With a background in cryptography and computer science, Morten has spent most of the past decade applying and adapting techniques from these fields to privacy-preserving machine learning and confidential blockchain. He is typically focusing on practical tools and concrete applications, with the aim of making privacy enhancing technologies more accessible to practitioners.
His work has primarily been on secure multiparty computation. Most recently, on the fhEVM for confidential smart contracts. And before that on TF Encrypted for encrypted deep learning in TensorFlow, and on SDA 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 senior director for blockchain at Zama, with a focus on making fully homomorphic encryption accessible. Previously, he worked as chief scientist at Cape Privacy, with a focus on taking privacy-preserving machine learning to production, and he lead the cryptography team at Snips, with a focus on building private-by-design machine learning systems for mobile devices. He was an early member of OpenMined, an online community building a platform for secure federated learning.