My goal is to advance machine intelligence and my research is in probabilistic machine learning. Machine learning has a large overlap with statistics, plays a central role in data science, and is fuelling the AI revolution we are experiencing today. My recent work aims at automating decision-making and democratising machine learning. I am interested in learning representations, meta-learning and, more generally, machine reasoning. I received the Electrical Engineering degree and the PhD in Applied Sciences from the Université catholique de Louvain, respectively in 2001 and 2005. As a member of the Machine Learning Group and the Crypto Group, I worked on the European projects OPTIVIP, in which I developed neural networks embedded in a visual prosthesis for blind people, and SCARD, where I evaluated the robustness of cryptographic hardware against side channel attacks exploiting electro-magnetic radiation. Next, I did a Post-doc with John Shawe-Taylor at University College London and collaborated closely with Manfred Opper. I was also an active participant of the PASCAL European network of excellence. Until December 2015, I held a Honorary Senior Research Associate position in the Centre for Computational Statistics and Machine Learning. In October 2009, I joined Xerox Research Centre Europe, where I led the Machine Learning group. The team conducted applied research in machine learning, computational statistics and mechanism design, with applications in customer care, transportation and governmental services. I joined Amazon, Berlin, as an Applied Science Manager in October 2013, where I was in charge of delivering zero-parameter machine learning algorithms. I am now a Principal Applied Scientist. Recently, I served as an Area Chair for NIPS '11, NIPS '13, AISTATS '14, AISTATS '15, ICML '15, and NIPS '17. I was Tutorials Chair at ECML-PKDD '09 and Industry Track Chair for ECML-PKDD '12, and taught a lecture on Bayesian optimisation at Machine Learning Summer School '16.
Teaching Data Science Summer School (DS3), Paris, 2017: Tutorial on Bayesian Optimisation; Amazon: A Playground for Machine Learning. Machine Learning Summer School (MLSS 2016, Arequipa): Bayesian Optimisation. Peyresq Summer School in Signal and Image Processing '16: Classification and Clustering. Engineering in Computer Science '12 at ENSIMAG: Statistical Principles and Methods. MSc in Machine Learning '11 (Applied Machine Learning) at UCL: Machine Learning at Xerox -- From statistical machine translation to large-scale image search. Tutorial on Probabilistic Graphical Models at PASCAL Bootcamp 2010: videolecture (2 parts). MSc in Intelligent Systems '08 at UCL: Advanced Topics in Machine Learning. CSML'07 reading group on Stochastic Differential Equations. Workshops Gaussian Process Approximations (GPA) workshop, Berlin, Germany, 2017. NIPS workshop on Learning Semantics, Montreal, Canada, 2014. NIPS workshop on Choice Models and Preference Learning, Grenada, Spain, 2011. Workshop on Automated Knowledge Base Construction, Grenoble, France, 2010. PASCAL2 workshop on Approximate Inference in Stochastic Processes and Dynamical Systems, Cumberland Lodge, United Kingdom, 2008. NIPS workshop on Dynamical Systems, Stochastic Processes and Bayesian Inference, Whistler, Canada, 2006.
Thesis Probabilistic Models in Noisy Environments - And their Application to a Visual Prosthesis for the Blind C. Archambeau Doctoral dissertation, Université catholique de Louvain, Louvain-la-Neuve, Belgium, September, 2005.
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