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The Symbiosis of Deep Learning and Differential Equations II
Michael Poli · Winnie Xu · Estefany Kelly Buchanan · Maryam Hosseini · Luca Celotti · Martin Magill · Ermal Rrapaj · Stefano Massaroli · Patrick Kidger · Archis Joglekar · Animesh Garg · David Duvenaud

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Author Information

Michael Poli (Stanford University)
Winnie Xu (University of Toronto / Stanford University)
Estefany Kelly Buchanan (Columbia University)
Maryam Hosseini (Université de Sherbrooke)
Luca Celotti (Université de Sherbrooke)
Martin Magill (University of Ontario Institute of Technology)

I am a PhD student in modelling and computational science under the supervision of Dr. Hendrick de Haan in the cNAB.LAB for computational nanobiophysics. Recently, I’ve been interested in using deep neural networks to solve the partial differential equations that describe electric fields and molecular transport through nanofluidic devices. I’ve also been using these mathematical problems as a controlled setting in which to study deep neural networks themselves.

Ermal Rrapaj (University of California Berkeley)
Stefano Massaroli (The University of Tokyo)
Patrick Kidger (University of Oxford)
Archis Joglekar (University of Michigan - Ann Arbor)
Animesh Garg (University of Toronto, Nvidia, Vector Institute)

I am a CIFAR AI Chair Assistant Professor of Computer Science at the University of Toronto, a Faculty Member at the Vector Institute, and Sr. Researcher at Nvidia. My current research focuses on machine learning for perception and control in robotics.

David Duvenaud (University of Toronto)

David Duvenaud is an assistant professor in computer science at the University of Toronto. His research focuses on continuous-time models, latent-variable models, and deep learning. His postdoc was done at Harvard University, and his Ph.D. at the University of Cambridge. David also co-founded Invenia, an energy forecasting and trading company.

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