Summary
Paul Melki is a Research & Innovation Scientist with eight years of experience applying probabilistic and statistical methods to build reliable, trustworthy ML systems, currently advancing AI and robotics for precision agriculture at EXXACT Robotics. He completed a CIFRE PhD at Université de Bordeaux focused on conformal prediction and uncertainty quantification for deep learning in proximal sensing, blending theory in statistical learning with hands-on deployment in agri-robotics. Trained in statistics and econometrics (Toulouse School of Economics) and software engineering (University of Balamand), he moves fluently between rigorous probabilistic modelling and practical computer vision pipelines. He teaches applied machine learning and statistics, translating research into lab courses and mentoring engineering students. Notably, his early work produced novel evaluation methodologies for robust segmentation models and influenced production practices during industry collaborations. Outside work he stays intellectually curious—reading, photographing nature, and drawing creative inspiration from classical music and forests.
8 years of coding experience
4 years of employment as a software developer
Bachelor of Science - BS, Computer Science: Software Engineering, 92 / 100, Bachelor of Science - BS, Computer Science: Software Engineering, 92 / 100 at University of Balamand
Doctor of Philosophy - PhD, CIFRE, Statistical Learning, Probability, Computer Vision, Agriculture, Doctor of Philosophy - PhD, CIFRE, Statistical Learning, Probability, Computer Vision, Agriculture at Université de Bordeaux
Master of Science - MS, Applied Mathematics: Statistics and Econometrics, 15.8 / 20, Master of Science - MS, Applied Mathematics: Statistics and Econometrics, 15.8 / 20 at Toulouse School of Economics
Arabic, French, English, Greek, Romanian