Alex Hernandez-Garcia

I have recently submitted the dissertation of my PhD, which I have done 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 their synergies.

Besides my research, I am a strong proponent of open science in the broadest sense, and I try to contribute to it with my actions and by getting involved in discussions about how to make science more inclusive, open, reproducible, transparent and have less impact on the environment.



It’s difficult and sad times for the world. COVID-19 is causing a lot of suffering to many people and keeping most of us at home almost all day. In Spanish we often say the idiom “to make a virtue out of necessity” to highlight the creativity that arises in the face of adversity. I thought of it after a great day attending a quarantine-induced online workshop on computational neuroscience organised by Pau Vilimilis Aceituno. Great talks, great people, all from home!

Since last October I am part of the Max Planck School of Cognition as a 0-year student. The past two weeks I had the chance to attend the second Cognition Academy, where I met the new PhD students and the rest of mentors of the programme. Not only was it a fun event, but it was also full of fantastic talks and workshops!

It has been a blast to attend the International Conference on Computer Vision (ICCV) and especially to participate in the workshop “Should we preregister experiments in computer vision?”. The talks by the invited speakers and the discussion were really inspiring! It was also great to present our pre-registered paper Learning representational invariance instead of categorization.