Member of team :
ENSEA, CYU Saint-Martin
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Joffrey Becker trained in social anthropology at EHESS (M.A, Ph.D) and was a member of the SPEAP program created by Bruno Latour at the Institut d’Études Politiques de Paris (M.A). He holds a teaching chair at the École nationale supérieure de l’électronique et de ses applications (ENSEA), and his research is conducted at the Neurocybernetics Team of the ETIS Lab. Joffrey Becker is also a research associate in the Anthropology of Life team at the Laboratoire d’Anthropologie Sociale (UMR 7130) of the Collège de France and a member of the Psyphine interdisciplinary research group (Université de Lorraine, Neurodegenerative Diseases Institute Bordeaux, Jean Nicod Institute Paris). He worked with various research teams from the public and private sectors (CNRS, MIT, INRIA, Google, Orange, ENSAD).
Personal website: www.joffreybecker.fr
Becker’s research focuses on robotics and artificial intelligence, and more particularly on the relationship between humans and machines. It aims to better understand how so-called intelligent machines question our models on an ontological, interactional and societal level, regardless of their form. In a collaborative, interdisciplinary and sometimes critical perspective, the challenge is to study how objects resulting from robotics and artificial intelligence reconfigure relationships and practices. This work has led to numerous public presentations, the publication of articles and book chapters, and the writing of a book entitled Humanoïdes, Expérimentations croisées entre arts et sciences, published in 2015 by the Presses Universitaires de Paris Ouest.
Google Scholar [https://scholar.google.com/citations?user=stvKx2QAAAAJ&hl=fr]
- Integrated neuro-robotic approaches for autonomous vehicle localisation and navigation
- Machine Learning-based Robust Physical Layer Authentication Using Angle of Arrival Estimation
- Deep Reinforcement Learning-Based Network Slicing Algorithm for 5G Heterogenous Services
- Wing Interferential Patterns (WIPs) and machine learning for the classification of some Aedes species of medical interest