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This is just a disambiguation page, and is not intended to be the bibliography of an actual person. Any publication listed on this page has not been assigned to an actual author yet. If you know the true author of one of the publications listed below, you are welcome to contact us.
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2020 – today
- 2024
[j15]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)- [j14]Javier Campos, Jovan Mitrevski, Nhan Tran, Zhen Dong, Amir Gholaminejad, Michael W. Mahoney, Javier M. Duarte:
End-to-end codesign of Hessian-aware quantized neural networks for FPGAs. ACM Trans. Reconfigurable Technol. Syst. 17(3): 36:1-36:22 (2024) - [c30]Nhan Tran, Ethan Yang, Angelique Taylor, Abe Davis:
Personal Time-Lapse. UIST 2024: 56:1-56:13 - [c29]Tommaso Baldi, Javier Campos, Benjamin Hawks, Jennifer Ngadiuba, Nhan Tran, Daniel Diaz, Javier M. Duarte, Ryan Kastner, Andres Meza, Melissa Quinnan, Olivia Weng, Caleb Geniesse, Amir Gholami, Michael W. Mahoney, Vladimir Loncar, Philip C. Harris, Joshua Agar, Shuyu Qin:
Reliable edge machine learning hardware for scientific applications. VTS 2024: 1-5 - [i35]Abhijith Gandrakota, Lily H. Zhang, Aahlad Manas Puli, Kyle Cranmer, Jennifer Ngadiuba, Rajesh Ranganath, Nhan Tran:
Robust Anomaly Detection for Particle Physics Using Multi-Background Representation Learning. CoRR abs/2401.08777 (2024) - [i34]Olivia Weng, Alexander Redding, Nhan Tran, Javier Mauricio Duarte, Ryan Kastner:
Architectural Implications of Neural Network Inference for High Data-Rate, Low-Latency Scientific Applications. CoRR abs/2403.08980 (2024) - [i33]Tommaso Baldi, Javier Campos, Benjamin Hawks, Jennifer Ngadiuba, Nhan Tran, Daniel Diaz, Javier M. Duarte, Ryan Kastner, Andres Meza, Melissa Quinnan, Olivia Weng, Caleb Geniesse, Amir Gholami, Michael W. Mahoney, Vladimir Loncar, Philip C. Harris, Joshua Agar, Shuyu Qin:
Reliable edge machine learning hardware for scientific applications. CoRR abs/2406.19522 (2024) - 2023
- [j13]Tejin Cai, Kenneth Herner, Tingjun Yang, Michael Wang, Maria Acosta Flechas, Philip C. Harris, Burt Holzman, Kevin Pedro, Nhan Tran:
Accelerating Machine Learning Inference with GPUs in ProtoDUNE Data Processing. Comput. Softw. Big Sci. 7(1): 11 (2023) - [j12]Rohan Shenoy, Javier M. Duarte, Christian Herwig, James Hirschauer, Daniel Noonan, Maurizio Pierini, Nhan Tran, Cristina Mantilla Suarez:
Differentiable Earth mover's distance for data compression at the high-luminosity LHC. Mach. Learn. Sci. Technol. 4(4): 45058 (2023) - [j11]Landon Brown, Jared Hamilton, Zhao Han, Albert Phan, Thao Phung, Eric Hansen, Nhan Tran, Tom Williams:
Best of Both Worlds? Combining Different Forms of Mixed Reality Deictic Gestures. ACM Trans. Hum. Robot Interact. 12(1): 9:1-9:23 (2023) - [c28]Nhan Tran, Trevor Grant, Thao Phung, Leanne M. Hirshfield, Christopher D. Wickens, Tom Williams:
Now Look Here! Đownarrow Mixed Reality Improves Robot Communication Without Cognitive Overload. HCI (17) 2023: 395-415 - [c27]Shikun Liu, Tianchun Li, Yongbin Feng, Nhan Tran, Han Zhao, Qiang Qiu, Pan Li:
