My goal is to build machine learning models capable of approaching the performance of biological brains in terms of flexibility to changes in tasks and environments. Drawing inspiration from adaptation behavior of biological systems, I study methods for domain adaptation, multi-task and self-supervised learning.

I pursue my doctoral studies at the International Max Planck Research School for Intelligent Systems advised by Matthias Bethge and Mackenzie Mathis at EPFL. I will visit EPFL in my second PhD year as part of the ELLIS PhD & PostDoc program. In September 2020, I joined Amazon Web Services in Tübingen as an Applied Science Intern where I am advised by Matthias Bethge, Bernhard Schölkopf and Peter Gehler.

Previously, I worked on self-supervised representation learning for speech processing with Michael Auli, Alexei Baevski and Ronan Collobert at Facebook AI Research in Menlo Park, CA. For my Master’s thesis, I worked on Domain Adaptation in Brains and Machines with Matthias Bethge and Alexander Ecker at the Max Planck Institute for Intelligent Systems and University of Tübingen and Jakob Macke at TU Munich.

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 and bring AI education to every school in Germany, don’t hesitate to reach out!

Likewise, if you are a student looking for opportunities for an internship, Bachelor or Master’s thesis, have a look at my past work and current student projects and ping me if you’re interested in working with me.


  • Self-Supervised Learning
  • Domain Adaptation
  • Sensorimotor Adaptation
  • Information Theory
  • Computational Neuroscience

Education & Research

  • Incoming Visiting PhD Student (ELLIS), 2021

    École polytechnique fédérale de Lausanne

  • Applied Science Intern, Fall 2020

    Amazon Web Services, Tübingen

  • PhD Candidate in Machine Learning and Neuroscience, from 2019

    Intl. Max Planck Graduate School for Intelligent Systems, Tübingen

  • AI Resident, Self-Supervised Learning for Speech Recognition, 2018 - 2019

    Facebook AI Research, Menlo Park, CA

  • MSc in Neuroengineering, 2016 - 2018

    Technical University of Munich

  • BSc in Electrical Engineering, Information Technology and Computer Engineering, 2013 - 2016

    RWTH Aachen University


Meetings, Workshops & Talks



Student Projects

I am always looking for motivated students interested in joining me in the Bethge and/or Mathis lab. If you’re interested in working with me on topics around robustness, domain adaptation, reinforcement learning and self-supervised learning at the intersection of neuroscience and machine learning, please contact me at

I am currently working with:

  • Shubham Krishna started as a research assistant in the Bethge lab. Shubham works on topics around invariant representation learning and pruning techniques to better understand adaptation mechanisms in DNNs.
  • Khushdeep Singh joined the Bethge lab as a Master thesis student in April. Khushdeep works on benchmarking and improving the robustness of reinforcement learning algorithms.
  • Jin Hwa Lee joined the Bethge & Mathis labs in September. Jin is building self-supervised representation learning algorithms for analyzing neuroscience datasets.
  • Xingying Chen joined mid september as a summer intern in the Bethge & Mathis labs. Xingying is modeling adaptation paradigms in neuroscience using reinforcement learning.
  • Jan Hansen-Palmus from the Bringmann group at Uni Tübingen is co-advised by Evgenia Rusak and me. Jan writes his Bachelor’s thesis on pseudo-labeling approaches for unsupervised domain adaptation.

In the past, I also worked with:

  • Mert Yüksekgönül (now visiting researcher in Poggio & Sinha Labs at MIT). Mert worked on 3D lifting approaches for analyzing behavioral data.


A full and up-to-date list is also available on Google Scholar.


KI macht Schule

KI macht Schule provides classes in AI & Machine Learning for German high school students


An Imaging Platform for Neurobehavioral Research

Biomodels Retreat

Establishing fruitful collaborations between biologists, computer scientists and mathematicians in a yearly one-week retreat.


M.Sc. Neuroengineering Student Blog with latest information about our study program and events.

Ecurie Aix eace04/05

Contributions include the design of electric control units

Campus Weggemeinschaft

Homepage der Campus Weggemeinschaft


IT4Kids provides computer science classes to elementary school pupils - Providing software, teaching materials and easy communication …