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Ming Zhao 0002
Person information
- affiliation: Arizona State University, Tempe, AZ, USA
- affiliation: Florida International University, Miami, FL, USA
Other persons with the same name
- Ming Zhao — disambiguation page
- Ming Zhao 0001 — Tsinghua University, National Laboratory for Information Science and Technology, Beijing, China
- Ming Zhao 0003 — Google (and 1 more)
- Ming Zhao 0004 — Xi'an Jiatong University, China
- Ming Zhao 0005
— Nanyang Technological University, School of Electrical and Electronic Engineering, Singapore
- Ming Zhao 0006
- Ming Zhao 0007
- Ming Zhao 0008
- Ming Zhao 0009
- Ming Zhao 0010
- Ming Zhao 0011
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2020 – today
- 2024
- [c66]Kaiqi Zhao, Ming Zhao:
Self-Supervised Quantization-Aware Knowledge Distillation. AISTATS 2024: 4375-4383 - [c65]Chongzhuo Yang
Can ZNS SSDs be Better Storage Devices for Persistent Cache? HotStorage 2024 - [i12]Kaiqi Zhao, Ming Zhao:
Self-Supervised Quantization-Aware Knowledge Distillation. CoRR abs/2403.11106 (2024) - 2023
- [j12]Ankur Chowdhary
Generative Adversarial Network (GAN)-Based Autonomous Penetration Testing for Web Applications. Sensors 23(18): 8014 (2023) - [c64]Qirui Yang, Runyu Jin, Ni Fan, Devasena Inupakutika, Bridget Davis, Ming Zhao:
AdaCache: A Disaggregated Cache System with Adaptive Block Size for Cloud Block Storage. CLOUD 2023: 348-359 - [c63]Kaiqi Zhao, Animesh Jain, Ming Zhao:
Automatic Attention Pruning: Improving and Automating Model Pruning using Attentions. AISTATS 2023: 10470-10486 - [c62]Runyu Jin, Qirui Yang, Ming Zhao:
When Edge Meets FaaS: Opportunities and Challenges. EDGE 2023: 23-25 - [c61]Kaiqi Zhao, Yitao Chen, Ming Zhao:
A Contrastive Knowledge Transfer Framework for Model Compression and Transfer Learning. ICASSP 2023: 1-5 - [c60]Kaiqi Zhao
Poster: Self-Supervised Quantization-Aware Knowledge Distillation. SEC 2023: 250-252 - [c59]Ming Zhao, Kritshekhar Jha, Sungho Hong:
GPU-enabled Function-as-a-Service for Machine Learning Inference. IPDPS 2023: 918-928 - [i11]Ming Zhao, Kritshekhar Jha, Sungho Hong:
GPU-enabled Function-as-a-Service for Machine Learning Inference. CoRR abs/2303.05601 (2023) - [i10]Kaiqi Zhao, Yitao Chen, Ming Zhao:
A Contrastive Knowledge Transfer Framework for Model Compression and Transfer Learning. CoRR abs/2303.07599 (2023) - [i9]Kaiqi Zhao, Animesh Jain, Ming Zhao:
Automatic Attention Pruning: Improving and Automating Model Pruning using Attentions. CoRR abs/2303.08595 (2023) - [i8]Qirui Yang, Runyu Jin, Ni Fan, Devasena Inupakutika, Bridget Davis, Ming Zhao:
AdaCache: A Disaggregated Cache System with Adaptive Block Size for Cloud Block Storage. CoRR abs/2306.17254 (2023) - [i7]Runyu Jin, Qirui Yang, Ming Zhao:
When Edge Meets FaaS: Opportunities and Challenges. CoRR abs/2307.06397 (2023) - [i6]Kaiqi Zhao, Ming Zhao:
Poster: Self-Supervised Quantization-Aware Knowledge Distillation. CoRR abs/2309.13220 (2023) - 2022
- [c58]Sang-Hun Sim, Tara Paranjpe, Nicole Roberts, Ming Zhao:
