Bahar Jenabzadeh

About

I am an AI researcher and engineer with experience spanning machine learning research, computer vision, federated learning, large language models, and multi-agent AI systems. My work combines strong mathematical foundations with practical implementation skills, demonstrated through research projects in privacy-preserving federated learning, interpretable AI, transformer-based computer vision, geometric deep learning, and medical image segmentation. I have contributed to research associated with ACM SIGMETRICS, published work on interpretable emotion detection, and am currently developing transformer-based solutions for rooftop image rectification and teeth segmentation. In industry, I have designed and evaluated AI-driven recruitment systems leveraging NLP, semantic search, and LLMs, while also contributing to the development of multi-agent frameworks for mental health analysis and decision support. My technical expertise includes Python, PyTorch, TensorFlow, deep learning, computer vision, data analysis, and AI system design. With experience in both academic research and real-world deployment, I am passionate about building scalable, trustworthy, and impactful AI solutions that bridge cutting-edge research and practical applications.