I build deployable AI systems for real-world operations: camera pipelines, detection and tracking workflows, edge inference, evaluation dashboards, and reliable multimodal medical-AI research. Open to applied AI, computer vision, edge AI, MLOps, GenAI/RAG, and healthcare AI roles across international, relocation-friendly, and remote teams.
Real-world camera pipelines, detection, tracking, QA workflows, and deployment-aware AI systems.
PhD research on multimodal MRI-clinical fusion, calibration, uncertainty, explainability, and leakage-aware evaluation.
Python, deep learning, FastAPI-ready systems, evaluation workflows, edge inference, cloud-aware development, and reproducible ML.
I sit between production computer vision, deployment-aware ML engineering, and reliable medical-AI research. The common thread is practical AI that can be evaluated, explained, and used beyond a notebook.
Computer vision systems for messy camera data, detection, tracking, counting, and operational analytics.
Production-minded ML workflows across data quality, evaluation, reporting, deployment constraints, and stakeholder communication.
Research discipline from reliable multimodal medical AI: calibration, uncertainty, explainability, leakage control, and generalization.
Two flagship proof points: production computer vision / edge AI and reliable multimodal medical AI research.
A computer-vision pipeline for poultry monitoring using object detection, camera-based counting, and density estimation. Designed around real-world farm constraints, noisy visual conditions, and production-oriented evaluation.
Read Case StudyA research case study on MRI-clinical fusion for neurodegenerative disease analysis, focused on leakage-aware evaluation, calibration, uncertainty, explainability, and review-aware decision-support outputs.
Read Research Case StudyMy PhD work focuses on reliable multimodal medical AI for neurodegenerative disease analysis, especially MRI-clinical fusion, calibration, uncertainty, explainability, and leakage-aware evaluation. That research mindset carries into production work: models need to be measured carefully, trusted responsibly, and designed for changing real-world data.
I am open to international, relocation-friendly, and remote roles where I can build practical AI systems that combine model development, deployment, evaluation, and business impact.
For AI roles, collaborations, or research/engineering opportunities, feel free to reach out.