Exploring Edge Machine Learning-based Stress Prediction using Wearable Devices. ICMLA 2022: 1266-1273 - [c57]Kaiqi Zhao, Hieu Nguyen, Animesh Jain, Nathan Susanj, Athanasios Mouchtaris, Lokesh Gupta, Ming Zhao:
Knowledge Distillation via Module Replacing for Automatic Speech Recognition with Recurrent Neural Network Transducer. INTERSPEECH 2022: 4436-4440 - [c56]Qirui Yang, Runyu Jin, Bridget Davis, Devasena Inupakutika, Ming Zhao:
Performance Evaluation on CXL-enabled Hybrid Memory Pool. NAS 2022: 1-5 - [i5]Kaiqi Zhao, Animesh Jain, Ming Zhao:
Iterative Activation-based Structured Pruning. CoRR abs/2201.09881 (2022) - [i4]Kaiqi Zhao, Animesh Jain, Ming Zhao:
Adaptive Activation-based Structured Pruning. CoRR abs/2201.10520 (2022) - [i3]Kaiqi Zhao, Yitao Chen, Ming Zhao:
Enabling Deep Learning on Edge Devices through Filter Pruning and Knowledge Transfer. CoRR abs/2201.10947 (2022) - 2021
- [j11]Saman Biookaghazadeh, Pravin Kumar Ravi, Ming Zhao:
Toward Multi-FPGA Acceleration of the Neural Networks. ACM J. Emerg. Technol. Comput. Syst. 17(2): 25:1-25:23 (2021) - [c55]Saman Biookaghazadeh, Fengbo Ren, Ming Zhao:
Characterizing Loop Acceleration in Heterogeneous Computing. CLOUD 2021: 445-455 - [c54]Liana V. Rodriguez, Farzana Beente Yusuf, Steven Lyons, Eysler Paz, Raju Rangaswami, Jason Liu, Ming Zhao, Giri Narasimhan:
Learning Cache Replacement with CACHEUS. FAST 2021: 341-354 - [c53]Eugene N. Kuznetsov, Yitao Chen, Ming Zhao:
SecureFL: Privacy Preserving Federated Learning with SGX and TrustZone. SEC 2021: 55-67 - 2020
- [j10]Jianyu Fu
COWCache: effective flash caching for Copy-on-Write virtual disks. Clust. Comput. 23(2): 623-639 (2020) - [e3]Irfan Ahmad, Ming Zhao:
3rd USENIX Workshop on Hot Topics in Edge Computing, HotEdge 2020, June 25-26, 2020. USENIX Association 2020 [contents] - [i2]Qirui Yang, Runyu Jin, Nabil Gandhi, Xiongzi Ge, Hoda Aghaei Khouzani, Ming Zhao:
EdgeBench: A Workflow-based Benchmark for Edge Computing. CoRR abs/2010.14027 (2020)
2010 – 2019
- 2019
- [j9]Vasily Tarasov, Lukas Rupprecht, Dimitris Skourtis, Wenji Li, Raju Rangaswami, Ming Zhao:
Evaluating Docker storage performance: from workloads to graph drivers. Clust. Comput. 22(4): 1159-1172 (2019) - [j8]Pengfei Zuo, Yu Hua, Ming Zhao, Wen Zhou, Yuncheng Guo:
Write Deduplication and Hash Mode Encryption for Secure Nonvolatile Main Memory. IEEE Micro 39(1): 44-51 (2019) - [j7]George Amvrosiadis, Ali Raza Butt, Vasily Tarasov, Erez Zadok, Ming Zhao:
Selected Results of the Workshop on Data Storage Research 2025. login Usenix Mag. 44(4) (2019) - [c52]Yitao Chen, Saman Biookaghazadeh, Ming Zhao
Exploring the capabilities of mobile devices in supporting deep learning. SEC 2019: 127-138 - [c51]Yitao Chen, Kaiqi Zhao, Baoxin Li, Ming Zhao:
Exploring the Use of Synthetic Gradients for Distributed Deep Learning across Cloud and Edge Resources. HotEdge 2019 - [c50]Ming Zhao, Yiqi Xu:
vPFS+: Managing I/O Performance for Diverse HPC Applications. MSST 2019: 51-64 - [c49]Saman Biookaghazadeh, Yitao Chen, Kaiqi Zhao, Ming Zhao:
KnowledgeNet: Disaggregated and Distributed Training and Serving of Deep Neural Networks. OpML 2019: 47-49 - [c48]Douglas Otstott, Ming Zhao, Sean Williams, Latchesar Ionkov, Michael Lang:
