David Landry
Research Scientist at Google DeepMind
Stony Stratford, England, United Kingdom
Summary
👤
Senior🎓
Top SchoolDavid Landry is a Research Scientist with 11 years of experience applying machine learning, scientific modelling, and high-performance computing to large, complex datasets, currently at Google DeepMind after completing a PhD in AI at Sorbonne/INRIA. His background spans climate science and mobile robotics—improving weather models at Environment and Climate Change Canada and developing lidar perception and ICP-related tooling from his work on libpointmatcher and libnabo. He combines rigorous academic research with pragmatic engineering, contributing Python bindings, refactors, and unit tests to low-level C++ libraries to make advanced algorithms more accessible and robust. Based in the UK, he is comfortable moving between prototype research and production-quality code, with a track record of modularizing core algorithms and validating them through tests. An understated strength is his ability to translate domain expertise (climate and robotics) into scalable ML solutions that bridge research and operational deployment.
11 years of coding experience
7 years of employment as a software developer
Doctor of Science Artificial Intelligence, Doctor of Science Artificial Intelligence at Sorbonne UniversitéMaster of Science Mobile Robotics, Master of Science Mobile Robotics at Université LavalDEC Sciences de la Nature, DEC Sciences de la Nature at Cégep Limoilou
English, French
Github Skills (22)
algorithm10
matcher10
algorithms10
c-language10
lib10
python10
nearest-neighbors10
data-structure10
numpy10
mat10
nearest-neighbor-search10
boost-python10
data-structures10
cprogramming-language10
point-cloud10
Programming languages (7)
Github contributions (5)
norlab-ulaval/libnabo
Mar 2017 - Apr 2017
A fast K Nearest Neighbor library for low-dimensional spaces
Role in this project:
Back-end Developer
Back-end DeveloperContributions:13 commits, 2 PRs, 4 comments in 1 month
Contributions summary:David primarily worked on enhancing the Python bindings for the `libnabo` library. Their contributions included adapting the C++ code to be compatible with Python 3, integrating with the NumPy library, and porting test files to function correctly in Python 3. Additionally, the user added documentation to the Python interface and addressed whitespace issues.
eigenvaluesnearest-neighborlinear-algebrandarraynearest
norlab-ulaval/libpointmatcher
May 2017 - Jun 2017
An Iterative Closest Point (ICP) library for 2D and 3D mapping in Robotics
Role in this project:Back-end Developer
Back-end DeveloperContributions:10 commits, 1 PR, 4 comments in 1 month
Contributions summary:David primarily focused on refactoring and modularizing the `libpointmatcher` library. They moved and restructured code related to error minimizers, specifically `PointToPlaneWithCovErrorMinimizer` and `PointToPlaneErrorMinimizer`, separating them into dedicated files. Additionally, they added unit tests to validate the functionality of these error minimizers. These changes suggest an effort to improve code organization and ensure the robustness of the library's core ICP (Iterative Closest Point) algorithms.
roboticsclosesticppoint-cloudsiterative
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.