govorkova, Ekaterina, Harris, Philip, Loncar, Vladimir, Moreno, Eric A

“…This study presents an efficient implementation of transformer architectures in Field-Programmable Gate Arrays(FPGAs) using hls4ml. We demonstrate the strategy…”
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Graph Neural Network-based Tracking as a Service by Zhao, Haoran, Naylor, Andrew, Hsu, Shih-Chieh, Calafiura, Paolo, Farrell, Steven, Feng, Yongbing, Harris, Philip Coleman, Khoda, Elham E, Mccormack, William Patrick, Rankin, Dylan Sheldon, Ju, Xiangyang

“…Recent studies have shown promising results for track finding in dense environments using Graph Neural Network (GNN)-based algorithms. However, GNN-based track…”
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FPGA Deployment of LFADS for Real-time Neuroscience Experiments by Liu, Xiaohan, Chen, ChiJui, Huang, YanLun, Yang, LingChi, Khoda, Elham E, Chen, Yihui, Hauck, Scott, Hsu, Shih-Chieh, Lai, Bo-Cheng

“…Fast Machine Learning for Science, ICCAD 2023 Large-scale recordings of neural activity are providing new opportunities to study neural population dynamics. A…”
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Anomalous production of massive gauge boson pairs at muon colliders by Abbott, Brad, Apyan, Aram, Azartash-Namin, Bianca, Balakrishnan, Veena, Berryhill, Jeffrey, Hsu, Shih-Chieh, Jindariani, Sergo, Kawale, Mayuri, Khoda, Elham E, Parsons, Ryan, Schuy, Alexander, Strauss, Michael, Stupak, John, Waits, Connor

“…Phys. Rev. D 108, 093009 (2023) The prospects of searches for anomalous production of hadronically decaying weak boson pairs at proposed high-energy muon…”
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FAIR Universe HiggsML Uncertainty Challenge Competition by Bhimji, Wahid, Calafiura, Paolo, Chakkappai, Ragansu, Chou, Yuan-Tang, Diefenbacher, Sascha, Dudley, Jordan, Farrell, Steven, Ghosh, Aishik, Guyon, Isabelle, Harris, Chris, Hsu, Shih-Chieh, Khoda, Elham E, Lyscar, Rémy, Michon, Alexandre, Nachman, Benjamin, Nugent, Peter, Reymond, Mathis, Rousseau, David, Sluijter, Benjamin, Thorne, Benjamin, Ullah, Ihsan, Zhang, Yulei

“…The FAIR Universe -- HiggsML Uncertainty Challenge focuses on measuring the physics properties of elementary particles with imperfect simulators due to…”
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Study of Electroweak Phase Transition in Exotic Higgs Decays at the CEPC by Wang, Zhen, Zhu, Xuliang, Khoda, Elham E, Hsu, Shih-Chieh, Konstantinidis, Nikolaos, Li, Ke, Li, Shu, Ramsey-Musolf, Michael J, Wu, Yanda, Zhang, Yuwen E

“…A strong first-order electroweak phase transition (EWPT) can be induced by light new physics weakly coupled to the Higgs. This study focuses on a scenario in…”
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Ultra-low latency recurrent neural network inference on FPGAs for physics applications with hls4ml by Khoda, Elham E, Rankin, Dylan, de Lima, Rafael Teixeira, Harris, Philip, Hauck, Scott, Hsu, Shih-Chieh, Kagan, Michael, Loncar, Vladimir, Paikara, Chaitanya, Rao, Richa, Summers, Sioni, Vernieri, Caterina, Wang, Aaron

“…Recurrent neural networks have been shown to be effective architectures for many tasks in high energy physics, and thus have been widely adopted. Their use in…”
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Data Science and Machine Learning in Education by Benelli, Gabriele, Chen, Thomas Y, Duarte, Javier, Feickert, Matthew, Graham, Matthew, Gray, Lindsey, Hackett, Dan, Harris, Phil, Hsu, Shih-Chieh, Kasieczka, Gregor, Khoda, Elham E, Komm, Matthias, Liu, Mia, Neubauer, Mark S, Norberg, Scarlet, Perloff, Alexx, Rieger, Marcel, Savard, Claire, Terao, Kazuhiro, Thais, Savannah, Roy, Avik, Vlimant, Jean-Roch, Chachamis, Grigorios

