I am a research engineer with a PhD in Computer Vision specializing in sensor fusion, deep learning for perception, and real-time calibration for autonomous driving.
My research focuses on developing robust deep learning methods for multi-sensor perception, with expertise in LiDAR-camera perception and uncertainty estimation. Through my work at Motional and Toyota Motor Europe, I've gained industry experience. During my PhD, I've developed practical solutions that have resulted in publications in top-tier conferences and an international patent.
My research focuses on robust deep learning methods for multi-sensor perception, with expertise in LiDAR-camera perception and uncertainty estimation. I've developed practical solutions that have resulted in publications in top-tier conferences and an international patent.
Novel uncertainty quantification approach for sensor calibration that provides reliability guarantees and improves odometry accuracy.
First method for online calibration of LiDAR with event cameras.
First method for deep learning-based sensor calibration without requiring manual initialization.
Efficient Transformer-based approach for both calibration and validation, leading to an international patent.
Novel deep learning approaches for robust multi-sensor calibration in autonomous driving systems with uncertainty-aware methods.
I was awarded the "Top Reviewer" award at NeurIPS 2025 for my reviews of the conference's submissions.
View reviewersExcited to start a new role as Perception Research Engineer at Konboi, a startup based in Paris, developping the future of freight transportation by working on autonomous trucks and AI retrofit kits for fuel savings.
Successfully defended my PhD thesis on "Deep Learning for Multi-Sensor Calibration in Autonomous Driving".
View thesisWhether you are interested in discussing my research topic or just want to connect, I'd love to hear from you.