A Foundation for Automated Placement of Data. PDSW@SC 2019: 50-59 - [c47]Qirui Yang, Runyu Jin, Ming Zhao:
SmartDedup: Optimizing Deduplication for Resource-constrained Devices. USENIX ATC 2019: 633-646 - 2018
- [c46]Ragini Sharma, Saman Biookaghazadeh, Baoxin Li, Ming Zhao
Are Existing Knowledge Transfer Techniques Effective for Deep Learning with Edge Devices? EDGE 2018: 42-49 - [c45]Ming Zhao, Jorge Cabrera:
RTVirt: enabling time-sensitive computing on virtualized systems through cross-layer CPU scheduling. EuroSys 2018: 27:1-27:13 - [c44]Saman Biookaghazadeh, Ming Zhao, Fengbo Ren:
Are FPGAs Suitable for Edge Computing? HotEdge 2018 - [c43]Giuseppe Vietri, Liana V. Rodriguez, Wendy A. Martinez, Steven Lyons, Jason Liu, Raju Rangaswami, Ming Zhao, Giri Narasimhan:
Driving Cache Replacement with ML-based LeCaR. HotStorage 2018 - [c42]Ragini Sharma, Saman Biookaghazadeh, Ming Zhao:
Are Existing Knowledge Transfer Techniques Effective For Deep Learning on Edge Devices? HPDC (Posters/Doctoral Consortium) 2018: 15-16 - [c41]Yitao Chen, Saman Biookaghazadeh, Ming Zhao
Exploring the Capabilities of Mobile Devices Supporting Deep Learning. HPDC (Posters/Doctoral Consortium) 2018: 17-18 - [c40]Jianyu Fu, Dulcardo Arteaga, Ming Zhao:
Locality-driven MRC Construction and Cache Allocation. HPDC (Posters/Doctoral Consortium) 2018: 19-20 - [c39]Ming Zhao, Lixi Wang, Yun Lv, Jing Xu:
Cross-Layer Optimization for Virtual Machine Resource Management. IC2E 2018: 90-98 - [c38]Pengfei Zuo, Yu Hua, Ming Zhao
Improving the Performance and Endurance of Encrypted Non-Volatile Main Memory through Deduplicating Writes. MICRO 2018: 442-454 - [e2]Ming Zhao, Abhishek Chandra, Lavanya Ramakrishnan:
Proceedings of the 27th International Symposium on High-Performance Parallel and Distributed Computing, HPDC 2018, Tempe, AZ, USA, June 11-15, 2018. ACM 2018 [contents] - [i1]Saman Biookaghazadeh, Fengbo Ren, Ming Zhao:
Are FPGAs Suitable for Edge Computing? CoRR abs/1804.06404 (2018) - 2017
- [j6]Douglas Otstott
TCASM: An asynchronous shared memory interface for high-performance application composition. Parallel Comput. 63: 61-78 (2017) - [c37]Saman Biookaghazadeh, Shujia Zhou, Ming Zhao:
Kaleido: Enabling Efficient Scientific Data Processing on Big-Data Systems. NAS 2017: 1-10 - [c36]Vasily Tarasov, Lukas Rupprecht, Dimitris Skourtis, Amit Warke, Dean Hildebrand, Mohamed Mohamed, NagaPramod Mandagere, Wenji Li, Raju Rangaswami, Ming Zhao:
In Search of the Ideal Storage Configuration for Docker Containers. FAS*W@SASO/ICCAC 2017: 199-206 - 2016
- [j5]Hector A. Duran-Limon, Jesus Flores-Contreras, Nikos Parlavantzas, Ming Zhao, Angel Meulenert-Peña:
Efficient execution of the WRF model and other HPC applications in the cloud. Earth Sci. Informatics 9(3): 365-382 (2016) - [c35]Wenji Li, Gregory Jean-Baptise, Juan Riveros, Giri Narasimhan, Tony Zhang, Ming Zhao:
CacheDedup: In-line Deduplication for Flash Caching. FAST 2016: 301-314 - [c34]Dulcardo Arteaga, Jorge Cabrera, Jing Xu, Swaminathan Sundararaman, Ming Zhao:
