About me

Machine learning, statistical science, and where they meet practice.

I am Director of Artificial Intelligence at Helsing. Before that I was a Principal Applied Scientist at Amazon and Chief Scientist for Amazon SageMaker until June 2023. Since 2017 I have been an associate member of the Department of Statistics at the University of Oxford, and in 2018 I was elected Fellow in the Robust Machine Learning Program of ELLIS, the European Laboratory for Learning and Intelligent Systems.

Background

I received the Electrical Engineering degree and the PhD in Applied Sciences from UCLouvain, in 2001 and 2005 respectively, where I was a member of the Machine Learning Group and the Crypto Group. I worked on the European projects OPTIVIP, developing neural networks embedded in a visual prosthesis for the blind, and SCARD, demonstrating weaknesses of cryptographic hardware against machine learning-based side-channel attacks exploiting electro-magnetic radiation.

I then did a post-doc with John Shawe-Taylor at University College London, collaborating closely with Manfred Opper at TU Berlin on data assimilation and approximate Bayesian inference, and took an active part in the PASCAL European network of excellence. Until December 2015 I held an Honorary Senior Research Associate position in UCL’s Centre for Computational Statistics and Machine Learning.

In October 2009 I joined Xerox Research Centre Europe — now Naver Labs Europe — where I led the Machine Learning group, working on natural language understanding and mechanism design with applications in customer care, transportation and governmental services. I joined Amazon in Berlin in October 2013 to develop zero-parameter machine learning algorithms. As a Principal Applied Scientist at AWS I oversaw the product-related science powering Amazon SageMaker and led long-term initiatives in automated machine learning, continual learning and responsible AI. That work laid the foundation of SageMaker Automatic Model Tuning, SageMaker Autopilot, SageMaker Clarify, and AWS Clean Rooms.

Research interests

• Approximate Bayesian inference and probabilistic modelling
• Automated machine learning — hyperparameter and neural architecture optimization
• Continual learning and uncertainty quantification
• Responsible AI: fairness, explainability and model understanding
• European technological sovereignty

Get in touch

Email: cedric,archambeau#helsing,ai (replace the comma with a dot and the hash with an at-sign)
Or find me on LinkedIn, GitHub and Bluesky.