About me

I am a first-year PhD student in computer science at UC Berkeley, supervised by Stuart Russell. My research focuses on AI existential safety, particularly on goal misgeneralization and learned optimization in machine learning models.

Previously, I studied computer science and mathematics at the University of Toronto, the University of Oxford, and the Technical University of Berlin. I am grateful to be supported by fellowships from Open Philanthropy and the Future of Life Institute, and for past support from the Center on Long-Term Risk Fund.

My great passion is playing the double bass. Before my research career, I studied the double bass in Stuttgart and Nuremberg and played with the Munich Philharmonic.

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Publications

1. Cem Anil*, Ashwini Pokle*, Kaiqu Liang*, Johannes Treutlein, Yuhuai Wu, Shaojie Bai, J. Zico Kolter, and Roger Grosse. Path Independent Equilibrium Models Can Better Exploit Test-Time Computation. NeurIPS 2022.
2. Timon Willi*, Alistair Letcher*, Johannes Treutlein*, Jakob Foerster. COLA: Consistent Learning with Opponent-Learning Awareness. ICML 2022. [Slides, Code]
3. Johannes Treutlein, Michael Dennis, Caspar Oesterheld, and Jakob Foerster. A New Formalism, Method and Open Issues for Zero-Shot Coordination. ICML 2021. [Poster, Video, Code]
4. William MacAskill, Aron Vallinder, Caspar Oesterheld, Carl Shulman, and Johannes Treutlein. The Evidentialist’s Wager. The Journal of Philosophy, Volume 118, Issue 6, June 2021. [Penultimate Draft]

Working papers

1. Evan Hubinger, Adam Jermyn, Johannes Treutlein, Rubi Hudson, Kate Woolverton. Conditioning Predictive Models: Risks and Strategies. arXiv preprint, 2023.
2. Caspar Oesterheld, Johannes Treutlein, Roger Grosse, Vincent Conitzer, Jakob Foerster. Similarity-based Cooperation. arXiv preprint, 2022.
3. Julian Stastny, Maxime Riché, Alexander Lyzhov, Johannes Treutlein, Allan Dafoe and Jesse Clifton. Normative disagreement as a challenge for Cooperative AI. Accepted at the NeurIPS 2021 StratML and Cooperative AI workshops.

* Equal contribution