Floris Laporte is a photonics engineering leader and Chief Scientific Officer with 9 years of experience translating PhD research in photonic neuromorphic computing into practical photonic device and circuit optimization using machine learning techniques. He has advanced photonic design automation at Rockley Photonics and now leads scientific strategy at GDSFactory, focusing on speeding chip development and improving modeling fidelity. Floris pairs deep academic training (PhD, multiple MSc degrees) with hands-on toolbuilding—his open-source FDTD simulator implements 3D electromagnetic modeling with GPU support, custom boundary conditions and performance profiling. Based in Berlin, he blends applied physics, AI-driven optimization and software craftsmanship to shorten photonic development cycles, often shipping tooling that bridges simulation and fabrication.
9 years of coding experience
4 years of employment as a software developer
Master of Science - MS, Photonics Engineering, Master of Science - MS, Photonics Engineering at National Taiwan University
Master of Engineering - MEng, Photonics, Master of Engineering - MEng, Photonics at University of St Andrews
Master of Science - MS, Photonics Engineering, Master of Science - MS, Photonics Engineering at Vrije Universiteit Brussel
Master of Science - MS, Photonics Engineering, Master of Science - MS, Photonics Engineering at Universiteit Gent
Doctor of Philosophy - PhD, Photonics Engineering, Doctor of Philosophy - PhD, Photonics Engineering at Ghent University
A 3D electromagnetic FDTD simulator written in Python with optional GPU support
Role in this project:
Back-end Developer
Contributions:153 commits, 11 PRs, 111 pushes in 4 years 2 months
Contributions summary:Floris implemented a 2D and then a 3D Finite-Difference Time-Domain (FDTD) electromagnetic simulator using Python. The work involved creating a `Grid` class with methods for updating electric and magnetic fields, applying boundary conditions, and adding sources. The user refactored the backend, updated docstrings, and added a performance profiler to keep track of new features and identify bottlenecks, alongside improvements like the ability to incorporate objects of arbitrary shape and the addition of perfectly matched layer boundary conditions, and the addition of more source types and detectors for better analysis.
Contributions:37 commits, 33 pushes, 11 branches in 1 year 3 months
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