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Applied Machine Learning Researcher
Espoo, Finland
I received my Ph.D. in Applied Computer Science from Universite Paris-Saclay, France, in 2019 where I have worked on a variety of problems including rare event prediction, log mining and recommender systems in relation with aeronautical systems predictive maintenance with the company Safran. Since then I have worked on rare disease diagnostics and I am now exploring wireless machine learning topics. I joined Nokia Bell Labs in December 2021.
I am interested in applied machine learning and how to bridge the gap between theoretical research and engineering in a broad sense.
Ph.D. Computer Science, Universite Paris-Saclay, France 2019, Real-time anomaly detection with in-flight data: streaming anomaly detection with heterogeneous communicating agents, under the supervision of Sophie Chabridon and Yohan Petetin
Msc. Computer Science, Telecom Sudparis, France, 2015, specialized in distributed service architecture
Aussel, N., Dubourvieux, F., & Petetin, Y. (2019, August). Spatio-temporal convolutional neural networks for failure prediction. In GRETSI 2019: XXVIIème colloque francophone de traitement du signal et des images (pp. 1-5)
Aussel, N., Petetin, Y., & Chabridon, S. (2018, September). Improving performances of log mining for anomaly prediction through nlp-based log parsing. In 2018 IEEE 26th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS) (pp. 237-243). IEEE.
Aussel, N., Jaulin, S., Gandon, G., Petetin, Y., Fazli, E., & Chabridon, S. (2017, December). Predictive models of hard drive failures based on operational data. In 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) (pp. 619-625). IEEE.
Safran Innovation of the Year award, IFE recommender system, 2019
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