Autonomous Driving

Promoted to Senior Software Tech Lead for AD/ADAS Scene Understanding at Ampère (Renault Group).

Heading to Barcelona for AutoSens Europe 2026 to present on multimodal sensor fusion and quantifying operational uncertainty for AD/ADAS.

Lectio magistralis delivered for the ECN Datasim course, introducing key concepts in sensor fusion and scene understanding for autonomous driving and ADAS.

Lectio magistralis delivered for the ESIEE, introducing key concepts in sensor fusion and scene understanding for autonomous driving and ADAS.

Accepted paper at IEEE ITSC 2025 benchmarking uncertainty estimation methods and safety-centric metrics for AEB applications.

Lectio magistralis delivered for the ECN Datasim, introducing key concepts in sensor fusion and scene understanding for autonomous driving and ADAS.

Lectio magistralis delivered for the ENSTA Mastère Spécialisé, introducing key concepts in sensor fusion and scene understanding for autonomous driving and ADAS.

A graduate-level course covering multi-sensor perception, probabilistic fusion, object tracking, and scene understanding for autonomous driving and ADAS — with hands-on Python lab exercises.

A patent describing a method for robust lane boundary detection using map data, polynomial boundary representation, and Mahalanobis distance computations.

A PhD thesis presenting data fusion methods combining sensors and HD maps to enhance lane boundary detection for autonomous vehicles.