About
Daniel is a Senior Applied Researcher working in M365 Research where he adapts state of art theoretical research to products across Microsoft. He has a passion for privacy preserving approaches to machine learning and his work has a strong focus on investigating and mitigating leakage capabilities of natural language models.
Prior to joining Microsoft, Daniel has worked as a data scientist in multiple industries (AstraZeneca and IHS Markit). He holds a PhD in Mathematics from Durham University, UK, where he studied algebraic invariants of knots (low-dimensional topology).