Daniel Addo (PhD)
Full-time: Software Engineering
Daniel Addo (Ph.D.) earned his Bachelor of Science in Computer Science from Valley View University in 2013 and a Master of Science in Information and Communication Technology from the Ghana Institute of Management and Public Administration (GIMPA) in 2016. He completed his PhD in Software Engineering at the University of Electronic Science and Technology of China (UESTC) in 2024, where his doctoral research applied deep learning to medical imaging, particularly in segmentation and analysis tasks. His work has been published in respected outlets such as IEEE Transactions on Geoscience and Remote Sensing, Biocybernetics and Biomedical Engineering, Journal of King Saud University – Computer and Information Sciences, and the Journal of Advanced Research. Following his PhD, Daniel joined the Research Center of Intelligent Software and Perceptual Computing at UESTC as a Postdoctoral Researcher. His current research interests lie at the intersection of medical imaging and machine learning, with a growing focus on traditional drug discovery where he seeks to leverage AI-driven models to accelerate biomedical insights. His teaching and research philosophy emphasize active and inclusive learning, bridging rigorous technical foundations with practical and ethically grounded applications. Committed to interdisciplinary collaboration, Daniel continues to mentor students and contribute to advancing intelligent software systems, with the goal of translating research into impactful healthcare and societal solutions.
- Programming for Computer Science
- Object Oriented Programming
- Deep Learning
- Application of machine learning to medical imaging
- Traditional Drug Discovery
- Natural language processing
- Towards Accurate Alzheimer’s Disease Diagnosis: Integrating Focused Linear Attention in Deep Learning Frameworks (http://dx.doi.org/10.1109/IDAP64064.2024.10710769)
- Scalable deep learning framework for sentiment analysis prediction for online movie reviews (http://dx.doi.org/10.1016/j.heliyon.2024.e30756)
- A hybrid lightweight breast cancer classification framework using the histopathological images (http://dx.doi.org/10.1016/j.bbe.2023.12.003), A Hybrid Explainable Ensemble Transformer Encoder for Pneumonia Identification from Chest X-ray Images (http://dx.doi.org/10.1016/j.jare.2022.08.021)
- PiCovS: Pixel-Level With Covariance Pooling Feature and Superpixel-Level Feature Fusion for Hyperspectral Image Classification (http://dx.doi.org/10.1109/TGRS.2023.3322641)
- Doctor of Engineering in Software Engineering, University of Electronic Science and Technology of China
- Master of Science in Information and Communication Technology, GIMPA
- Bachelor of Science in Computer Science, Valley View University
