Adrian Hill
Adrian Hill
Doctoral Researcher
Adrian Hill is a PhD student at the TU Berlin Machine Learning Group. He received his M.Sc. in Engineering Science at TU Berlin with a focus on numerics and control theory. His research interests lie at the intersection of Machine Learning and dynamical systems.
- Neural ODEs
- Inverse Problems
- Molecular dynamics
- Automatic Differentiation
- Explainable AI
Adrian Hill, Neal McKee, Johannes Maeß, Stefan Bluecher, Klaus Robert Muller
Smoothed Differentiation Efficiently Mitigates Shattered Gradients in Explanations
Guillaume Dalle, Adrian Hill
A Common Interface for Automatic Differentiation
Jan Swierczek-Jereczek, Adrian Hill, Alexander Robinson, Jorge Alvarez-Solas, Marisa Montoya
A reduced-order representation of the West Antarctic Ice Sheet
Adrian Hill, Guillaume Dalle
Sparser, Better, Faster, Stronger: Efficient Automatic Differentiation for Sparse Jacobians and Hessian
Adrian Hill, Guillaume Dalle, Alexis Montoison
An Illustrated Guide to Automatic Sparse Differentiation
Open-Source Award for DifferentiationInterface.jl
DifferentiationInterface.jl, co-developed by BIFOLD researcher Adrian Hill, wins one of France’s Open Science Awards for making cutting-edge modeling and optimization more flexible, efficient, and open.