I am a research scientist at Google DeepMind. Previously, I spent a year in PostDoc at the Vision Lab in University of California, Los Angeles (UCLA). I obtained my PhD from the signal processing laboratory in EPFL in 2016, and my M.Sc. in Electrical Engineering from EPFL in 2012. I was awarded the IBM PhD Fellowship awards
for the academic years 2013-2014 and 2015-2016.
I am broadly interested in challenging problems related to computer vision and machine learning.
In my recent research, I have focused on analyzing the robustness and invariance of classifiers to transformations from an empirical and theoretical perspective.
Email: afawzi [AT] google [dot] com
Empirical study of the topology and geometry of deep networks
Alhussein Fawzi, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard, Stefano Soatto
IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2014 and earlier
Universal adversarial perturbations
Implementation of the algorithm for computing universal perturbationsGitHub page
Implementation of the DeepFool algorithm for fooling deep neural networks GitHub page
Learning Algorithm for Soft-Thresholding (LAST)
Implementation of the DC-based dictionary learning algorithm for soft-thresholding based based classifiers. Download MATLAB code.