2021
Advances in Reliably Evaluating and Improving Adversarial Robustness
I presented a brief summary of my PhD research at the annual meeting of the Bosch Research Foundation.
2020
Making Machine Learning Robust Against Adversarial Inputs
I presented my latest research results at the annual meeting of the Bosch Research Foundation.
How to evaluate adversarial robustness?
We tought an interactive workshop on evaluating adversarial robustness at the 1st Tübingen Robust Learning Symposium.
2019
How to evaluate adversarial robustness?
I tought an interactive workshop on evaluating adversarial robustness at the Deep Learning and Inverse Problems Autumn School in Bremen.
Modeling patterns of smartphone usage and their relationship to cognitive health
Presented my intership work at an internal Apple Machine Learning Research Symposium
Making Machine Learning Robust Against Adversarial Inputs
I presented my latest research results at the annual meeting of the Bosch Research Foundation.
Linear Region Attack
I presented our work on a new type of geometric adversarial attack in the Schölkopf group at the Max Planck Institute for Intelligent Systems
2018
Towards Robust Neural Networks
I presented my latest research results at the annual meeting of the Bosch Research Foundation.
2017
TensorFlow Workshop for Computational Neuroscientists
We gave an interactive tutorial on TensorFlow targeted at compututational neuroscientists.
Foolbox: A Python toolbox to benchmark the robustness of machine learning models.
I presented our work on Foolbox and the Robust Vision Benchmark in the Reliable Machine Learning in the Wild workshop at ICML 2017 in Sydney.
2015