CloudCache: On-demand Flash Cache Management for Cloud Computing. FAST 2016: 355-369 - [c33]Yiqi Xu, Ming Zhao:
IBIS: Interposed Big-data I/O Scheduler. HPDC 2016: 111-122 - 2015
- [c32]Saman Biookaghazadeh, Yiqi Xu, Shujia Zhou, Ming Zhao:
Enabling scientific data storage and processing on big-data systems. IEEE BigData 2015: 1978-1984 - [c31]Lixi Wang, Jing Xu, Hector A. Duran-Limon, Ming Zhao:
QoS-Driven Cloud Resource Management through Fuzzy Model Predictive Control. ICAC 2015: 81-90 - [c30]Sajib Kundu, Raju Rangaswami, Ming Zhao, Ajay Gulati, Kaushik Dutta:
Revenue Driven Resource Allocation for Virtualized Data Centers. ICAC 2015: 197-206 - 2014
- [j4]Yun Lu, Ming Zhao, Lixi Wang, Naphtali Rishe:
v-TerraFly: large scale distributed spatial data visualization with autonomic resource management. J. Big Data 1: 4 (2014) - [c29]Michel Angelo Roger, Yiqi Xu, Ming Zhao:
BigCache for big-data systems. IEEE BigData 2014: 189-194 - [c28]Douglas Otstott, Noah Evans, Latchesar Ionkov, Ming Zhao, Michael Lang
Enabling composite applications through an asynchronous shared memory interface. IEEE BigData 2014: 219-224 - [c27]Dulcardo Arteaga, Ming Zhao:
Client-side Flash Caching for Cloud Systems. SYSTOR 2014: 7:1-7:11 - 2013
- [c26]Ricardo Koller, Leonardo Mármol, Raju Rangaswami, Swaminathan Sundararaman, Nisha Talagala, Ming Zhao:
Write policies for host-side flash caches. FAST 2013: 45-58 - [c25]Yiqi Xu, Ming Zhao:
Two-level Throughput and Latency IO Control for Parallel File Systems. Feedback Computing 2013 - [c24]Yiqi Xu, Adrian Suarez, Ming Zhao:
IBIS: interposed big-data I/O scheduler. HPDC 2013: 109-110 - [c23]Yun Lu, Ming Zhao, Guangqiang Zhao, Lixi Wang, Naphtali Rishe:
Massive GIS Database System with Autonomic Resource Management. ICMLA (2) 2013: 451-456 - [c22]Yonggang Liu, Renato J. O. Figueiredo
On the design and implementation of a simulator for parallel file system research. MSST 2013: 1-5 - [e1]Yixin Diao, Jie Liu, Ming Zhao:
8th International Workshop on Feedback Computing, San Jose, CA, USA, June 25, 2013. USENIX Association 2013 [contents] - 2012
- [c21]Lixi Wang, Jing Xu, Ming Zhao:
Application-aware cross-layer virtual machine resource management. ICAC 2012: 13-22 - [c20]Yiqi Xu, Dulcardo Arteaga, Ming Zhao, Yonggang Liu, Renato J. O. Figueiredo
vPFS: Bandwidth virtualization of parallel storage systems. MSST 2012: 1-12 - [c19]Sajib Kundu, Raju Rangaswami, Ajay Gulati, Ming Zhao, Kaushik Dutta:
Modeling virtualized applications using machine learning techniques. VEE 2012: 3-14 - 2011
- [c18]Lixi Wang, Jing Xu, Ming Zhao, José A. B. Fortes:
Adaptive virtual resource management with fuzzy model predictive control. ICAC 2011: 191-192 - [c17]Lixi Wang, Jing Xu, Ming Zhao, Yicheng Tu, José A. B. Fortes:
Fuzzy Modeling Based Resource Management for Virtualized Database Systems. MASCOTS 2011: 32-42 - [c16]Dulcardo Arteaga, Ming Zhao:
Towards Scalable Application Checkpointing with Parallel File System Delegation. NAS 2011: 130-139 - [c15]Hector A. Duran-Limon, Luis A. Silva-Banuelos, Victor H. Tellez-Valdez, Nikos Parlavantzas, Ming Zhao:
Using Lightweight Virtual Machines to Run High Performance Computing Applications: The Case of the Weather Research and Forecasting Model. UCC 2011: 146-153 - 2010
- [c14]Sajib Kundu, Raju Rangaswami, Kaushik Dutta, Ming Zhao:
Application performance modeling in a virtualized environment. HPCA 2010: 1-10 - [c13]Jason Liu
Model-driven network emulation with virtual time machine. WSC 2010: 688-696
2000 – 2009
- 2009
- [c12]Juan C. Martinez, Lixi Wang, Ming Zhao, S. Masoud Sadjadi:
Experimental study of large-scale computing on virtualized resources. VTDC@ICAC 2009: 35-42 - [c11]Jing Xu, Ming Zhao, José A. B. Fortes:
Cooperative Autonomic Management in Dynamic Distributed Systems. SSS 2009: 756-770 - 2008
- [j3]Jing Xu, Ming Zhao, José A. B. Fortes, Robert Carpenter, Mazin S. Yousif:
Autonomic resource management in virtualized data centers using fuzzy logic-based approaches. Clust. Comput. 11(3): 213-227 (2008) - 2007
- [c10]Jing Xu, Ming Zhao, José A. B. Fortes, Robert Carpenter, Mazin S. Yousif:
On the Use of Fuzzy Modeling in Virtualized Data Center Management. ICAC 2007: 25 - [c9]Jack DiGiovanna, Loris Marchal
Towards Real-Time Distributed Signal Modeling for Brain-Machine Interfaces. International Conference on Computational Science (1) 2007: 964-971 - [c8]Ming Zhao, Renato J. O. Figueiredo
Experimental study of virtual machine migration in support of reservation of cluster resources. VTDC@SC 2007: 5:1-5:8 - [c7]Ming Zhao, Renato J. O. Figueiredo
A user-level secure grid file system. SC 2007: 45 - 2006
- [j2]Ming Zhao, Jian Zhang, Renato J. O. Figueiredo
Distributed File System Virtualization Techniques Supporting On-Demand Virtual Machine Environments for Grid Computing. Clust. Comput. 9(1): 45-56 (2006) - [c6]Ming Zhao, Jing Xu, Renato J. O. Figueiredo:
Towards Autonomic Grid Data Management with Virtualized Distributed File Systems. ICAC 2006: 209-218 - [c5]Ming Zhao, Renato J. O. Figueiredo
Application-Tailored Cache Consistency for Wide-Area File Systems. ICDCS 2006: 41 - 2005
- [j1]Sumalatha Adabala, Vineet Chadha, Puneet Chawla, Renato J. O. Figueiredo
From virtualized resources to virtual computing grids: the In-VIGO system. Future Gener. Comput. Syst. 21(6): 896-909 (2005) - [c4]Andréa M. Matsunaga
On the Use of Virtualization and Service Technologies to Enable Grid-Computing. Euro-Par 2005: 1-12 - [c3]Ming Zhao, Vineet Chadha, Renato J. O. Figueiredo:
Supporting application-tailored grid file system sessions with WSRF-based services. HPDC 2005: 24-33 - 2004
- [c2]Jithendar Paladugula, Ming Zhao, Renato J. O. Figueiredo
Support for Data-Intensive, Variable-Granularity Grid Applications via Distributed File System Virtualization - A Case Study of Light Scattering Spectroscopy. CLADE 2004: 12 - [c1]Ming Zhao, Jian Zhang, Renato J. O. Figueiredo:
Distributed File System Support for Virtual Machines in Grid Computing. HPDC 2004: 202-211
Coauthor Index
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last updated on 2024-11-11 21:23 CET by the dblp team
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