Work

Co-founder / CTO - early stage AI + retail startup, 2019-
Software Engineer - YouTube, Google, Paris France, 2016-2019
Software Engineer - Play Books, Google, New York NY, 2015-2016
Director of Data Science - Oyster, New York NY, 2014-2015
Data Scientist - Birchbox, New York NY, 2013-2014
Intern - Google, New York NY, 2012
Intern - The Echo Nest, Somerville MA, 2011
Intern - IREQ, Quebec, CAN, 2009
Intern - Sun Microsystems, Burlington MA, 2007

Education

Exec. MBA, INSEAD
Ph.D. Electrical Engineering, Columbia University
M.Sc. Computer Science, University of Montreal
B.Sc. Mathematics, University of Montreal

Publications

1. T. Bertin-Mahieux, Large-Scale Pattern Discovery in Music, Ph.D. thesis, Columbia University, 2013. [pdf][bib]

2. T. Bertin-Mahieux and D. Ellis, Large-scale cover song recognition using the 2D Fourier transform magnitude, In Proceedings of the 13th International Conference on Music Information Retrieval (ISMIR), 2012. [pdf] [bib]

3. B. McFee, T. Bertin-Mahieux, D. Ellis and G. Lanckriet, The Million Song Dataset Challenge, In Proceedings of the 4th International Workshop on Advances in Music Information Research (AdMIRe ’12), 2012. [pdf] [bib] [web]

4. T. Bertin-Mahieux, D. Ellis, B. Whitman and P. Lamere, The Million Song Dataset, In Proceedings of the 12th International Conference on Music Information Retrieval (ISMIR), 2011. [pdf] [bib] [dataset] [code]

5. T. Bertin-Mahieux and D. Ellis, Large-scale cover song recognition using hashed chroma landmarks, In Proceedings of the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), 2011. [pdf] [bib] (Best Paper Nominee)

6. T. Bertin-Mahieux, G. Grindlay, R. Weiss and D. Ellis, Evaluating music sequence models through missing data, In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2011. [pdf] [bib]

7. T. Bertin-Mahieux, R. Weiss and D. Ellis, Clustering beat-chroma patterns in a large music database, In Proceedings of the 11th International Conference on Music Information Retrieval (ISMIR), 2010. [pdf] [bib] [poster]

8. T. Bertin-Mahieux, D. Eck and M. Mandel, Automatic tagging of audio: The state-of-the-art. In Wenwu Wang, editor, Machine Audition: Principles, Algorithms and Systems. IGI Publishing, 2010. [amazon] [draft] [bib]

9. T. Bertin-Mahieux. Apprentissage statistique pour l’étiquetage de musique et la recommandation. M.Sc. thesis, University of Montreal. [pdf] (in French)

10. P.-A. Manzagol, T. Bertin-Mahieux and D. Eck. On the use of sparse time relative auditory codes for music. In Proceedings of the 9th International Conference on Music Information Retrieval (ISMIR), 2008. [pdf] [bib] (Best Student Paper Award)

11. T. Bertin-Mahieux, D. Eck, F. Maillet and P. Lamere, Autotagger: a model for predicting social tags from acoustic features on large music databases. In Journal of New Music Research, special issue: ”From genres to tags: Music Information Retrieval in the era of folksonomies.“, 37(2):151–165, 2008. [journal] [draft] [bib]

12. B. Kégl, T. Bertin-Mahieux and D. Eck, Metropolis-Hastings sampling in a FilterBoost music classifier, at International Workshop on Machine Learning and Music (ICML08 Workshop), 2008. [pdf] [slides] [video] [bib]

13. D. Eck, P. Lamere, T. Bertin-Mahieux and S. Green. Automatic generation of social tags for music recommendation. In Neural Information Processing Systems Conference (NIPS) 20, 2007. [pdf] [bib] [code]

14. D. Eck, T. Bertin-Mahieux and P. Lamere. Autotagging music using supervised machine learning. In Proceedings of the 8th International Conference on Music Information Retrieval (ISMIR), 2007. [pdf] [longer submission] [bib]