Thomas Müller
Head Of Algorithmic Coding at DAMEDIC GmbH
Cologne, North Rhine-Westphalia, Spain
Summary
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Rockstar🎓
Top SchoolThomas Müller is a Head of Algorithmic Coding and NLP/ML researcher with a PhD from Ludwig-Maximilians Universität München and 11 years of experience building production-grade language and reasoning systems. He has led research and engineering teams at AWS, Google Research, Symanto and now Damedic, translating advances in semantic parsing, text-to-SQL and robust model fine-tuning into deployable products for content safety and medical coding automation. Known for bridging deep research (CRFs, zero/few-shot learning, transformers) with applied engineering, he has driven projects across question answering for semi-structured data, arithmetic reasoning improvements for large models, and practical clinical NLP. Fluent in both academic and industrial settings, he also teaches advanced NLP topics and has a track record of shipping high-impact features such as Bedrock Guardrails workstreams. Based in Cologne with international academic roots in Spain and Germany, he blends rigorous quantitative thinking with a pragmatic focus on reliable, regulatory-aware ML systems.
11 years of coding experience
14 years of employment as a software developer
Ludwig Maximilian University of MunichDiplom (MSc), Computer Science, 1.3 (German grade very good), Diplom (MSc), Computer Science, 1.3 (German grade very good) at Universität StuttgartUniversitat Politècnica de València
English, German, Spanish
Github Skills (70)
nlp-machine-learning10
pygame10
segmentation10
text-table10
sdl9
python9
streamlit9
game-dev9
question-answering9
data-visualization8
developer-tools8
deep-learning8
machine-learning8
data-analysis8
data-science8
Programming languages (5)
Github contributions (5)
muelletm/cistern
Mar 2015 - Oct 2016
Open-source tools for morphological tagging, segmentation and stemming.
Contributions:193 commits, 146 pushes in 1 year 7 months
segmentation
symanto-research/few-shot-learning-label-tuning
Feb 2022 - Mar 2022
A few-shot learning method based on siamese networks.
Contributions:12 commits, 2 PRs, 11 pushes in 29 days
few-shot-learningsiamese-network
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