My research is in probabilistic machine learning. Machine learning plays a key role in artificial intelligence and data science, and it has a large overlap with statistics. The Bayesian paradigm provides a natural framework for learning from data under uncertainty and to automate decision-making. My recent work aims to democratise machine learning by scaling up (approximate) inference algorithms to handle massive data sets and by automating the process of tuning learning parameters through Bayesian optimisation. I am also interested in learning data representations 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. I was a member of the Machine Learning Group and the Crypto Group. Until mid 2005, I was involved in the European project OPTIVIP, in which we developed a visual prosthesis for blind people. Since then, I contributed to the European projects SCARD, PinView, FUPOL and Fusepool, as well as the UK funded project VISDEM. At present, I hold an Honorary Senior Research Associate position in the Centre for Computational Statistics and Machine Learning at University College London. I was also an active participant of the PASCAL network of excellence. 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 am now a Senior Research Scientist with Machine Learning Science at Amazon, Berlin, where I lead the algorithms group. Recently, I served as an Area Chair for NIPS '11, NIPS '13, AISTATS '14, AISTATS '15 and ICML '15. I was Tutorials Chair at ECML-PKDD '09 and Industry Track Chair for ECML-PKDD '12, and taught a lecture on Bayesian optimisation at MLSS 2016.
Teaching 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 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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