Dr. Jakob Weissteiner

As of September 2023 Jakob works as a Risk Modeling and Analytics Specialist at UBS in Zurich.

From May 2023 - September 2023, Jakob was a Postdoctoral Researcher in the Computation and Economics Research Group at the Department of Informatics of the University of Zurich, where he worked on machine learning-based market design.

In April 2023 Jakob received a Ph.D. (summa cum laude) advised by Prof. Sven Seuken in the Computation and Economics Research Group at the Department of Informatics of the University of Zurich, where he worked 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). Additionally, he gained first working experience in the Advanced Analytics team of the Raiffeisen Bank International as a Junior Data Scientist.

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

1. Machine Learning-powered Combinatorial Clock Auction.
   Ermis Soumalias*, Jakob Weissteiner*, Jakob Heiss and Sven Seuken.
   In Proceedings of the Thirty-eight AAAI Conference on Artificial Intelligence (AAAI'24), Vancouver, Canada, February 2024.
   Full paper version including appendix: [pdf][code]
2. 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]
3. 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]
4. 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]
5. 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]
6. 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.
   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.
   Full paper version including appendix: [pdf]

Integrating Advanced Machine Learning Methods Into Market Mechanisms (PDF, 19 MB)
Jakob Weissteiner, University of Zurich, Mar 2023 

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

CV_Mai_2023 (PDF, 58 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.

1. ML-based Uncertainty Quantification on Real World Data
   Semester Thesis of Aurelio Dolfini, ETH Zurich, 2022
2. Bayesian Optimization with Neural Networks
   Master Thesis of Marius Högger, University of Zurich, 2020