Eyob Abera Deboch at Shenzhen Institute of Advanced Technology, China
Professional Profile
Summary:
Mr. Deboch Eyob Abera is a highly skilled computer vision researcher with over three years of project experience. He recently graduated with a master’s degree in Information and Communication Engineering from the University of Electronic Science and Technology of China, achieving a remarkable CGPA of 4/4. His research interests lie in artificial intelligence, machine learning, and deep learning, particularly focusing on image fusion, classification, segmentation, detection, and tracking.
👩🎓Education:
- MSc in Information and Communication Engineering, University of Electronic Science and Technology of China (Sep 2021 – June 2023)
- Thesis: “Research on Image Fusion with the Deep Learning Framework”
- BEng in Electronic Information Engineering, University of Electronic Science and Technology of China (Sep 2017 – June 2021)
- Thesis: “Image Fusion Algorithms based on Machine Learning”
Professional Experience:
- Master’s Thesis (Summer 2022 – June 2023): Designed and implemented a deep learning model for infrared and visible image fusion under the guidance of Associate Prof. Qi Jin.
- School Assistant (Fall 2021): Provided assistance to students in the School of Information and Communication Engineering, addressing inquiries and guiding students.
- Bachelor Thesis (Spring 2021): Designed and Implemented Ensemble Network for Infrared and Visible image fusion under the supervision of Associate Prof. Wu Ruiqing.
- Upwork Freelancer: Completed projects including ball height detection during volleyball shots and dataset preparation.
Research Interests:
- Image fusion
- Classification
- Segmentation
- Detection
- Tracking
- Machine learning and deep learning algorithms
- Deep learning frameworks
Skills:
- Programming: Proficient in Python, MATLAB, and C/C++
- Platforms: Experienced with Windows, Linux (Debian, Ubuntu), Android, Arduino, and Raspberry Pi
- Networking: Skilled in Routing & Switching
- Deep Learning: Extensive experience with CNN, ResNet, Autoencoder, DenseNet, GAN, RNN, UNet, Ensemble learning, and transformer models
- Frameworks: Proficient in TensorFlow, PyTorch, Keras, OpenCV, and Pandas
Paper Title: Multi-scale feature fusion for prediction of IDH1 mutations in glioma histopathological images
- Authors: Liu, X.; Hu, W.; Diao, S.; Racoceanu, D.; Qin, W.
- Journal: Computer Methods and Programs in Biomedicine
- Year: 2024
Paper Title: A deep learning and image enhancement based pipeline for infrared and visible image fusion
- Authors: Qi, J.; Abera, D.E.; Fanose, M.N.; Wang, L.; Cheng, J.
- Journal: Neurocomputing
- Year: 2024
- Citations: 1
Paper Title: Automatic Image Contrast Enhancement Based on Reinforcement Learning
- Authors: Abera, D.E.; Gerezgiher, T.S.; Jin, Q.; Mesfin, G.F.
- Year: 2022