My goal is to advance machine intelligence to benefit the people. 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 research interests lie in probabilistic machine learning and Bayesian decision making. My recent work focusses on learning representations, meta-learning, and continual learning. I received the Electrical Engineering degree and the PhD in Applied Sciences from the UCLouvain, 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 the blind, and SCARD, where I evaluated the robustness of cryptographic hardware against machine learning-based 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 (now Naver Labs Europe), where I led the Machine Learning group. My 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 at Amazon Web Services, where I oversee the product-related science powering Amazon SageMaker and lead long-term science initiatives in the area of automated machine learning. I served as an Area Chair for NIPS '11, NIPS '13, AISTATS '14, AISTATS '15, ICML '15, NIPS '17, ICML '18, NIPS '18, IJCAI '19, NIPS '19, AISTATS '20, IJCAI '20, NIPS '20, ICML '21, ICLR '21, and NIPS '21. I was Tutorials Chair at ECML-PKDD '09 and Industry Track Chair for ECML-PKDD '12. I taught several lectures at the University of Oxford since 2017, as well as a lecture on Bayesian optimisation at the Machine Learning Summer School '16 and the Data Science Summer School '17.
Lectures and selected presentations Mini-symposium on Bayesian Methods in Science and Engineering at the SIAM Conference on Computational Science and Engineering: Bayesian Optimization by Density-Ratio Estimation. CVPR 2020 tutorial From HPO to NAS: Automated Deep Learning: Automated HP and Architecture Tuning. (recording) Computational Statistics and Machine Learning Seminars, Oxford, 2019: Learning Representations to Accelerate Hyperparameter Tuning. Machine Learning module of the (OxWaSP) Centre for Doctoral Training, Oxford, 2019: Bayesian Optimisation and Variational Inference. DALI 2018 workshop on Goals and Principles of Representation Learning, Lanzarote, 2018: Learning Reperesentations for Hyperparameter Transfer Learning. Congrès MATh.en.Jeans, Potsdam, 2018: L'Apprentissage Statistique et son Application en Industrie. Machine Learning module of the (OxWaSP) Centre for Doctoral Training, Oxford, 2018: Bayesian Optimisation and Variational Inference. NIPS workshop on Advances in Approximate Bayesian Inference (AABI), Long Beach, 2017: Approximate Bayesian Inference in Industry: Two Applications at Amazon. Machine Learning Tutorial at Imperial College, London, 2017: Bayesian Optimisation. 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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