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 at ETH Zurich, and affiliated with the ETH AI Center.

My main research interest is the mathematical theory of deep learning algorithms (in terms of their inductive bias and multi-task learning). Additionally, I work on quantifying epistemic uncertainty of deep neural networks, and I apply deep learning to market design (preference elicitation for combinatorial auctions). I also work on Neural Jump ODEs for irregularly observed time series and on compression of neural networks.

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

What do I enjoy about being a researcher?

I love working in teams on scientifically exciting open questions, trying to understand paradoxical phenomena. I particularly like to establish new perspectives on such questions, which can partially resolve such paradoxes. I also enjoy developing new methods and diagnosing, discussing, understanding, and improving methods in teams. It is important to me that each team member is passionate about the projects, and I am happy when students continue to work with me in their free time for years after my supervision purely out of excitement about our work.