benjamin at rain dot ai
I am a principal research scientist at Rain.
Before that, I was a resident at Google X, then a resident at Google Zurich, and then a postdoctoral researcher at the Department of Mathematics of ETH Zurich.
I completed my PhD at Mila (University of Montreal) under the supervision of Yoshua Bengio.
My main research interests lie at the interface of deep learning, physics and neuroscience. I am more particularly interested in physics-based computation and learning.
Much of my current research revolves around equilibrium propagation (EP), a novel mathematical framework for gradient-descent-based machine learning. Compared to the more conventional framework based on automatic differentiation (i.e. "backpropagation"), the benefit of the EP framework is that inference and gradient computation are performed using the same physical laws. By suggesting a path to perform the desired computations (inference and learning) more efficiently, this framework may have implications for the design of novel hardware ("accelerators") for deep learning. For more information on EP, Chapter 2 of my PhD thesis provides a (relatively recent) overview. See also these notes on EP.