I am a machine learning researcher building statistical methods to enhance our grasp of biological systems in neuroscience and the life sciences more broadly. My main interest is to understand how machine learning systems can be used to infer computations and dynamics underlying a phenomenon using observational and interventional data.

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 interested joining us, have a look at our current openings.

I am also very active in computing education for more than 10 years now. In 2019, I founded KI macht Schule (“AI at schools”), a non-profit organization teaching ML basics to high-school students. We provide teachers with modern teaching materials, AI tools and infrastructure in our open teaching hub, and offer courses on AI for students and teachers in Germany, Switzerland and Austria. We have a great network of volunteers in more than 9 cities who do science outreach directly in schools. We also have a growing team of instructional designers, software engineers and AI trainers to extend our platform, build new teaching materials and conduct teacher trainings. If you want to help educating the next generation of students and make them literate in AI, consider to join our team!

Finally, I am interested in deploying ML solutions in the real world. In 2023, I co-founded Kinematik AI, a company offering customized machine intelligence solutions in the biopharma and animal healthcare sector. If you have a business usecase for our customized behavioral analysis software, please reach out.

Background

I pursued my doctoral studies at the Max Planck International Research School for Intelligent School and the Swiss Federal Institute of Technology Lausanne (EPFL), advised by Matthias Bethge and Mackenzie Mathis in the ELLIS PhD & PostDoc program. My PhD studies were supported by a Google PhD Fellowship in Computational Neural and Cognitive Sciences.

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.

Short Bio (for talks etc.)

Steffen Schneider is a tenure-track group leader at Helmholtz Munich and associated faculty at the Graduate School of Systemic Neurosciences at LMU Munich and the Munich Center for Machine Learning. His research focuses on statistical methods for modeling and understanding biological dynamical systems. He earned his PhD in the ELLIS PhD program at EPFL and the Tübingen AI Center, advised by Mackenzie Mathis and Matthias Bethge. Previously, he was an AI Resident at Meta with Michael Auli, working on self-supervised contrastive learning for speech recognition. Beyond research, in 2019 he founded the non-profit KI macht Schule (“AI at Schools”) to advance AI education and statistical literacy. His PhD was supported by a Google PhD Fellowship, and he was recently named Early Career Scientist of the Year 2024 by academics for his contributions to research and education.