Matthias Treder
Staff Machine Learning Engineer at Sony Interactive Entertainment
London, England, United Kingdom
Summary
👤
Senior🎓
Top SchoolMatthias Treder is a Staff Machine Learning Engineer with a decade of experience bridging academic research and real-time production systems, currently applying ML to denoising and neural rendering at Sony Interactive Entertainment. He previously led neural rendering at iSize Technologies and holds an academic track record as an assistant professor and research associate across top labs, with a strong foundation in cognitive psychology, mathematics, and neuroimaging. His work uniquely spans brain-imaging MVPA tools (contributing core functions to the widely used FieldTrip toolbox) to game-ready neural asset generation, demonstrating deep expertise in model generalization, feature selection, and practical classifier integration. Comfortable moving between research and engineering, he ships solutions that translate cutting-edge computer vision and ML research into production constraints. Based in London, he maintains an active scholarly profile (Google Scholar) and a consistent open-source footprint that underscores both scientific rigor and applied impact.
10 years of coding experience
13 years of employment as a software developer
Bachelor's degree, Mathematics, Bachelor's degree, Mathematics at Technische Universität BerlinPropedeuse, Computer Science, Propedeuse, Computer Science at Radboud University Nijmegen
Github Skills (5)
statistics10
analyse10
matlab10
neuroimaging10
machine-learning9
Programming languages (5)
Github contributions (5)
fieldtrip/fieldtrip
Mar 2019 - Mar 2021
The MATLAB toolbox for MEG, EEG and iEEG analysis
Role in this project:
Data Scientist
Data ScientistContributions:2 reviews, 8 commits, 7 PRs in 2 years
Contributions summary:Matthias implemented and refined `ft_statistics_mvpa.m`, a core function for multivariate pattern analysis. This included integrating and adjusting a MVPA-Light interface, suggesting the user's work focused on integrating and utilizing machine learning models within the FieldTrip framework. The contributions show the user working on feature selection, cross-validation, and model generalization strategies. They also demonstrated skill in applying different classifiers (e.g., LDA, SVM) to the provided data.
brainneurosciencetime-frequencyfieldtripieeg
treder/MVPA-Light
Jun 2017 - Oct 2022
Matlab toolbox for classification and regression of multi-dimensional data
Contributions:1 review, 908 commits, 11 PRs in 5 years 5 months
matlab-toolboxmulti-dimensionalregressionmatlabtoolbox
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