Summary
Alexandre Morgand is a research scientist in computer vision and machine learning with 12 years of experience building SLAM, VIO and dense 3D reconstruction systems for robotics, AR and next‑gen VFX workflows. Currently at Simulon, he combines novel-view synthesis (NeRF, Gaussian Splatting) with practical SLAM and rendering R&D to simplify VFX for creators, drawing on prior work at SLAMcore where he improved VIO, loop closure, pose-graph estimation and map management (achieving an 80% clutter reduction). His PhD focused on light-source modeling to enhance AR rendering realism, producing multiple international publications and a patent, and his toolset spans C++, Python/Matlab and real‑time systems. Comfortable bridging research and product, he also has hands-on QA and dataset recording experience (Vicon) and a background in robotics middleware and real-time positioning systems. Notably, he pairs deep academic expertise with pragmatic engineering that reuses and compresses maps for downstream tasks—making research directly deployable.
11 years of coding experience
6 years of employment as a software developer
PhD in Computer Vision for Robotics Computer Science; Computer Vision; Augmented Reality; SLAM, PhD in Computer Vision for Robotics Computer Science; Computer Vision; Augmented Reality; SLAM at Université Clermont Auvergne
CS61A CS61B, CS61A CS61B at University of California, Berkeley
Master degree in Computer science Computer Science, Master degree in Computer science Computer Science at EPITA: Ecole d'Ingénieurs en Informatique
Baccalauréat Scientifique, Baccalauréat Scientifique at Lycée Saint Joseph du Loquidy
French, English, German