Nhan Tran

According to our database, Nhan Tran authored at least 65 papers between 2003 and 2024.

Collaborative distances:

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

2024

Architectural Implications of Neural Network Inference for High Data-Rate, Low-Latency Scientific Applications.

Olivia Weng, Alexander Redding, Nhan Tran, Javier Mauricio Duarte, Ryan Kastner

CoRR, 2024

Robust Anomaly Detection for Particle Physics Using Multi-Background Representation Learning.

Abhijith Gandrakota, Lily H. Zhang, Aahlad Manas Puli, Kyle Cranmer, Jennifer Ngadiuba, Rajesh Ranganath, Nhan Tran

CoRR, 2024

2023

Differentiable Earth mover's distance for data compression at the high-luminosity LHC.

Rohan Shenoy, Javier M. Duarte, Christian Herwig, James Hirschauer, Daniel Noonan, Maurizio Pierini, Nhan Tran, Cristina Mantilla Suarez

Mach. Learn. Sci. Technol., December, 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

Beyond PID Controllers: PPO with Neuralized PID Policy for Proton Beam Intensity Control in Mu2e.

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

CoRR, 2023

Neural Architecture Codesign for Fast Bragg Peak Analysis.

Luke McDermott, Jason Weitz, Dmitri Demler, Daniel Cummings, Nhan Tran, Javier M. Duarte

CoRR, 2023

Low latency optical-based mode tracking with machine learning deployed on FPGAs on a tokamak.

Yumou Wei, Ryan F. Forelli, Chris Hansen, Jeffrey P. Levesque, Nhan Tran, Joshua C. Agar, Giuseppe Di Guglielmo, Michael E. Mauel, Gerald A. Navratil

CoRR, 2023

End-to-end codesign of Hessian-aware quantized neural networks for FPGAs and ASICs.

Javier Campos, Zhen Dong, Javier M. Duarte, Amir Gholami, Michael W. Mahoney, Jovan Mitrevski, Nhan Tran

CoRR, 2023

A 3D Implementation of Convolutional Neural Network for Fast Inference.

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

Proceedings of the IEEE International Symposium on Circuits and Systems, 2023

On-Sensor Data Filtering using Neuromorphic Computing for High Energy Physics Experiments.

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

Proceedings of the 2023 International Conference on Neuromorphic Systems, 2023

Structural Re-weighting Improves Graph Domain Adaptation.

Shikun Liu, Tianchun Li, Yongbin Feng, Nhan Tran, Han Zhao, Qiang Qiu, Pan Li

Proceedings of the International Conference on Machine Learning, 2023

Now Look Here! Đownarrow Mixed Reality Improves Robot Communication Without Cognitive Overload.

Nhan Tran, Trevor Grant, Thao Phung, Leanne M. Hirshfield, Christopher D. Wickens, Tom Williams

Proceedings of the Virtual, Augmented and Mixed Reality, 2023

2022

<i>AIgean</i>: An Open Framework for Deploying Machine Learning on Heterogeneous Clusters.

Naif Tarafdar, Giuseppe Di Guglielmo, Philip C. Harris, Jeffrey D. Krupa, Vladimir Loncar, Dylan S. Rankin, Nhan Tran, Zhenbin Wu, Qianfeng Shen, Paul Chow

ACM Trans. Reconfigurable Technol. Syst., 2022

Applications and Techniques for Fast Machine Learning in Science.

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

Frontiers Big Data, 2022

Neural network accelerator for quantum control.

David Xu, A. Baris Özgüler, Giuseppe Di Guglielmo, Nhan Tran, Gabriel N. Perdue, Luca P. Carloni, Farah Fahim

CoRR, 2022

FastML Science Benchmarks: Accelerating Real-Time Scientific Edge Machine Learning.

Javier M. Duarte, Nhan Tran, Benjamin Hawks, Christian Herwig, Jules Muhizi, Shvetank Prakash, Vijay Janapa Reddi

CoRR, 2022

Open-source FPGA-ML codesign for the MLPerf Tiny Benchmark.

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

CoRR, 2022

QONNX: Representing Arbitrary-Precision Quantized Neural Networks.

