Nahom M. Birhan
Machine Learning & AI Software Engineer
Machine Learning and AI Software Engineer with over 5 years of professional experience specializing in Applied Machine Learning for Cybersecurity and Data Analytics. Experienced in developing ML solutions for edge devices, IoT, and software applications, with a focus on implementing deep learning models for security purposes.
Proficient in anomaly detection, image processing, and network security using TensorFlow, PyTorch, and AWS. Committed to ethical AI development and innovative applications of machine learning in security and data analysis.
Research Interests
My research focuses on the intersection of machine learning, artificial intelligence, and cybersecurity, with a particular emphasis on leveraging ML for cybersecurity applications and exploring the robustness and vulnerabilities of ML models. I'm interested in developing lightweight ML solutions for edge devices and IoT environments, addressing challenges in network security, anomaly detection, and impostor identification. My work extends to investigating email phishing detection, blockchain applications in healthcare data security, and novel ML applications in environmental monitoring. Through this research, I aim to enhance the security and efficiency of AI systems while deepening our understanding of their potential vulnerabilities and limitations in real-world, resource-constrained scenarios.
Research Areas
Research Projects
Impostor Detection in IoT Edge Sensor Networks
Developed lightweight LSTM model for edge devices, optimized using TensorFlow Lite quantization
Technologies: Raspberry PI, Arduino nano 33 BLE, TensorFlow/Lite, Python, C/C++
Email Phishing Detection in Africa
Analyzed African-specific phishing patterns, implemented and compared five ML models, mitigated overfitting
Technologies: Scikit-learn, TensorFlow, Numpy
Blockchain-Based Healthcare Data Security
Proposed system integrating blockchain and IPFS for secure medical records, compared IPFS and Amazon S3 access times.
Technologies: IPFS, AWS (IAM, S3), Blockchain concepts
Prime and Composite Classification
Created dataset of 2 million numbers, developed models using sequence models and 1D-CNN, analyzed performance
Technologies: Python, TensorFlow, NumPy, Google Colab
Smart Irrigation System (BSc. research)
Designed automated system with XBee sensor communication, integrated motors, pumps, and sensors, developed control algorithms
Technologies: Proteus, XBee, Arduino, C/C++, Various sensors