In 2011, I defended my phD in applied mathematics on a clustering approach for high-dimensional data. After that, I spent one year in a post-doctoral position at the University of Angers working with Sebastien Loustau.
In 2013, I went to Seattle and I found a position as a statistician at autograph.me . I worked for this company during 3 years.
Since October 2016, I am a statistician @lumenai ... or a data scientist - for using a trendier word ;) -.
Life is like riding a bicycle. To keep your balance, you must keep moving.
since October 2016
Coming from the academic area, I am very attached to stay connected to the research field in Machine Learning and Statistics. Of course, as a data scientist I love challenging the data and solving use cases. However, another fundamental task for me is to keep my knowledge state-of-the-art with the Machine Learning / Statistics community research.
Our company enables me to work this way because we are a research lab made of experts of different fields (computer scientists, mathematicians, developers, etc). This gives you a great energy to work, to learn and to create even if the team is spread in different places. Moreover, we are constantly in touch with scientific and academic research experts either by inviting them to work with us on specific subjects, or by meeting them to statistical conferences, seminars or meetups.
Journal & conference papers
Time series clustering
S. Loustau and C. Saumard, k-Nearest clusters for time series classification, JDS, 2018.
On model-based clustering
2011 - 2014
C. Bouveyron and C. Brunet, Model-based clustering for high-dimensional data: A review, Computational Statistics and data analysis, vol. 71, pp. 52-78, 2014.
C. Bouveyron and C. Brunet, Discriminative variable selection for clustering with the sparse Fisher-EM algorithm, Computational Statistics 28:3, 2013.
C. Bouveyron and C. Brunet, Theoretical and practical considerations on the convergence properties of the Fisher-EM algorithm, Journal of Multivariate Analysis, 2012.
C. Bouveyron and C. Brunet, Probabilistic Fisher discriminant analysis: A robust and flexible alternative to Fisher discriminant analysis, Neurocomputing, vol. 90 (1), pp. 12-22, 2012.
C. Bouveyron and C. Brunet, Simultaneously model-based clustering and visualization in the Fisher discriminative subspace, Statistics and Computing, 22(1), 2012.
C. Bouveyron and C. Brunet, On the estimation of the latent discriminative subspace in the Fisher-EM algorithm, Journal de la SFDS, 2011.
Meetup ML Rennes
Interventions and videos
Anomaly Detection in Maritime Traffic
Sylvain Barthélémy, CEO TAC economics