Johannes Roth

Experience

Datasets & Tools

Publications

• 2026 Characterizing Universal Object Representations Across Vision Models Mahner*, Roth* et al. / arXiv preprint Shared first author
  We analyzed the object similarity structure of 162 diverse vision models to ask what, if anything, converges across architectures, objectives, and datasets. The paper separates universal from model-specific dimensions and links the more universal structure to interpretability, semantic image properties, macaque IT activity, and human similarity judgments.

• 2025 How to sample the world for understanding the visual system Roth & Hebart / CCN Oral
  Vision neuroscience runs on large fMRI datasets, but nobody had checked whether the stimulus images in these datasets actually cover what humans see in the real world. We built LAION-natural -a reference distribution of ~120M naturalistic photographs filtered from 2 billion LAION images using a CLIP-based classifier trained on 25k actively sampled labels. Then we measured coverage: ~50% of the visual-semantic space is missing from the two most widely used datasets (NSD and THINGS).

  The good news: you don't need millions of images to fix this. In both simulations and real fMRI data, out-of-distribution generalization saturates at 5-10k samples - as long as you draw them from a diverse enough pool. We compared seven sampling strategies (random, stratified, k-Means, Core-Set, effective dimensionality optimization, active learning) and found that pool diversity matters far more than which algorithm you use to sample from it.

  The pipeline processes billions of images using CLIP embeddings, Annoy indices for nearest-neighbor search, mini-batch k-Means clustering, and Ridge regression encoding models - all at a scale that runs on a university HPC cluster, not a cloud budget.

• 2025 Ten principles for reliable, efficient, and adaptable coding Roth et al. / Communications Psychology
  Most scientists learn to code informally - picking things up as they go, optimizing for "does it run?" over "will anyone else understand this?" This paper introduces a structured framework for writing better research code, built around the idea that researchers naturally switch between quick prototyping and careful development - and that being deliberate about which mode you're in makes all the difference.

  The ten principles span three tiers: organizing code (standardized project structures, version control, automation), writing reusable code (testing, documentation, clean interfaces), and collaborating (code review systems, shared knowledge bases, lab-wide standards). Already at 22k+ accesses, it clearly hit a nerve - these are problems every computational lab deals with but rarely talks about explicitly.

• 2025 Fine-grained image and category information in ventral visual pathway Badwal, Bergmann, Roth et al. / J Neuroscience

Get in Touch

Happy to chat about research, potential collaborations, or opportunities. Email is best. Also on LinkedIn, GitHub, Hugging Face, and Google Scholar.