Machine Learning, Deep Learning, Probabilistic AI, Combinatorial Auctions, Market Design, Preference Elicitation

Since February 2019, Jakob is a Ph.D. student advised by Prof. Sven Seuken in the Computation and Economics Research Group at the Department of Informatics of the University of Zurich, where he works on machine learning-based market design.

Jakob received a B.Sc. (2015) and a M.Sc. (2018) in Mathematics from the Technical University of Vienna (specialization: Financial and Actuarial Mathematics). Additionally he received a M.Sc. (2018) in Quantitative Finance from the Vienna University of Economics and Business.

Since September 2021 he is a ETH AI Center affiliated PhD student.

Besides his studies, Jakob was as Workflow Chair part of the organizing committee of the twenty-third ACM Conference on Economics and Computation (EC'22), he worked in the Advanced Analytics team of the Raiffeisen Bank International as a data scientist. From September 2019 until March 2022, he was a board member of the Club Alpbach Zürich.

1. Deep Learning-powered Iterative Combinatorial Auctions. 
   Jakob Weissteiner and Sven Seuken.
   In Proceedings of the Thirty-fourth AAAI Conference on Artificial Intelligence (AAAI '20), New York, USA, February 2020.
   Working paper version from Oct 2020: [pdf] [code]
2. Fourier Analysis-based Iterative Combinatorial Auctions.
   Jakob Weissteiner*, Chris Wendler*, Sven Seuken, Ben Lubin, and Markus Püschel.
   In Proceedings of the Thirty-first International joint Conference on Artificial Intelligence (IJCAI '22), Vienna, AUT, July 2022.
   Full paper version including appendix:  [pdf] [code]
3. Monotone-Value Neural Networks: Exploiting Preference Monotonicity in Combinatorial Assignment
   Jakob Weissteiner*, Jakob Heiss*, Julien Siems* and Sven Seuken.
   In Proceedings of the Thirty-first International joint Conference on Artificial Intelligence (IJCAI '22), Vienna, AUT, July 2022.
   Full paper version including appendix: [pdf] [code]
4. NOMU: Neural Optimization-based Model Uncertainty
   Jakob Weissteiner*, Hanna Wutte*, Jakob Heiss*, Sven Seuken, and Josef Teichmann.
   In Proceedings of the Thirty-ninth International Conference on Machine Learning (ICML '22), Baltimore, USA, July 2022.
   Full paper version including appendix:  [pdf] [code]
5. Bayesian Optimization-based Combinatorial Assignment
   Jakob Weissteiner*, Jakob Heiss*, Julien Siems* and Sven Seuken
   In Proceedings of the Thirty-seventh AAAI Conference on Artificial Intelligence (AAAI'23), Washington, D.C., USA, February 2023.
   Full paper version including appendix: [pdf] [code]

*These authors contributed equally

1. Machine Learning-powered Course Allocation.
   Ermis Soumalias*, Behnoosh Zamanlooy*, Jakob Weissteiner and Sven Seuken.
   ArXiv preprint: [pdf]

1. Variable importance measures in classification and regression methods. 
   Jakob Weissteiner. Master's Thesis. Vienna University of Economics and Business, Austria, Sep 2018. [pdf] (PDF, 1 MB)
2. Über die Orderbuchmodellierung mit Markovschen Ketten in stetiger Zeit.
   Jakob Weissteiner. Master's Thesis. Technical University of Vienna, Austria, Jan 2018. [pdf] (PDF, 2 MB)

CV_01_2023 (PDF, 130 KB)

2021: Head teaching assistant for lecture Market Design and Machine Learning 
          Head teaching assistant for lecture Seminar: Advanced Topics in Economics and Computation

2020: Head teaching assistant for lecture Seminar: Advanced Topics in Economics and Computation

2019: Teaching assistant for lecture Economics and Computation
          Head teaching assistant for Seminar: Advanced Topics in Economics and Computation.

2018: Teaching assistant for lecture Economics and Computation
          Teaching assistant for lecture Mathematics II, Vienna University of Economics and Business.

2017: Teaching assistant for lecture Probability Theory, Vienna University of Economics and Business.

2016: Teaching assistant for lecture Risk Management in Finance and Insurance, Technical University of Vienna.

• Bayesian Optimization with Neural Networks
  Master Thesis by Marius Högger, Fall 2020