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

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

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

Ps and Qs: Quantization-Aware Pruning for Efficient Low Latency Neural Network Inference.

Benjamin Hawks, Javier M. Duarte, Nicholas J. Fraser, Alessandro Pappalardo, Nhan Tran, Yaman Umuroglu

Frontiers Artif. Intell., 2021

Applications and Techniques for Fast Machine Learning in Science.

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

CoRR, 2021

MLPerf Tiny Benchmark.

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

CoRR, 2021

A reconfigurable neural network ASIC for detector front-end data compression at the HL-LHC.

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

CoRR, 2021

hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices.

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

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

Robot-Generated Mixed Reality Gestures Improve Human-Robot Interaction.

Nhan Tran, Trevor Grant, Thao Phung, Leanne M. Hirshfield, Christopher D. Wickens, Tom Williams

Proceedings of the Social Robotics - 13th International Conference, 2021

MLPerf Tiny Benchmark.

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

Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

Get This!? Mixed Reality Improves Robot Communication Regardless of Mental Workload.

Nhan Tran, Trevor Grant, Thao Phung, Leanne M. Hirshfield, Christopher D. Wickens, Tom Williams

Proceedings of the Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction, 2021

What's The Point?: Tradeoffs Between Effectiveness and Social Perception When Using Mixed Reality to Enhance Gesturally Limited Robots.

Jared Hamilton, Thao Phung, Nhan Tran, Tom Williams

Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction, 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

Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs.

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

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

Fast inference of Boosted Decision Trees in FPGAs for particle physics.

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

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

Adapting Mixed Reality Robot Communication to Mental Workload.

Nhan Tran

Proceedings of the Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, 2020

Using Augmented Reality to Better Study Human-Robot Interaction.

Tom Williams, Leanne M. Hirshfield, Nhan Tran, Trevor Grant, Nicholas Woodward

Proceedings of the Virtual, Augmented and Mixed Reality. Design and Interaction, 2020

AIgean: An Open Framework for Machine Learning on Heterogeneous Clusters.

Naif Tarafdar, Giuseppe Di Guglielmo, Philip C. Harris, Jeffrey D. Krupa, Vladimir Loncar, Dylan S. Rankin, Nhan Tran, Zhenbin Wu, Qianfeng Shen, Paul Chow

Proceedings of the 28th IEEE Annual International Symposium on Field-Programmable Custom Computing Machines, 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

Algorithms and Statistical Models for Scientific Discovery in the Petabyte Era.

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

CoRR, 2019

Improving Long Handwritten Text Line Recognition with Convolutional Multi-way Associative Memory.

Duc Nguyen, Nhan Tran, Hung Le

CoRR, 2019

Mixed Reality Deictic Gesture for Multi-Modal Robot Communication.

Tom Williams, Matthew Bussing, Sebastian Cabrol, Elizabeth Boyle, Nhan Tran

Proceedings of the 14th ACM/IEEE International Conference on Human-Robot Interaction, 2019

Investigating the Potential Effectiveness of Allocentric Mixed Reality Deictic Gesture.

Tom Williams, Matthew Bussing, Sebastian Cabrol, Ian Lau, Elizabeth Boyle, Nhan Tran

Proceedings of the Virtual, Augmented and Mixed Reality. Applications and Case Studies, 2019

Fast Inference of Deep Neural Networks for Real-time Particle Physics Applications.

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

Proceedings of the 2019 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, 2019

2018

Fast inference of deep neural networks in FPGAs for particle physics.

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

CoRR, 2018

Augmented, Mixed, and Virtual Reality Enabling of Robot Deixis.

Tom Williams, Nhan Tran, Josh Rands, Neil T. Dantam

Proceedings of the Virtual, Augmented and Mixed Reality: Interaction, Navigation, Visualization, Embodiment, and Simulation, 2018

2017

Applications of potential theoretic mother bodies in Electrostatics.

Nhan Tran

CoRR, 2017

Big Data in HEP: A comprehensive use case study.

