NATNAEL DEJENE
Snellville,GA
Summary
Computer Science graduate with advance knowledge in Python, and a focus on Machine Learning applications. Currently pursuing a Master's degree in Electrical and Computer Engineering, specializing in neural networks for Maximum PowerPoint Tracking. Experienced Data Annotator with a strong background in data labeling and enhancing machine learning algorithms through precise annotation. Proven ability to improve models and collaborate effectively in team environments.
Skills
- Python
- Java
- Database querying using SQL
- Unix
- SDN using the RYU controller
- TensorFlow
- Deep learning
- Neural networks
- Pandas
- NumPy
Work History
AI Data Annotator
HandShake
, Remote
06.2025 - Current
- Annotated diverse datasets for machine learning models, ensuring high-quality data representation.
- Collaborated with data scientists to refine annotation guidelines and improve project outcomes.
- Utilized advanced annotation tools to efficiently label and categorize data points.
Programmer
Tech Excellence Training
06.2023 - 08.2023
- Developed AI-based image classification models using TensorFlow, achieving high accuracy in classification tasks.
- Implemented data augmentation techniques to improve model performance in crop disease detection.
- Managed precise record-keeping, ensuring zero discrepancies.
Education
Master of Science - Electrical and Computer Engineering
05.2026
- Focus: Artificial Intelligence, Neural Networks, and Machine Learning Applications
- Relevant Coursework: Applications of Neural Networks, Circuit Analysis, Research Methodologies, Software Defined Networking (SDN), Field Programmable Gate Array (FPGA) Design
- 3.85 GPA
Bachelor of Science - Computer Science
05.2023
- Relevant Coursework: Data Structures, Software Engineering , Relational Database Querying
- Honors & Awards: Dean's List (Fall 2022, Spring 2023)
Projects
Endangered Animal Classification
Developer (Team of 3)
https://github.com/nateej/deep-learning-dataset
Developed an AI-based image classification model using Python and TensorFlow. Employed transfer learning with a customized ResNet50 model to achieve 85% accuracy across 10 endangered animal classes.
Crop Disease detection Developer
https://colab.research.google.com/drive/1mILcomEwM6ziN7CCF15NGYLE5asGcDaL?usp=sharing
Engineered a deep learning model leveraging the VGG19 architecture to classify plant diseases across 38 categories from a dataset of 87,867 images. Applied transfer learning, fine-tuning, and data augmentation techniques, achieving 99.4% test accuracy.
References
References available upon request.
Overview
1
1
years of professional experience