Lukas Ruff - Co-Head Of Data Science

Lukas Ruff

Co-Head Of Data Science

Berlin, Germany

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Summary

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Senior

🎓

Top School

Lukas Ruff is a machine learning researcher and leader with 11 years of experience, currently Co-Head of Data Science at Aignostics, where he focuses on developing robust and trustworthy AI for digital pathology. He holds a summa cum laude PhD in Machine Learning from TU Berlin and an MSc in Statistics, bringing strong theoretical grounding to applied problems like anomaly detection. Lukas has a proven track record building end-to-end anomaly detection pipelines—his Deep-SVDD PyTorch implementation showcases hands-on expertise in model, data loader, and training infrastructure development. He combines academic rigor with product-minded engineering, bridging research and deployment to make AI systems more reliable in high-stakes medical settings. Based in Berlin, he blends a background in mathematical finance with deep statistical and ML knowledge, enabling a pragmatic yet principled approach to trustworthy AI.

10 years of coding experience

B.Sc. in Mathematical Finance, B.Sc. in Mathematical Finance at Universität Konstanz / University of Konstanz

Master of Science (M.Sc.), Statistik, Master of Science (M.Sc.), Statistik at HU Berlin, TU Berlin, FU Berlin

Doctor of Philosophy - PhD, Machine Learning, with distinction (summa cum laude), Doctor of Philosophy - PhD, Machine Learning, with distinction (summa cum laude) at Technische Universität Berlin

German, English, French

Github Skills (10)

machine-learning10

pytorch10

deep-learning10

anomaly-detection10

python10

ml9

datasets9

mle9

data-structures6

data-structure6

Programming languages (2)

RubyPython

Github contributions (5)

lukasruff/Deep-SVDD-PyTorch

Oct 2018 - Jun 2019

A PyTorch implementation of the Deep SVDD anomaly detection method

Role in this project:

ML Engineer

Contributions:82 commits, 4 pushes, 6 comments in 8 months

Contributions summary:Lukas implemented the core Deep SVDD anomaly detection method in PyTorch. Their work included setting up the DeepSVDD class, integrating datasets and data loaders, and defining the basic structure for datasets with MNIST and CIFAR-10 implementations. They also established a base class structure for models and trainers, including autoencoder pretraining functionality, demonstrating a focus on developing a complete anomaly detection pipeline.

pytorchanomalypythonsvdddeep-learning

lukasruff/Deep-SAD-PyTorch

Jun 2019 - Feb 2020

A PyTorch implementation of Deep SAD, a deep Semi-supervised Anomaly Detection method.

Contributions:11 commits, 10 pushes, 1 branch in 8 months

pytorchanomalypythonsuperviseddeep-learning

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