“…The growing role of data science (DS) and machine learning (ML) in high-energy physics (HEP) is well established and pertinent given the complex detectors,…”
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Physics Community Needs, Tools, and Resources for Machine Learning by Harris, Philip, Katsavounidis, Erik, McCormack, William Patrick, Rankin, Dylan, Feng, Yongbin, Gandrakota, Abhijith, Herwig, Christian, Holzman, Burt, Pedro, Kevin, Tran, Nhan, Yang, Tingjun, Ngadiuba, Jennifer, Coughlin, Michael, Hauck, Scott, Hsu, Shih-Chieh, Khoda, Elham E, Chen, Deming, Neubauer, Mark, Duarte, Javier, Karagiorgi, Georgia, Liu, Mia

“…Machine learning (ML) is becoming an increasingly important component of cutting-edge physics research, but its computational requirements present significant…”
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Applications of Deep Learning to physics workflows by Agarwal, Manan, Alameda, Jay, Audenaert, Jeroen, Benoit, Will, Beveridge, Damon, Bhattacharya, Meghna, Chatterjee, Chayan, Chatterjee, Deep, Chen, Andy, Cholayil, Muhammed Saleem, Chou, Chia-Jui, Choudhary, Sunil, Coughlin, Michael, Dax, Maximilian, Desai, Aman, Di Luca, Andrea, Duarte, Javier Mauricio, Farrell, Steven, Feng, Yongbin, Goodarzi, Pooyan, Govorkova, Ekaterina, Graham, Matthew, Guiang, Jonathan, Gunny, Alec, Guo, Weichangfeng, Hakenmueller, Janina, Hawks, Ben, Hsu, Shih-Chieh, Jawahar, Pratik, Ju, Xiangyang, Katsavounidis, Erik, Kellis, Manolis, Khoda, Elham E, Lahbabi, Fatima Zahra, Lian, Van Tha Bik, Liu, Mia, Malanchev, Konstantin, Marx, Ethan, McCormack, William Patrick, McLeod, Alistair, Mo, Geoffrey, Moreno, Eric Anton, Muthukrishna, Daniel, Narayan, Gautham, Naylor, Andrew, Neubauer, Mark, Norman, Michael, Omer, Rafia, Pedro, Kevin, Peterson, Joshua, Pürrer, Michael, Raikman, Ryan, Raj, Shivam, Ricker, George, Robbins, Jared, Samani, Batool Safarzadeh, Scholberg, Kate, Schuy, Alex, Skliris, Vasileios, Soni, Siddharth, Sravan, Niharika, Sutton, Patrick, Villar, Victoria Ashley, Wang, Xiwei, Wen, Linqing, Wuerthwein, Frank, Yang, Tingjun, Yeh, Shu-Wei

“…Modern large-scale physics experiments create datasets with sizes and streaming rates that can exceed those from industry leaders such as Google Cloud and…”
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Report of the Topical Group on Electroweak Precision Physics and Constraining New Physics for Snowmass 2021 by Belloni, Alberto, Freitas, Ayres, Tian, Junping, Apyan, Juan Alcaraz Maestre Aram, Azartash-Namin, Bianca, Azzurri, Paolo, Banerjee, Swagato, Beyer, Jakob, Bhattacharya, Saptaparna, de Blas, Jorge, Blondel, Alain, Britzger, Daniel, Dam, Mogens, Du, Yong, d'Enterria, David, Fujii, Keisuke, Grojean, Christophe, Gu, Jiayin, Han, Tao, Hildreth, Michael, Irles, Adrián, Janot, Patrick, Jeans, Daniel, Kawale, Mayuri, Khoda, Elham E, Lane, Samuel, Lewis, Ian, Liang, Zhijun, List, Jenny, Liu, Zhen, Ma, Yang, Miralles, Victor, Mizuno, Takahiro, Mwewa, Chilufya, Narain, Meenakshi, Nedic, Luka, Neubauer, Mark S, Paranjape, Chaitanya, Peskin, Michael, Petti, Roberto, Pleier, Marc-André, Poeschl, Roman, Potamianos, Karolos, Reina, Laura, Reuter, Jürgen, Robens, Tania, Roloff, Philipp, Roney, Michael, Ruan, Manqi, Ruiz, Richard, Schuy, Alex, Shepherd, William, Stolarski, Daniel, Stupak, John, Suehara, Taikan, Tenchini, Roberto, Tricoli, Alessandro, Vryonidou, Eleni, Vos, Marcel, Waits, Connor, Wilson, Graham, Wu, Yongcheng, Xie, Keping, Yang, Siqi, Yang, Tianyi, Yumino, Keita, Zhou, Shuang-Yong, Zhu, Junjie

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

“…Front. Big Data 5, 787421 (2022) In this community review report, we discuss applications and techniques for fast machine learning (ML) in science -- the…”
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