My research is in probabilistic machine learning. Machine learning is a key component in data science. It is closely related to artificial intelligence and has a large overlap with statistics. My work focuses on problems in automated-decision making, collaborative filtering, relational learning and natural language processing. The Bayesian paradigm provides a natural framework for dealing with the uncertainty we encounter in any real-life situation. One of the main challenges today is to scale up inference algorithms to the exponential increase of available digital data. Being able to assimilate more data is important as more data means more reliable and nuanced data models. Hence, I recently got interested in distributed approaches and very large scale inference algorithms. 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 computational statistics and mechanism design. I am now a Senior Research Scientist with Machine Learning Science at Amazon Berlin. Recently, I served as an Area Chair for NIPS '11, NIPS '13 and AISTATS '14. I was Tutorials Chair at ECML-PKDD '09 and Industry Track Chair at ECML-PKDD '12. Email: C/Archambeau#cs/ucl/ac/uk
Workshops 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. Teaching 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.
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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