AI Engineer | Deep Learning | Computer Vision | NLP | Generative Modeling
I work on end-to-end machine learning systems with a focus on computer vision, large language models, generative approaches (GANs), and scientific imaging.
My experience includes medical imaging research, applied deep learning, and building production-oriented AI applications.
Generative model for synthesizing CT from MRI to support radiotherapy dose planning.
- Developed preprocessing and paired dataset preparation pipeline
- Trained and evaluated GAN architectures (PSNR, SSIM, structural metrics)
- Achieved a 2.5% improvement in anatomical preservation compared to baseline
Repository / Report: coming soon
Technologies: PyTorch, GANs, Medical Imaging
A modular conversational interface supporting inference from multiple LLM backends.
- Implemented token streaming, temperature/top-p tuning, and model switching
- Designed multilingual handling for real-time translation and interaction
- Built with modular architecture for future model integration
Repository: https://github.com/yaghmo/chatbot
Technologies: Python, Streamlit, Hugging Face Transformers
Comparative study of segmentation architectures for lung region extraction.
- U-Net, DeepLabV3, and FCNN under identical training and evaluation settings
- Evaluation using Dice, IoU, and boundary metrics
Repository: https://github.com/yaghmo/U_Net-DeepLabV3-FCNN-lung-segmentation
Technologies: PyTorch, Computer Vision
Real-time pipeline combining YOLOv8 detection with SORT tracking.
- Extraction of spatial interaction descriptors (distance, direction, relative motion)
- Framework supports downstream tasks such as scene understanding or activity prediction
Repository: https://github.com/yaghmo/yolov8-sort-detection-tracking-segmentation-force-histogram-banner-cnn
Technologies: YOLOv8, SORT, NumPy, OpenCV
End-to-end sentiment analysis workflow integrating language identification, translation, classification, and LLM-based summarization.
Repository: https://github.com/yaghmo/sentiment_analysis
Technologies: Python, Transformers, NLP
- Computer Vision and Image Understanding
- Medical Imaging and Scientific Computing
- Generative Modeling (GANs, diffusion models)
- Large Language Models and conversational AI
- Multimodal AI systems
Machine Learning:
Deep Learning, Computer Vision, NLP, Generative Models, LLMs, Representation Learning
Frameworks:
PyTorch, TensorFlow, Hugging Face, MONAI, Scikit-Learn, OpenCV
Systems & Tools:
Docker, Git, Linux, Streamlit
Programming:
Python, C, C++
LinkedIn: https://www.linkedin.com/in/belmir-yaghmoracen/
Email: yaghmo.belmir@gmail.com