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Research Scientist at Google DeepMind | PhD in Machine Learning from University of Cambridge
I'm a scientist at Google DeepMind in the AI for Science team (London, UK). Before that, I was a scientist at Secondmind (Cambridge, UK). I have a PhD in Machine Learning from the University of Cambridge. My work focuses on the intersection of statistical methods and deep learning. My research primarily revolves around generative modelling and sequential decision-making, focusing on applying these methods to problems in the physical sciences. Preceding all of this, I completed my Bachelor's and Master's degrees in Computer Science Engineering from Ghent University (Belgium).
University of Cambridge
Doctor of Philosophy - PhD, ENGINEERING
N/A – Present
Ghent University
Master's degree, Computer Science
N/A – Present
National Taiwan University
Master in Computer Science Engineering, Computer Science and Information Engineering
N/A – Present
Ghent University
Bachelor's degree, Computer Science
N/A – Present
Google DeepMind
Senior Research Scientist
April 1, 2026 – Present
Greater London
Google DeepMind
Research Scientist
January 1, 2024 – April 1, 2026
Greater London
Secondmind
Senior Machine Learning Researcher
April 1, 2019 – January 1, 2024
Secondmind
Machine Learning Researcher
September 1, 2017 – April 1, 2019
In The Pocket
Software Engineer
August 1, 2015 – September 1, 2015
Ghent
Master thesis
August 1, 2016 – June 1, 2017
Master’s dissertation submitted in order to obtain the academic degree of Master of Science in Computer Science Engineering. In this thesis, we explore Deep Gaussian Processes for metamodelling of non-stationary surrogate surfaces.
Cultural Fit Analysis
The candidate's profile is heavily skewed towards academic research and machine learning. While this demonstrates intellectual rigor, the lack of diverse project experience or explicit involvement in traditional backend engineering roles (outside of a brief internship) suggests a potential mismatch with a typical Backend Engineer role that often requires broader system design, API development, and operational responsibilities. The focus on research might indicate a preference for exploratory work over product-driven development cycles.
Soft Skills & Operational Fit
The candidate's extensive research background suggests strong analytical thinking, problem-solving, and independent work capabilities. However, the provided data lacks specific details on collaboration, project management, or direct software engineering operational experience beyond an internship, making a comprehensive assessment of operational fit challenging.