Oliver Gutsche, Matteo Cremonesi, Peter Elmer, Bo Jayatilaka, Jim Kowalkowski, Jim Pivarski, Saba Sehrish, Cristina Mantilla Suarez, Alexey Svyatkovskiy, Nhan Tran

CoRR, 2017

Computational Public Safety: The Evolution to Public Safety Research.

Nhan Tran, Muthana Zouri, Alexander Ferworn

Proceedings of the Advances in Network-Based Information Systems, 2017

A content addressable memory with multi-Vdd scheme for low power tunable operation.

Siddhartha Joshi, Dawei Li, Seda Ogrenci Memik, Grzegorz Deptuch, James Hoff, Sergo Jindariani, Tiehui Liu, Jamieson Olsen, Nhan Tran

Proceedings of the IEEE 60th International Midwest Symposium on Circuits and Systems, 2017

2015

A methodology for power characterization of associative memories.

Dawei Li, Siddhartha Joshi, Seda Ogrenci Memik, James Hoff, Sergo Jindariani, Tiehui Liu, Jamieson Olsen, Nhan Tran

Proceedings of the 33rd IEEE International Conference on Computer Design, 2015

2014

A Complete 256-Electrode Retinal Prosthesis Chip.

Nhan Tran, Shun Bai, Jiawei Yang, Hosung Chun, Omid Kavehei, Yuanyuan Yang, Vijay Muktamath, David C. Ng, Hamish Meffin, Mark E. Halpern, Efstratios Skafidas

IEEE J. Solid State Circuits, 2014

2013

A flexible biphasic pulse generating and accurate charge balancing stimulator with a 1μW neural recording amplifier.

Hosung Chun, Omid Kavehei, Nhan Tran, Stan Skafidas

Proceedings of the 2013 IEEE International Symposium on Circuits and Systems (ISCAS2013), 2013

A charge-balanced 4-wire interface for the interconnections of biomedical implants.

Jiawei Yang, Shun Bai, Nhan Tran, Hosung Chun, Omid Kavehei, Yuanyuan Yang, Efstratios Skafidas, Mark E. Halpern, David C. Ng, Vijay Muktamath

Proceedings of the 2013 IEEE Biomedical Circuits and Systems Conference (BioCAS), Rotterdam, The Netherlands, October 31, 2013

2012

Crosstalk current measurements using multi-electrode arrays in saline.

Nhan Tran, Mark E. Halpern, Shun Bai, Efstratios Skafidas

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2012

A precise charge balancing and compliance voltage monitoring stimulator front-end for 1024-electrodes retinal prosthesis.

Hosung Chun, Nhan Tran, Yuanyuan Yang, Omid Kavehei, Shun Bai, Stan Skafidas

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2012

2011

A prototype 64-electrode stimulator in 65 nm CMOS process towards a high density epi-retinal prosthesis.

Nhan Tran, Efstratios Skafidas, Jiawei Yang, Shun Bai, Meng Fu, David C. Ng, Mark E. Halpern, Iven M. Y. Mareels

Proceedings of the 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2011

2008

Modeling privacy compromise: visibility of individuals via DRM and RFID in ubiquitous computing.

Nhan Tran, Vijay Somers

Proceedings of the 2008 Spring Simulation Multiconference, 2008

2007

WiFi repeater deployment for improved communication in confined-space urban disaster search.

Alexander Ferworn, Nhan Tran, James Tran, Gerry Zarnett, Farrokh Janabi-Sharifi

Proceedings of the 2nd IEEE International Conference on System of Systems Engineering, 2007

2004

A knowledge based approach for representing and reasoning about signaling networks.

Chitta Baral, Karen Chancellor, Tran Hoai Nam, Nhan Tran, Anna M. Joy, Michael E. Berens

Proceedings of the Proceedings Twelfth International Conference on Intelligent Systems for Molecular Biology/Third European Conference on Computational Biology 2004, 2004

2003

Representing and reasoning about signal networks: an illustration using NF\kappaB dependent signaling pathways.

Chitta Baral, Karen Chancellor, Tran Hoai Nam, Nhan Tran

Proceedings of the 2nd IEEE Computer Society Bioinformatics Conference, 2003