Hai Jiang
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Professor
Also affiliated: Wayne State University (2002–2004); The University of Texas at Arlington (2010); UL Research Institutes (2018); Software (Spain) (2023)
Formerly Arkansas Affiliated with Arkansas State University through 2024; recent publications list Beijing University of Posts and Telecommunications.
Faculty Researcher
Research Areas
Links
Biography and Research Information
OverviewAI-generated summary
Hai Jiang's research focuses on advanced computing techniques, particularly in the areas of deep learning, blockchain technology, and high-performance computing systems. He has investigated malware detection using autoencoders and explored the application of blockchain for Internet-of-Things and cyber-physical systems. His work also includes the design and implementation of scalable smart contracts for platforms like Ethereum, with applications in digital certificates. Jiang has contributed to the development of heterogeneous computing systems, optimizing performance and energy efficiency using FPGA, GPU, and CPU architectures. Additionally, his research extends to fault-tolerant and scalable methods for processing vertically partitioned data. Jiang has published extensively, with a notable body of work in computer science and engineering. His scholarship metrics include an h-index of 18 and over 100 publications, indicating a sustained contribution to his fields.
Metrics
- h-index: 18
- Publications: 122
- Citations: 1,018
Selected Publications
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A fault‐tolerant and scalable boosting method over vertically partitioned data (2024)
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Guest Editorial Introduction to the Special Section on Computing and Networking for Cyber-Physical-Social Systems (2022)
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A Malware Detection Approach Using Autoencoder in Deep Learning (2022)
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High-Performance and Energy-Efficient FPGA-GPU-CPU Heterogeneous System Implementation (2021)
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A FPGA-Based Heterogeneous Implementation of NTRUEncrypt (2021)
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Guest Editorial for Special Issue on Blockchain for Internet-of-Things and Cyber-Physical Systems (2021)
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A Malware Detection Approach Using Malware Images and Autoencoders (2020)
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CRState: checkpoint/restart of OpenCL program for in-kernel applications (2020)
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CRAC: An automatic assistant compiler of checkpoint/restart for OpenCL program (2020)
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A K-means algorithm based on characteristics of density applied to network intrusion detection (2020)
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CASH: correlation-aware scheduling to mitigate soft error impact on heterogeneous multicores (2020)
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CRAC: An Automatic Assistant Compiler of Checkpoint/Restart for OpenCL Program (2020)
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Generic attribute revocation systems for attribute-based encryption in cloud storage (2019)
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CRState: In-Kernel Checkpoint/Restart of OpenCL Program Execution on GPU (2019)
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GPU Acceleration of Ciphertext-Policy Attribute-Based Encryption (2019)
Collaboration Network
Top Collaborators
- A FPGA-Based Heterogeneous Implementation of NTRUEncrypt
- High-Performance and Energy-Efficient FPGA-GPU-CPU Heterogeneous System Implementation
- A FPGA-Based Heterogeneous Implementation of NTRUEncrypt
- High-Performance and Energy-Efficient FPGA-GPU-CPU Heterogeneous System Implementation
- Guest Editorial for Special Issue on Blockchain for Internet-of-Things and Cyber-Physical Systems
- Guest Editorial for Special Issue on Blockchain for Internet-of-Things and Cyber-Physical Systems
- Guest Editorial for Special Issue on Blockchain for Internet-of-Things and Cyber-Physical Systems
- Guest Editorial for Special Issue on Blockchain for Internet-of-Things and Cyber-Physical Systems
- Guest Editorial for Special Issue on Blockchain for Internet-of-Things and Cyber-Physical Systems
- Guest Editorial for Special Issue on Blockchain for Internet-of-Things and Cyber-Physical Systems
- High-Performance and Energy-Efficient FPGA-GPU-CPU Heterogeneous System Implementation
- A Malware Detection Approach Using Autoencoder in Deep Learning
- A Malware Detection Approach Using Autoencoder in Deep Learning
- A Malware Detection Approach Using Autoencoder in Deep Learning
- A Malware Detection Approach Using Autoencoder in Deep Learning
- Guest Editorial Introduction to the Special Section on Computing and Networking for Cyber-Physical-Social Systems
- Guest Editorial Introduction to the Special Section on Computing and Networking for Cyber-Physical-Social Systems
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