CV

Education

Visiting Ph.D. Scholar in Statistics and Operational Research,
Columbia University (New York City, USA, ongoing)
Ph.D in Statistics,
University of Geneva (Switzerland, ongoing)
Supplementary Minor in Computational Science and Engineering,
EPFL (Lausanne, Switzerland, 2020)
M.Sc. in Applied Mathematics,
EPFL (Lausanne, Switzerland, 2020)
B.Sc. in Mathematics,
EPFL (Lausanne, Switzerland, 2017)

Work experience

Research And Teaching Assistant at University of Geneva (Switzerland, 2020-2025)

Teaching

Co-created and co-taught master program course "Advanced Topics in Machine Learning and Artificial Intelligence" about natural language processing (GPT) and reinforcement learning (autonomous robots).
Managing teaching assistant for B.Sc. Probability class (~400 students per semester, team of 4 assistants, for 5 semesters).
Assisted in reworking the "Machine Learning" master program course with modern tools, and taught practical lectures and tutorials (4 semesters).

Research

Successful research project management, from brainstorming stage to final execution, for all undertaken projects.
Effective long-term collaboration with leading and junior scientists from various institutions and countries.

Data Science Consultant for Services Industriels de Genève (SIG) (Geneva, Switzerland, Aug-Dec 2019)

Project management, needs analysis and concrete solution proposals for better data use and analysis procedures leading to a significantly better use of resources in finances, marketing and business intelligence.
Predictive analysis for customer satisfaction enabling a better understanding of the market, and reduced churn probabilities.

Junior Data Scientist at Qim info SA (Geneva, Switzerland, Jul-Dec 2019)

Development of an AI mobile app for predicting the risk of heart diseases.
Conception of a chat bot solution for administrative and HR requests.
Development of a reinforcement learning framework for future consultants.
Introductory training talks on the topics of data science, mathematics, ML and AI.

Publications

Neural Networks for Extreme Quantile Regression with an Application to Forecasting of Flood Risk

Pasche, O. C. and Engelke, S. (2024). "Neural networks for extreme quantile regression with an application to forecasting of flood risk." Annals of Applied Statistics 18(4), 2818–2839. https://doi.org/10.1214/24-AOAS1907

Granger Causality in Extremes

Bodik J., Pasche, O. C. (2024). "Granger Causality in Extremes." ArXiv:2407.09632. https://doi.org/10.48550/arXiv.2407.09632

Modeling Extreme Events: Univariate and Multivariate Data-Driven Approaches

Buriticá, G., Hentschel, M., Pasche, O. C., Röttger, F. and Zhang, Z. (2024). "Modeling extreme events: Univariate and multivariate data-driven approaches." Extremes. https://doi.org/10.1007/s10687-024-00499-9

Validating Deep-Learning Weather Forecast Models on Recent High-Impact Extreme Events

Pasche, O. C., Wider, J., Zhang, Z., Zscheischler, J. and Engelke, S. (2024). "Validating Deep-Learning Weather Forecast Models on Recent High-Impact Extreme Events." Artificial Intelligence for the Earth Systems. http://doi.org/10.1175/AIES-D-24-0033.1

The Effect of a Short Observational Record on the Statistics of Temperature Extremes

Zeder, J., Sippel, S., Pasche, O. C., Engelke, S., and Fischer, E. M. (2023). "The effect of a short observational record on the statistics of temperature extremes." Geophysical Research Letters 50(16), e2023GL104090. https://doi.org/10.1029/2023GL104090

Causal Modelling of Heavy-Tailed Variables and Confounders with Application to River Flow

Pasche, O. C., Chavez-Demoulin, V. and Davison, A. C. (2023). "Causal modelling of heavy-tailed variables and confounders with application to river flow." Extremes 26(3), 573–594. https://doi.org/10.1007/s10687-022-00456-4