Phd/doctoral Enschede, Netherlands Apply by
University of Twente
Department of Applied Mathematics
Drienerlolaan 5
7522 NB Enschede

We are looking for a talented, research-oriented PhD candidate to join the project "Regularized dynamics in optimization schemes for neural networks-based models".

Gradient methods, such as gradient descent and stochastic gradient descent, achieve remarkable performances in neural network training but suffer typically from strong instability that makes the optimization of specific architectures challenging, time-consuming, and susceptible to attacks. For this reason, a dynamic regularization of the optimization algorithm is often necessary. Our goal is to lay a solid theoretical foundation able to unify different regularization strategies by looking at the gradient-flow structure of the training algorithm. We will use these theoretical insights to study sparsity properties of neural networks during training, analyzing how they depend on the chosen regularization. We will then develop a robustness theory for dynamically regularized neural networks able to explain and defend against adversarial attacks. Applications to biological data-driven models such as CT-reconstruction, single-particle tracking (SPT) for fluorescence microscopy and microbubbles flow for drug delivery will be considered.

The PhD candidate will work under the supervision of Dr. Marcello Carioni and will be part of the group Mathematics of Imaging and Artificial Intelligence (MIA) headed by Prof. Christoph Brune at the department of Applied Mathematics. 

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Contact Person
Dr. Marcello Carioni