Structural Re-weighting Improves Graph Domain Adaptation. ICML 2023: 21778-21793 - [c26]Shruti R. Kulkarni, Aaron R. Young, Prasanna Date, Narasinga Rao Miniskar, Jeffrey S. Vetter, Farah Fahim, Benjamin Parpillon, Jennet Dickinson, Nhan Tran, Jieun Yoo, Corrinne Mills, Morris Swartz, Petar Maksimovic, Catherine D. Schuman, Alice Bean:
On-Sensor Data Filtering using Neuromorphic Computing for High Energy Physics Experiments. ICONS 2023: 3:1-3:8 - [c25]Narasinga Rao Miniskar, Pruek Vanna-Iampikul, Aaron R. Young, Sung Kyu Lim, Frank Liu, Jieun Yoo, Corrinne Mills, Nhan Tran, Farah Fahim, Jeffrey S. Vetter:
A 3D Implementation of Convolutional Neural Network for Fast Inference. ISCAS 2023: 1-5 - [i32]Tejin Cai, Kenneth Herner, Tingjun Yang, Michael Wang, Maria Acosta Flechas, Philip C. Harris, Burt Holzman, Kevin Pedro, Nhan Tran:
Accelerating Machine Learning Inference with GPUs in ProtoDUNE Data Processing. CoRR abs/2301.04633 (2023) - [i31]Javier Campos, Zhen Dong, Javier M. Duarte, Amir Gholami, Michael W. Mahoney, Jovan Mitrevski, Nhan Tran:
End-to-end codesign of Hessian-aware quantized neural networks for FPGAs and ASICs. CoRR abs/2304.06745 (2023) - [i30]Shikun Liu, Tianchun Li, Yongbin Feng, Nhan Tran, Han Zhao, Qiu Qiang, Pan Li:
Structural Re-weighting Improves Graph Domain Adaptation. CoRR abs/2306.03221 (2023) - [i29]Rohan Shenoy, Javier M. Duarte, Christian Herwig, James Hirschauer, Daniel Noonan, Maurizio Pierini, Nhan Tran, Cristina Mantilla Suarez:
Differentiable Earth Mover's Distance for Data Compression at the High-Luminosity LHC. CoRR abs/2306.04712 (2023) - [i28]Shruti R. Kulkarni, Aaron R. Young, Prasanna Date, Narasinga Rao Miniskar, Jeffrey S. Vetter, Farah Fahim, Benjamin Parpillon, Jennet Dickinson, Nhan Tran, Jieun Yoo, Corrinne Mills, Morris Swartz, Petar Maksimovic, Catherine D. Schuman, Alice Bean:
On-Sensor Data Filtering using Neuromorphic Computing for High Energy Physics Experiments. CoRR abs/2307.11242 (2023) - [i27]Yumou Wei, Ryan F. Forelli, Chris Hansen, Jeffrey P. Levesque, Nhan Tran, Joshua C. Agar, Giuseppe Di Guglielmo, Michael E. Mauel, Gerald A. Navratil:
Low latency optical-based mode tracking with machine learning deployed on FPGAs on a tokamak. CoRR abs/2312.00128 (2023) - [i26]Luke McDermott, Jason Weitz, Dmitri Demler, Daniel Cummings, Nhan Tran, Javier M. Duarte:
Neural Architecture Codesign for Fast Bragg Peak Analysis. CoRR abs/2312.05978 (2023) - [i25]Chenwei Xu, Jerry Yao-Chieh Hu, Aakaash Narayanan, Mattson Thieme, Vladimir Nagaslaev, Mark Austin, Jeremy Arnold, Jose Berlioz, Pierrick Hanlet, Aisha Ibrahim, Dennis Nicklaus, Jovan Mitrevski, Jason Michael St. John, Gauri Pradhan, Andrea Saewert, Kiyomi Seiya, Brian Schupbach, Randy Thurman-Keup, Nhan Tran, Rui Shi, Seda Ogrenci, Alexis Maya-Isabelle Shuping, Kyle J. Hazelwood, Han Liu:
Beyond PID Controllers: PPO with Neuralized PID Policy for Proton Beam Intensity Control in Mu2e. CoRR abs/2312.17372 (2023) - 2022
- [j10]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) - [j9]Naif Tarafdar, Giuseppe Di Guglielmo, Philip C. Harris, Jeffrey D. Krupa, Vladimir Loncar, Dylan S. Rankin, Nhan Tran, Zhenbin Wu, Qianfeng Shen, Paul Chow:
AIgean: An Open Framework for Deploying Machine Learning on Heterogeneous Clusters. ACM Trans. Reconfigurable Technol. Syst. 15(3): 23:1-23:32 (2022) - [i24]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 abs/2203.16255 (2022) - [i23]Alessandro Pappalardo, Yaman Umuroglu, Michaela Blott, Jovan Mitrevski, Benjamin Hawks, Nhan Tran, Vladimir Loncar, Sioni Summers, Hendrik Borras, Jules Muhizi, Matthew Trahms, Shih-Chieh Hsu, Scott Hauck, Javier M. Duarte:
QONNX: Representing Arbitrary-Precision Quantized Neural Networks. CoRR abs/2206.07527 (2022) - [i22]Hendrik Borras, Giuseppe Di Guglielmo, Javier M. Duarte, Nicolò Ghielmetti, Benjamin Hawks, Scott Hauck, Shih-Chieh Hsu, Ryan Kastner, Jason Liang, Andres Meza, Jules Muhizi, Tai Nguyen, Rushil Roy, Nhan Tran, Yaman Umuroglu, Olivia Weng, Aidan Yokuda, Michaela Blott:
Open-source FPGA-ML codesign for the MLPerf Tiny Benchmark. CoRR abs/2206.11791 (2022) - [i21]Javier M. Duarte, Nhan Tran, Benjamin Hawks, Christian Herwig, Jules Muhizi, Shvetank Prakash, Vijay Janapa Reddi:
FastML Science Benchmarks: Accelerating Real-Time Scientific Edge Machine Learning. CoRR abs/2207.07958 (2022) - [i20]David Xu, A. Baris Özgüler, Giuseppe Di Guglielmo, Nhan Tran, Gabriel N. Perdue, Luca P. Carloni, Farah Fahim:
Neural network accelerator for quantum control. CoRR abs/2208.02645 (2022) - 2021
- [j8]Benjamin Hawks, Javier M. Duarte, Nicholas J. Fraser, Alessandro Pappalardo, Nhan Tran, Yaman Umuroglu:
Ps and Qs: Quantization-Aware Pruning for Efficient Low Latency Neural Network Inference. Frontiers Artif. Intell. 4: 676564 (2021) - [j7]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:
Compressing deep neural networks on FPGAs to binary and ternary precision with hls4ml. Mach. Learn. Sci. Technol. 2(1): 15001 (2021) - [j6]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:
GPU coprocessors as a service for deep learning inference in high energy physics. Mach. Learn. Sci. Technol. 2(3): 35005 (2021) - [j5]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:
Fast convolutional neural networks on FPGAs with hls4ml. Mach. Learn. Sci. Technol. 2(4): 45015 (2021) - [c24]Jared Hamilton, Thao Phung, Nhan Tran, Tom Williams:
What's The Point?: Tradeoffs Between Effectiveness and Social Perception When Using Mixed Reality to Enhance Gesturally Limited Robots. HRI 2021: 177-186 - [c23]Nhan Tran, Trevor Grant, Thao Phung, Leanne M. Hirshfield, Christopher D. Wickens, Tom Williams:
Get This!? Mixed Reality Improves Robot Communication Regardless of Mental Workload. HRI (Companion) 2021: 412-416 - [c22]Colby R. Banbury, Vijay Janapa Reddi, Peter Torelli, Nat Jeffries, Csaba Király, Jeremy Holleman, Pietro Montino, David Kanter, Pete Warden, Danilo Pau, Urmish Thakker, Antonio Torrini, Jay Cordaro, Giuseppe Di Guglielmo, Javier M. Duarte, Honson Tran, Nhan Tran, Wenxu Niu, Xuesong Xu:
MLPerf Tiny Benchmark. NeurIPS Datasets and Benchmarks 2021 - [c21]Nhan Tran, Trevor Grant, Thao Phung, Leanne M. Hirshfield, Christopher D. Wickens, Tom Williams:
Robot-Generated Mixed Reality Gestures Improve Human-Robot Interaction. ICSR 2021: 768-773 - [i19]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:
Fast convolutional neural networks on FPGAs with hls4ml. CoRR abs/2101.05108 (2021) - [i18]Benjamin Hawks, Javier M. Duarte, Nicholas J. Fraser, Alessandro Pappalardo, Nhan Tran, Yaman Umuroglu:
Ps and Qs: Quantization-aware pruning for efficient low latency neural network inference. CoRR abs/2102.11289 (2021) - [i17]Farah Fahim, Benjamin Hawks, Christian Herwig, James Hirschauer, Sergo Jindariani, Nhan Tran, Luca P. Carloni, Giuseppe Di Guglielmo, Philip C. Harris, Jeffrey D. Krupa, Dylan S. Rankin, Manuel Blanco Valentin, Josiah D. Hester, Yingyi Luo, John Mamish, Seda Ogrenci Memik, Thea Aarrestad, Hamza Javed, Vladimir Loncar, Maurizio Pierini, Adrian Alan Pol, Sioni Summers, Javier M. Duarte, Scott Hauck, Shih-Chieh Hsu, Jennifer Ngadiuba, Mia Liu, Duc Hoang, Edward Kreinar, Zhenbin Wu:
hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices. CoRR abs/2103.05579 (2021) - [i16]Giuseppe Di Guglielmo, Farah Fahim, Christian Herwig, Manuel Blanco Valentin, Javier M. Duarte, Cristian Gingu, Philip C. Harris, James Hirschauer, Martin Kwok, Vladimir Loncar, Yingyi Luo, Llovizna Miranda, Jennifer Ngadiuba, Daniel Noonan, Seda Ogrenci-Memik, Maurizio Pierini, Sioni Summers, Nhan Tran:
A reconfigurable neural network ASIC for detector front-end data compression at the HL-LHC. CoRR abs/2105.01683 (2021) - [i15]Colby R. Banbury, Vijay Janapa Reddi, Peter Torelli, Jeremy Holleman, Nat Jeffries, Csaba Király, Pietro Montino, David Kanter, Sebastian Ahmed, Danilo Pau, Urmish Thakker, Antonio Torrini, Pete Warden, Jay Cordaro, Giuseppe Di Guglielmo, Javier M. Duarte, Stephen Gibellini, Videet Parekh, Honson Tran, Nhan Tran, Wenxu Niu, Xuesong Xu:
MLPerf Tiny Benchmark. CoRR abs/2106.07597 (2021) - [i14]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) - 2020
- [j4]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:
Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics. Frontiers Big Data 3: 598927 (2020) - [j3]Michael Wang, Tingjun Yang, Maria Acosta Flechas, Philip C. Harris, Benjamin Hawks, Burt Holzman, Kyle Knoepfel, Jeffrey D. Krupa, Kevin Pedro, Nhan Tran:
GPU-Accelerated Machine Learning Inference as a Service for Computing in Neutrino Experiments. Frontiers Big Data 3: 604083 (2020) - [c20]Naif Tarafdar, Giuseppe Di Guglielmo, Philip C. Harris, Jeffrey D. Krupa, Vladimir Loncar, Dylan S. Rankin, Nhan Tran, Zhenbin Wu, Qianfeng Shen, Paul Chow:
AIgean: An Open Framework for Machine Learning on Heterogeneous Clusters. FCCM 2020: 239 - [c19]Tom Williams, Leanne M. Hirshfield, Nhan Tran, Trevor Grant, Nicholas Woodward:
Using Augmented Reality to Better Study Human-Robot Interaction. HCI (10) 2020: 643-654 - [c18]Nhan Tran:
Adapting Mixed Reality Robot Communication to Mental Workload. HRI (Companion) 2020: 609-611 - [c17]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:
FPGAs-as-a-Service Toolkit (FaaST). H2RC@SC 2020: 38-47 - [i13]Sioni Summers, Giuseppe Di Guglielmo, Javier M. Duarte, Philip C. Harris, Duc Hoang, Sergo Jindariani, Edward Kreinar, Vladimir Loncar, Jennifer Ngadiuba, Maurizio Pierini, Dylan S. Rankin, Nhan Tran, Zhenbin Wu:
Fast inference of Boosted Decision Trees in FPGAs for particle physics. CoRR abs/2002.02534 (2020) - [i12]Giuseppe Di Guglielmo, Javier M. Duarte, Philip C. Harris, Duc Hoang, Sergo Jindariani, Edward Kreinar, Mia Liu, Vladimir Loncar, Jennifer Ngadiuba, Kevin Pedro, Maurizio Pierini, Dylan S. Rankin, Sheila Sagear, Sioni Summers, Nhan Tran, Zhenbin Wu:
