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I am an incoming assistant professor at Concordia University in the Department of Computer Science and Software Engineering, and Mila.
My research advances ML algorithms and software through consideration of quantities beyond the gradient, such as curvature information.
I am interested in neural network training algorithms (Shampoo, SOAP, K-FAC, …), automatic differentiation (Taylor mode, PINNs, …), information geometry (Fisher information, …) as well as curvature approximations and their application outside optimization (model merging, training data attribution, unlearning, bi-level problems for safety, …).
Before, I completed a Postdoc at the Vector Institute in Toronto, obtained my PhD in Computer Science from the University of Tübingen, and a Master's and Bachelor's degree in Physics from the University of Stuttgart.
To make my research accessible, I provide efficient open source implementations.
Check out my Github profile for an always up-to-date list.
An ongoing note and code snippet collection. To navigate to a post, click on its title.
How to arrive at the Kronecker-factorized Hessian approximations, how to generalize them to transpose convolutions, and how to link them to other approximations.
A utility function to combine nested einsum expressions.
BN spoils the concept of per-sample quantities (like individual gradients). Which structure remains?
Example use case for Hessian-vector products in PyTorch (using a utility function in BackPACK).
My website is an .org file exported to HTML with ReadTheOrg. This snippet is for new posts.
Org mode has been a great and free tool throughout, and after, my PhD (task and time management, notes, website, …). You can support its maintainers!
Author: Felix Dangel
Created: 2025-11-10 Mon 13:23
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