Benjamin Scellier
Principal Research Scientist at Rain
Zurich, Zurich, Switzerland
Summary
👤
Senior🎓
Top SchoolBenjamin Scellier is a principal research scientist at Rain, advancing energy-efficient machine learning hardware and algorithms. He designs processors that merge memory and computation and leverage analog physics to reduce ML energy costs by orders of magnitude. His research centers on Equilibrium Propagation and energy-based learning, developing physics-grounded algorithms for inference and training on novel analog systems. With a PhD in Deep Learning from Mila and master's degrees in Applied Mathematics and Statistics, his career spans Google, X, ETH Zürich, and leading roles in academia and industry, grounded in Zurich. He also builds practical software to simulate analog systems compatible with EP, including the energy-based-learning framework on GitHub. This unique blend of theory, hardware, and software reflects his aim to harmonize AI research with scalable, energy-conscious deployment.
10 years of coding experience
3 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Deep Learning, Doctor of Philosophy (Ph.D.), Deep Learning at Mila - Quebec Artificial Intelligence InstituteMaster’s Degree, Applied Mathematics, Master’s Degree, Applied Mathematics at Ecole polytechniqueMaster’s Degree, Statistics, Master’s Degree, Statistics at National University of Singapore
French, English, German, Chinese
Github Skills (8)
pymc10
mathematical10
mathematical-expressions10
theano10
expressions10
multi-dimensional9
python8
math7
Programming languages (1)
Github contributions (5)
bscellier/Towards-a-Biologically-Plausible-Backprop
Mar 2016 - Jan 2020
Contributions:11 commits, 12 pushes, 1 branch in 3 years 10 months
bscellier/Theano
Oct 2016 - Feb 2017
Contributions:13 pushes, 8 branches in 3 months
python-librarymulti-dimensionalpythonevaluatemathematical-expressions
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