I am a passionate Machine Learning Engineer with 4 years of experience in developing and deploying machine learning models to solve real-world problems. My expertise lies in the analysis of electropysiological data.

• Programming Languages: Python (Advanced), Java, C++
• Machine Learning Frameworks: TensorFlow, PyTorch, Scikit-learn
• Deep Learning: Transformers, CNNs, bio-inspired neural networks
• Natural Language Processing (NLP)
• Computer Vision
• Data Visualization: Matplotlib, Seaborn
• Deployment: Docker, Kubernetes
• Version Control: Git, GitHub

Implemented a novel dual-branch transformer model and motion sensor-based training approach for finger gesture recognition using EMG, overcoming challenges associated with hand position dependency.

• GitHub Repository: link

Explored and contrasted the effectiveness of Kalman Filters, including their non-linear adaptations, and Neural Networks for precise robot state estimation

1. "Machine Learning Models Evaluation and Feature Importance Analysis on NPL Dataset", Machine Learning for Development (ML4D) workshop, NeurIPS 2021.
2. "Improving Remote Sensing Scene Classification via Label-Free Prompt Tuning", ECCV, 2023.

• Master of Science in Machine Learning, Mohamed Bin Zayed University of Artificial Inteligence, Abu Dhabi, UAE 2024
• Bachelor of Science in Electrical and Computer Engineering, Addis Ababa Science and Technology University, Addis Ababa, Ethiopia 2021

• Machine Learning, Stanford University via Coursera, 2019
• Deep Learning Specialization, DeepLearning.AI via Coursera, 2020
• Structuring Machine Learning Projects, DeepLearning.AI, 2020

• LinkedIn: LinkedIn Profile URL
• Email: [email protected]