Jakob Heiss

Jakob Heiss

About me

Since November 2019, I am a Ph.D. student advised by Prof. Josef Teichmann in the Stochastic Finance Group of ETH Zurich, and affiliated with ETH AI Center.

My main research interest is the mathematical theory of deep learning algorithms (in terms of their inductive bias). Additionally I work on quantifying epistemic uncertainty of deep neural networks and I apply deep learning to market design.

Prior to that, I received a B.Sc. and a M.Sc. (2019) in Technical Mathematics from the Technical University of Vienna.

What do I enjoy about being a researcher?

I love to work in teams on scientific exiting open questions trying to understand paradoxical phenomena. I particularly like to establish new points of view on such questions that can to some extend resolve such paradoxes. Further I enjoy engineering new methods and to diagnose, discuss, understand and improve methods in teams. I think it is important that every team member is passionate about the projects, and I am happy when students keep working with me in their free time for years after my supervision purely out of excitement about our work.

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