Hongcheng Jiang
I hold a Ph.D. in Electrical and Computer Engineering at the School of Computing and Engineering, University of Missouri-Kansas City (UMKC), under the mentorship of Prof. ZhiQiang Chen. My research emphasizes machine learning, deep learning, and computer vision, with extensive experience in developing and optimizing algorithms for applications in remote sensing and image enhancement.
Currently, I work as a researcher at the DIGIT Lab, USA, where I design and implement advanced machine learning models to address complex challenges in remote sensing and geospatial analytics. My expertise spans end-to-end machine learning pipelines, model deployment, and performance optimization for scalable, production-level environments.
I hold a Master’s degree in Computer Science from UMKC, where I refined my skills in algorithm development, statistical analysis, and software engineering, providing a robust foundation for my career in machine learning and AI-driven technologies.
Email / Google Scholar / LinkedIn / Github
News
Recent Projects
My work applies machine learning to overcome challenges in enhancing remote sensing images. The key focus areas are: Spatial Enhancement: Creating deep learning models for super-resolution to improve the spatial resolution and clarity of remote sensing images. Spectral Enhancement: Utilizing machine learning techniques to expand and enrich spectral information for more comprehensive data analysis. Spatial-Spectral Enhancement: Designing and optimizing neural networks to combine spatial and spectral features, generating high-quality, integrated image outputs.
Teaching
- 01/2025 - 05/2025, Graduate Instructor / E&C-ENGR 229 - Introduction to Computer Design Laboratory
- 01/2025 - 05-2025, Teaching Assistant / E&C-ENGR 330 - Electronic Circuits
- 08/2024 - 12/2024, Graduate Instructor / E&C-ENGR 227 - Logic Design Laboratory
- 08/2024 - 12-2024, Teaching Assistant / E&C-ENGR 334 - Semiconductors and Devices
- 01/2024 - 05/2024, Graduate Instructor / E&C-ENGR 229 - Introduction to Computer Design Laboratory
- 08/2023 - 12/2023, Graduate Instructor / E&C-ENGR 227 - Logic Design Laboratory
- 01/2023 - 05/2023, Graduate Instructor / E&C-ENGR 229 - Introduction to Computer Design Laboratory
- 08/2022 - 12-2022, Teaching Assistant / E&C-ENGR 5582 - Computer Version
- 01/2022 - 07-2022, Graduate Instructor / E&C-ENGR 216 - Engineering Computation
- 08/2021 - 12-2021, Graduate Instructor / COMP-SCI 291 - Discrete Structures II
- 01/2020 - 05-2020, Graduate Instructor / COMP-SCI 423 - Client/Server Programming and Applications
- 08/2018 - 12-2018, Teaching Assistant / COMP-SCI 5592 - Design and Analysis of Algorithms
- 08/2018 - 12-2018, Teaching Assistant / COMP-SCI 291 - Discrete Structures II
Research
I am passionate about developing efficient and scalable models for computer vision applications, with a focus on optimizing performance for real-world deployment. My expertise lies in leveraging deep learning techniques to design end-to-end pipelines that improve accuracy, reduce computational costs, and ensure model efficiency. I am particularly interested in solving challenges related to image processing, feature extraction, and multimodal data integration to create robust solutions for diverse applications.
Conference Papers:
- Hyperspectral Pansharpening with Transform-based Spectral Diffusion Priors
Hongcheng Jiang, ZhiQiang Chen
WACV 2025 | paper - Flexible Window-based Self-attention Transformer in Thermal Image Super-Resolution
Hongcheng Jiang, ZhiQiang Chen
CVPR 2024 | paper - DCT-Based Residual Network for NIR Image Colorization
Hongcheng Jiang, Paras Maharjan, Zhu Li, George York
IEEE ICIP 2022 | paper - Crucial data selection based on random weight neural network
Jie Ji, Hongcheng Jiang, Bin Zhao, Peng Zhai
IEEE SMC 2015 | paper
Journal Papers:
- Transformer-based Diffusion and Spectral Priors Model For Hyperspectral Pansharpening
Hongcheng Jiang, ZhiQiang Chen
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing | under revision. - Spatial-Frequency Guided Pixel Transformer for NIR-to-RGB Translation
Hongcheng Jiang, ZhiQiang Chen
Infrared Physics and Technology | paper - Fully-connected LSTM–CRF on medical concept extraction
Jie Ji, Bairui Chen, Hongcheng Jiang
IEEE MLC 2020 | paper