Morten Dahl
Paris, Ile-de-France
Summary
🤩
Rockstar🎓
Top SchoolMorten Dahl is a computer scientist and cryptographer with a PhD in cryptography and over 12 years of software engineering experience, specializing in secure computation and privacy-preserving machine learning. He blends academic research—secure multiparty computation, formal verification of cryptographic protocols, and type systems—with hands-on engineering, including 6+ years in Rust and contributions to privacy-focused open-source projects like PySyft (improving TF Encrypted integration and private prediction serving). Based in Paris, he has operated in fully remote teams across Europe and North America for 4+ years, making him adept at distributed collaboration and asynchronous workflows. Morten’s background spans roles as cryptographer, software engineer, and data scientist, enabling him to translate formal security models into practical, auditable systems. An often-overlooked strength is his track record of refactoring complex ML privacy integrations to improve usability and reliability for end users.
12 years of coding experience
Bachelor of Science, Computer Science, Bachelor of Science, Computer Science at Aalborg UniversityVisiting M.Sc. student, Computer Science, Visiting M.Sc. student, Computer Science at The University of EdinburghVisiting M.Sc. student, Computer Science, Visiting M.Sc. student, Computer Science at Aarhus UniversitetVisiting researcher, Computer Science, Visiting researcher, Computer Science at Ecole normale supérieureMaster of Science (MS), Computer Science, Master of Science (MS), Computer Science at Aalborg Universitet
Danish, English, French
Github Skills (9)
keras10
tensorflow10
federated-learning10
python10
machine-learning9
deep-learning9
secure-computation9
cryptography8
pytorch6
Programming languages (9)
Github contributions (5)
OpenMined/PySyft
May 2019 - Jul 2019
Perform data science on data that remains in someone else's server
Role in this project:
ML Engineer
ML EngineerContributions:20 commits, 8 PRs, 30 comments in 1 month
Contributions summary:Morten primarily contributed to integrating and improving the functionality of TF Encrypted (TFE) within the PySyft Keras framework. They introduced model shutdown capabilities, refactored and improved TFE configuration, and implemented support for asynchronous model querying. The commits also involved cleanup and adjustments to example notebooks for TFE integration, enhancing the user experience and demonstrating private prediction serving.
data-sciencedeep-learningsecure-computationpytorchprivacy
mortendahl/privateml
Apr 2017 - Sep 2019
Various material around private machine learning, some associated with blog
Contributions:134 commits, 106 pushes, 2 branches in 2 years 5 months
machine-learning
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