With a background in cryptography and privacy, Morten has spent recent years applying and adapting techniques from these fields to machine learning, focusing on practical tools and concrete applications with the aim of making privacy-preserving machine learning more accessible to practitioners.
His work has primarily been on encrypted deep learning and secure federated learning. This includes TF Encrypted, an open source project for encrypted deep learning in TensorFlow, and SDA, an open source project for secure federated learning on sporadic devices.
Morten holds a MSc in theoretical computer science and a PhD in cryptography. He currently works as a research scientist at Dropout Labs, a startup focused on taking privacy-preserving machine learning to production. Previously, he lead the cryptography team at Snips, a startup focused on building private-by-design machine learning systems for mobile devices. He is a member of OpenMined, an online community building a platform for secure federated learning.