About Me
I am a research engineer with a PhD in Computer Vision specialising in sensor fusion, deep learning for perception, and online 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 developed practical solutions resulting in publications at top-tier conferences and an international patent.
I am currently a Perception Research Engineer at Konboi, a Paris-based startup developing autonomous trucks and AI retrofit kits for freight.
Full curriculum vitae (PDF) →Research & Publications
Deep learning approaches for robust multi-sensor calibration in autonomous driving — LiDAR-camera perception, uncertainty quantification, and online calibration.
Uncertainty-Aware Online Extrinsic Calibration: A Conformal Prediction Approach
Novel uncertainty quantification for sensor calibration with reliability guarantees and improved odometry accuracy.
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MULi-Ev: Maintaining Unperturbed LiDAR-Event Calibration
First method for online calibration of LiDAR with event cameras.
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PseudoCal: Towards Initialization-Free Camera-LiDAR Calibration
First deep learning-based sensor calibration method without requiring manual initialization.
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UniCal: A Single-Branch Transformer-Based Model for Camera-to-LiDAR Calibration
Efficient Transformer approach for calibration and validation, leading to an international patent.
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Deep Learning for Multi-Sensor Calibration in Autonomous Driving
Novel deep learning approaches for robust multi-sensor calibration with uncertainty-aware methods.
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Awarded for outstanding review contributions to the conference.
View reviewers →Joined Konboi, a Paris-based startup developing 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 thesis →