Compressing deep neural networks on FPGAs to binary and ternary precision with HLS4ML. CoRR abs/2003.06308 (2020) - [i11]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:
GPU coprocessors as a service for deep learning inference in high energy physics. CoRR abs/2007.10359 (2020) - [i10]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:
Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics. CoRR abs/2008.03601 (2020) - [i9]Michael Wang, Tingjun Yang, Maria Acosta Flechas, Philip C. Harris, Benjamin Hawks, Burt Holzman, Kyle Knoepfel, Jeffrey D. Krupa, Kevin Pedro, Nhan Tran:
GPU-accelerated machine learning inference as a service for computing in neutrino experiments. CoRR abs/2009.04509 (2020) - [i8]Dylan Sheldon 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:
FPGAs-as-a-Service Toolkit (FaaST). CoRR abs/2010.08556 (2020) - [i7]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
- 2019
- [j2]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:
FPGA-Accelerated Machine Learning Inference as a Service for Particle Physics Computing. Comput. Softw. Big Sci. 3(1) (2019) - [c16]Javier M. Duarte, Song Han, Philip C. Harris, Sergo Jindariani, Edward Kreinar, Benjamin Kreis, Vladimir Loncar, Jennifer Ngadiuba, Maurizio Pierini, Dylan S. Rankin, Ryan A. Rivera, Sioni Summers, Nhan Tran, Zhenbin Wu:
Fast Inference of Deep Neural Networks for Real-time Particle Physics Applications. FPGA 2019: 305 - [c15]Tom Williams, Matthew Bussing, Sebastian Cabrol, Ian Lau, Elizabeth Boyle, Nhan Tran:
Investigating the Potential Effectiveness of Allocentric Mixed Reality Deictic Gesture. HCI (10) 2019: 178-198 - [c14]Tom Williams, Matthew Bussing, Sebastian Cabrol, Elizabeth Boyle, Nhan Tran:
Mixed Reality Deictic Gesture for Multi-Modal Robot Communication. HRI 2019: 191-201 - [i6]Duc Nguyen, Nhan Tran, Hung Le:
Improving Long Handwritten Text Line Recognition with Convolutional Multi-way Associative Memory. CoRR abs/1911.01577 (2019) - [i5]Brian Nord, Andrew J. Connolly, Jamie Kinney, Jeremy Kubica, Gautaum Narayan, Joshua E. G. Peek, Chad Schafer, Erik J. Tollerud, Camille Avestruz, Gutti Jogesh Babu, Simon Birrer, Douglas Burke, João Caldeira, Douglas A. Caldwell, Joleen K. Carlberg, Yen-Chi Chen, Chuanfei Dong, Eric D. Feigelson, V. Zach Golkhou, Vinay Kashyap, T. S. Li, Thomas Loredo, Luisa Lucie-Smith, Kaisey S. Mandel, J. R. Martínez-Galarza, Adam A. Miller, Priyamvada Natarajan, Michelle Ntampaka, Andy Ptak, David Rapetti, Lior Shamir, Aneta Siemiginowska, Brigitta M. Sipocz, Arfon M. Smith, Nhan Tran, Ricardo Vilalta, Lucianne M. Walkowicz, John ZuHone:
Algorithms and Statistical Models for Scientific Discovery in the Petabyte Era. CoRR abs/1911.02479 (2019) - [i4]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:
Response to NITRD, NCO, NSF Request for Information on "Update to the 2016 National Artificial Intelligence Research and Development Strategic Plan". CoRR abs/1911.05796 (2019) - 2018
- [c13]Tom Williams, Nhan Tran, Josh Rands, Neil T. Dantam:
Augmented, Mixed, and Virtual Reality Enabling of Robot Deixis. HCI (9) 2018: 257-275 - [i3]Javier M. Duarte, Song Han, Philip C. Harris, Sergo Jindariani, Edward Kreinar, Benjamin Kreis, Jennifer Ngadiuba, Maurizio Pierini, Ryan A. Rivera, Nhan Tran, Zhenbin Wu:
Fast inference of deep neural networks in FPGAs for particle physics. CoRR abs/1804.06913 (2018) - 2017
