Steffen Schneider

Group Leader

Helmholtz Munich

My goal is to build machine learning tools and statistical methods for decompiling intelligent behavior. In my research group at Helmholtz Munich, we develop machine learning algorithms for representation learning and inference of nonlinear system dynamics, study how large and multi-modal biological datasets can be compressed into foundation models, and study their mechanistic interpretability.

If you are looking for opportunities for a position as PhD student, postdoc, research engineer, research assistant, or an internship, Bachelor or Master’s thesis, have a look at my past work and student projects and ping me if you’re interested in working with us.

I pursued my doctoral studies at the Swiss Federal Institute of Technology Lausanne (EPFL) and the International Max Planck Research School for Intelligent Systems, advised by Mackenzie Mathis and Matthias Bethge in the ELLIS PhD & PostDoc program.

During my PhD, I also worked as a Research Scientist Intern advised by Laurens van der Maaten and Ishan Misra on multimodal representation learning in the FAIR team at Meta NYC, and at Amazon Web Services in Tübingen as an Applied Science Intern where I worked on self-learning and object centric representations with Peter Gehler, Bernhard Schölkopf and Matthias Bethge.

Prior to starting my PhD, I worked on wav2vec and vq-wav2vec, two self-supervised representation learning algorithms for speech processing with Michael Auli, Alexei Baevski and Ronan Collobert at Facebook AI Research in Menlo Park, CA.

Aside from my research, I’m a strong supporter of exposing children to modern computer science topics early on during their school education. That’s why I co-founded and advised IT4Kids to teach CS in elementary school, KI macht Schule to teach AI and Machine Learning fundamentals in high school and helped organizing the German National Competition in AI for high school students. If you want to join our team at KI macht Schule and bring AI education to every school in Germany, Austria and Switzerland, don’t hesitate to reach out!


  • Self-Supervised Learning
  • Sensorimotor Adaptation
  • AI for Life Sciences
  • Domain Adaptation
  • Computational Neuroscience

Education & Research

  • Group Leader, Dynamical Inference Lab

    Helmholtz Munich

    from 2024

  • Visiting PhD Student (ELLIS)

    Swiss Federal Institute of Technology Lausanne (EPFL)

    2021 - 2023

  • PhD Candidate, Machine Learning

    Intl. Max Planck Research School, Tübingen

    2019 - 2023

  • Research Scientist Intern

    FAIR at Meta, New York City

    Spring 2022

  • Applied Science Intern

    Amazon Web Services, Tübingen

    Fall 2020

  • AI Resident, Self-Supervised Learning for Speech Recognition

    Facebook AI Research, Menlo Park, CA

    2018 - 2019

  • MSc in Neuroengineering

    Technical University of Munich

    2016 - 2018

  • BSc in Electrical Engineering, Information Technology and Computer Engineering

    RWTH Aachen University

    2013 - 2016


Latest Research

  • Machine learning for Neuroscience: Growing datasets in neuroscience require tools for jointly analyzing high-dimensional behavioral and neural recordings. We built a contrastive learning framework for estimating Consistent EmBeddings of high-dimensional Recordings using Auxiliary variables (CEBRA). Stay tuned for the code release.
  • Robustness & Adaptation in Computer Vision: We demonstrated that robustness estimates of ImageNet-scale architectures can be drastically improved by adapting models with simple domain adaptation methods. Batch norm adaptation yields consistent 5-15% points gains across various model architectures. Self-learning can further improve scores, even for models pre-trained on large amounts of data. We obtain scores as low as 22% mCE on ImageNet-C, 17.4% top-1 error on ImageNet-R and 14.8% top-1 error on ImageNet-A. Check out robusta, our PyTorch library for robustness & adaptation on Github. Beyond ImageNet, we demonstrate improved ood. performance of pre-trained pose estimation networks and discuss principles, pitfalls and perspectives of pose estimation algorithms in our recent Neuron primer.
  • Self-supervised Learning: wav2vec and vq-wav2vec demonstrated the effectiveness of contrastive learning for reducing the need for labeled data in speech recognition models, and a lot of progress in contrastive learning has been obtained in other fields of machine learning. We worked on understanding the effectiveness of contrastive pre-training and found that contrastive learning can invert the data-generating process.


  • October 2022: We updated the CEBRA pre-print with additional experiments and identifiability results. Make sure to watch the repository to stay tuned about updates on the code release.
  • May 2022: I joined the FAIR team at Meta in New York as a research scientist intern! I will be working with Laurens van der Maaten and Ishan Misra on multimodal representation learning.
  • April 2022: We released the pre-print of CEBRA, our contrastive representation learning method for joint behavioral and neural data. Read more at cebra.ai!
  • Jan 2021: I started as a visiting PhD student at Campus Biotech, EPFL, in Geneva.
  • Sep 2020: I joined Amazon Web Services in Tübingen on September 1st as a full-time Applied Science intern, advised by Matthias Bethge, Bernhard Schölkopf and Peter Gehler.
  • Aug 2020: Our team at KI macht Schule organized a four day AI & ML bootcamp for students; learn more at KI-Camp.de.
  • Feb 2020: Together with fellow doctoral students of the Tübingen AI Competence Center, I am organizing a one-day doctoral symposium in February.
  • Feb 2020: I joined the Mouse Motor Lab led by Mackenzie Mathis as an ELLIS PhD student. I’ll be working at the Rowland Institute at Harvard in February and March.
  • Jan 2020: I co-organized a course on quantum machine learning with Luisa Eck (LMU Munich) and Lucas Stoffl (TU Munich) at the CdE winter school 2020 in Kaub and Oberwesel, Germany.

Meetings, Workshops & Talks