Alexis Perrier is a data analyst and professor of data science at the Université Gustave Eiffel, OpenClassrooms, and UM6P-Emines. Contributor to InfoQ, Manning, ODSC, and Datacamp.

He has written two books on machine learning on AWS and Google Cloud, published by Packt.

The focus of his research revolves around Bayesian analysis (probabilistic programming) as a part of natural language processing (NLP) applied to social digital dynamics and to the practical applications of machine learning (ML). He has a PhD in signal processing from TelecomParis 95.

Recent publications:

[2020] Ramaciotti Morales P., Benbouzid B., Gauthier E., Perrier A., “The Effects of Disaggregated Factors of YouTube Recommendations in Diversity”, 6th International Conference on Computational Social Science IC2S2, 17-20 July, 2020

[2020] Benbouzid, B., Emma Gauthier, Alexis Perrier and Pedro Ramaciotti, “YouTube as Three Recommendation Networks. The Case of the French Media and Political Sphere”, INSNA Sunbelt Conference, Paris 2020.

[2013] Nuernberg C., Perrier A. “Behind the Scenes with MOOCs: Berklee College of Music’s Experience Developing, Running, and Evaluating Courses through Coursera.” The Journal of Continuing Higher Education, 77:136

https://www.researchgate.net/profile/Alexis_Perrier