- [c12]Siddhartha Joshi, Dawei Li, Seda Ogrenci Memik, Grzegorz Deptuch, James Hoff, Sergo Jindariani, Tiehui Liu, Jamieson Olsen, Nhan Tran:
A content addressable memory with multi-Vdd scheme for low power tunable operation. MWSCAS 2017: 401-404 - [c11]Nhan Tran, Muthana Zouri, Alexander Ferworn:
Computational Public Safety: The Evolution to Public Safety Research. NBiS 2017: 385-394 - [i2]Oliver Gutsche, Matteo Cremonesi, Peter Elmer, Bo Jayatilaka, Jim Kowalkowski, Jim Pivarski, Saba Sehrish, Cristina Mantilla Suarez, Alexey Svyatkovskiy, Nhan Tran:
Big Data in HEP: A comprehensive use case study. CoRR abs/1703.04171 (2017) - [i1]Nhan Tran:
Applications of potential theoretic mother bodies in Electrostatics. CoRR abs/1710.06991 (2017) - 2015
- [c10]Dawei Li, Siddhartha Joshi, Seda Ogrenci Memik, James Hoff, Sergo Jindariani, Tiehui Liu, Jamieson Olsen, Nhan Tran:
A methodology for power characterization of associative memories. ICCD 2015: 491-498 - 2014
- [j1]Nhan Tran, Shun Bai, Jiawei Yang, Hosung Chun, Omid Kavehei, Yuanyuan Yang, Vijay Muktamath, David C. Ng, Hamish Meffin, Mark E. Halpern, Efstratios Skafidas:
A Complete 256-Electrode Retinal Prosthesis Chip. IEEE J. Solid State Circuits 49(3): 751-765 (2014) - 2013
- [c9]Jiawei Yang, Shun Bai, Nhan Tran, Hosung Chun, Omid Kavehei, Yuanyuan Yang, Efstratios Skafidas, Mark E. Halpern, David C. Ng, Vijay Muktamath:
A charge-balanced 4-wire interface for the interconnections of biomedical implants. BioCAS 2013: 202-205 - [c8]Hosung Chun, Omid Kavehei, Nhan Tran, Stan Skafidas:
A flexible biphasic pulse generating and accurate charge balancing stimulator with a 1μW neural recording amplifier. ISCAS 2013: 1885-1888 - 2012
- [c7]Hosung Chun, Nhan Tran, Yuanyuan Yang, Omid Kavehei, Shun Bai, Stan Skafidas:
A precise charge balancing and compliance voltage monitoring stimulator front-end for 1024-electrodes retinal prosthesis. EMBC 2012: 3001-3004 - [c6]Nhan Tran, Mark E. Halpern, Shun Bai, Efstratios Skafidas:
Crosstalk current measurements using multi-electrode arrays in saline. EMBC 2012: 3021-3024 - 2011
- [c5]Nhan Tran, Efstratios Skafidas, Jiawei Yang, Shun Bai, Meng Fu, David C. Ng, Mark E. Halpern, Iven M. Y. Mareels:
A prototype 64-electrode stimulator in 65 nm CMOS process towards a high density epi-retinal prosthesis. EMBC 2011: 6729-6732
2000 – 2009
- 2008
- [c4]Nhan Tran, Vijay Somers:
Modeling privacy compromise: visibility of individuals via DRM and RFID in ubiquitous computing. SpringSim 2008: 17 - 2007
- [c3]Alexander Ferworn, Nhan Tran, James Tran, Gerry Zarnett, Farrokh Janabi-Sharifi:
WiFi repeater deployment for improved communication in confined-space urban disaster search. SoSE 2007: 1-5 - 2004
- [c2]Chitta Baral, Karen Chancellor, Tran Hoai Nam, Nhan Tran, Anna M. Joy, Michael E. Berens:
A knowledge based approach for representing and reasoning about signaling networks. ISMB/ECCB (Supplement of Bioinformatics) 2004: 15-22 - 2003
- [c1]Chitta Baral, Karen Chancellor, Tran Hoai Nam, Nhan Tran:
Representing and reasoning about signal networks: an illustration using NF\kappaB dependent signaling pathways. CSB 2003: 623-628
Coauthor Index
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