July 2021: I just submitted my doctoral thesis and joined FAIR for a summer internship, where I’ll be working with Ari Morcos!

May 2021:The developmental trajectory of object recognition robustness: comparing children, adults, and CNNs“, a project of my Master student, Lukas Huber, has been accepted as a talk at VSS 2021!

December 2020: Since our paper “On the surprising similarities of supervised and self-supervised models” was selected as “Oral” at the NeurIPS 2020 workshop on the Shared Visual Representations in Human & Machine Intelligence, I had the pleasure to talk about our work comparing human perception against networks trained with and without labels:

November 2020: Proud to have received a NeurIPS 2020 Outstanding Reviewer Award (top 10% of reviewers)

November 2020:Shortcut learning in deep neural networks” has just been published by Nature Machine Intelligence!

May 2020: I am honoured to have been selected for an Elsevier/Vision Research Travel Award to attend the 2020 virtual meeting of the Vision Sciences Society

February 2020: Spektrum der Wissenschaft, the German edition of the Scientific American (popular science magazine), has printed an article featuring our work on shape vs. texture

July 2019: Our work has been featured by an article in Quanta Magazine: “Where We See Shapes, AI Sees Textures”

July 2019: I’ve attended the Computational Vision Summer School (CVSS) in Freudenstadt, Germany

May 2019: I’ve given a talk at VSS 2019 about “Inducing a human-like shape bias leads to emergent human-level distortion robustness in CNNs”

May 2019: Hosted by Robbe Goris, I’ve visited UT Austin‘s Center for Perceptual Systems for a few days where I gave a talk about “Where humans still outperform Convolutional Neural Networks—and how to narrow the gap”

May 2019: I’ve given a talk at ICLR 2019 about “ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness”:

The talk is available here:

March 2019: I’ve been invited to give a talk at the AI Meetup Hamburg about “The (in)corrigible laziness of convolutional neural networks”