Maxence Ernoult

Principal Research Engineer at Rain Neuromorphics

Maxence Ernoult has a diverse work experience spanning from 2013 to 2022. Maxence began their career in 2013 as an Undergraduate Teaching Fellow at Lycée Sainte-Geneviève. In 2015, they became a Graduate Research Fellow at the Harvard John A. Paulson School of Engineering and Applied Sciences, where they conducted research in plasmonics at the Capasso Group and earned the 'Grand prix de recherche' in physics from Ecole Polytechnique. In 2016, they were a Graduate Research Fellow at the Université Pierre et Marie Curie (Paris VI) and a Graduate Teaching Fellow at École Polytechnique. Maxence also held a research fellowship at the Department of Engineering at the University of Cambridge, where they earned the best poster award at the European Aerosol conference in Tours. In 2017, they began their Ph.D. Student role at Sorbonne Université, where they worked on neuromorphic computing under the supervision of Prof. Julie Grollier (CNRS/Thalès). In 2020, they became a Research Fellow at Mila - Institut Québécois d'Intelligence Artificielle, working (remotely) under the supervision of Yoshua Bengio and Blake Richards on biologically plausible deep learning. Most recently, in 2021, they became a Research Staff Member at IBM, working on AI safety in the fields of uncertainty quantification, out-of-distribution detection, model calibration, and object detection.

Maxence Ernoult completed their education history with a Master of Advanced Studies (MASt) in Applied Mathematics and Theoretical Physics from the University of Cambridge between 2015 and 2016. Prior to that, they attended \u00c9cole Polytechnique from 2012 to 2015, and Lyc\u00e9e Sainte-Genevi\u00e8ve from 2009 to 2012. Maxence also holds several certifications from Coursera, including Structuring Machine Learning Projects (November 2017), Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization (October 2017), Neural Networks and Deep Learning (September 2017), Neural Networks for Machine Learning (July 2017), and Machine Learning (April 2017).

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