Alex Hernandez-Garcia

I am a last-year PhD student at the University of Osnabrück and EyeQuant with Prof. Peter König, as a fellow of the Marie Sklodowska-Curie ITN NexGenVis. I obtained my B.Sc and M.Sc. at the University Carlos III of Madrid and I have been a visiting PhD student at the University of Cambridge with Dr. Tim Kietzmann and at the Spinoza Center for Neuroimaging with Dr. Serge Dumoulin.

My main research focus is on brain-inspired deep learning and computational neuroscience. I believe that machine learning and neuroscience can highly benefit each other and my aim is exploring and exploiting these interactions.

I am currently looking for a postdoc position at a lab where I can keep working on computational neuroscience and machine learning.



Our pre-print “Global visual salience of competing stimuli” is out on PsyArXiv! We run an eye-tracking experiment showing natural images side by side and trained a machine learning model to predict to direction of the first fixation (left or right) given a pair of images. The coefficients learned for each image characterize the likelihood of each image to attract the first saccade, when shown next to another competing stimulus, which we called global visual salience.

We have just uploaded to arXiv a new pre-print, “Learning robust visual representations using data augmentation invariance”. We first demonstrate that typical CNNs are remarkably sensitive to the common transformations of data augmentation. Second, taking inspiration from the invariance of the visual cortex to identity-preserving transformations, we add a loss term to the objective function that successfully learns more robust features.

This week I am attending the VSS 2019 meeting and presenting a poster, “Saliency and the population receptive field model to identify images from brain activity”, the project I carried out during my internship at the Spinoza Centre for Neuroimaging in Amsterdam.