Marcus Märtens
Associate Professor
Tokyo, Japan
Summary
👤
Senior🎓
Top SchoolMarcus Märtens is an Associate Professor and researcher based in Tokyo with 13 years of experience at the intersection of optimization, AI and space systems. He is best known for his work at the European Space Agency on trajectory optimization, satellite pose estimation and super-resolution, and he helped build and maintain the widely used pagmo optimization library, improving cross-platform compatibility and build reliability. Marcus combines academic depth—a PhD on information propagation in complex networks—with hands-on engineering in scientific crowdsourcing, competition design and verification of ML technologies for autonomous spacecraft. Comfortable spanning roles from backend/DevOps to machine learning research, he thrives on multidisciplinary problems that require rigorous optimization and creative algorithm design. An open-science advocate with a playful curiosity (his GitHub bio reads “I am an alien”), he blends technical precision with a collaborative, community-first approach.
13 years of coding experience
3 years of employment as a software developer
Master of Science (M.Sc.), Informatik, 1.1 (very good), Master of Science (M.Sc.), Informatik, 1.1 (very good) at Universität Paderborn / University of PaderbornTU Delft
German, English
Github Skills (8)
c-language10
cmake10
cprogramming-language10
algorithm8
optimization8
optimisation8
optimizers8
parallel-computing8
Programming languages (4)
Github contributions (5)
esa/pagmo2
May 2016 - Apr 2018
A C++ platform to perform parallel computations of optimisation tasks (global and local) via the asynchronous generalized island model.
Role in this project:
Back-end Developer & DevOps Engineer
Back-end Developer & DevOps EngineerContributions:36 commits, 1 PR, 11 pushes in 1 year 11 months
Contributions summary:Marcus primarily focused on improving the compatibility of the PaGMO2 library with the Microsoft Visual Studio Compiler (MSVC) and fixing related compilation warnings. Their contributions include modifications to the problem and random number generator headers to address MSVC-specific issues. Furthermore, the user made several changes related to CMake build files, specifically addressing compiler warnings and fixing compilation issues with tests for MSVC. These actions also included merging branches from an external repository and general code clean-up.
optimization-methodsoptimization-algorithmspythonoptimization-toolsoperations-research
CoolRunning/dcgp
Jul 2020 - Aug 2021
Implementation of a differentiable CGP (Cartesian Genetic Programming)
Contributions:9 pushes, 1 branch in 1 year 1 month
differentiablecartesian-genetic-programminggeneticgenetic-programmingcartesian
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