Teaching

Lectures

The courses at LUT and DTU were based on the following slides (with different topic variations), with derivations and examples done on blackboard. At some point, I should create a GitHub repository with all the Python codes we covered in class.

1. [00 - Introduction]
2. [01 - Intro probability theory]
3. [02a - Monte Carlo methods]
4. [02b - Variance reduction methods]
5. [03 - Random fields, KL expansion and neural networks]
6. [04 - Bayesian inverse problems]
7. [05a - Prior models and intro model selection]
8. [05b - MCMC and approximation methods]

LUT University, School of Engineering Sciences

• January-February 2024: Special Course on Inverse Problems.
• January-February 2023: Special Course on Inverse Problems.

Technical University of Denmark, Department of Applied Mathematics and Computer Science

• June 2024: Introduction to Bayesian Inverse Problems.
• January 2022: Introduction to Uncertainty Quantification for Inverse Problems.

National University of Colombia at Manizales, Department of Civil Engineering

• I - 2016: Structural Reliability (In Spanish).
• I - 2016: Numerical Methods Applied to Civil Engineering (In Spanish).

Guest-lectures/Tutorials/Seminars

LUT University, School of Engineering Sciences

• Jan. and Nov. 2025: Guest Lectures for the Special Course on Inverse Problems and the course on Computational Science and Artificial Intelligence — Working life.

Technical University of Denmark, Department of Applied Mathematics and Computer Science

• Sept. 2021: Guest Lecture for the course on Introduction to Inverse Problems. Ph.D. Course.
• 2020 and 2021: Organizer of the Reading Course and Journal Club on Sampling Methods for Bayesian Inverse Problems. Ph.D. Course.

Technical University of Munich, Department of Civil and Environmental Engineering

• Mar.-Aug. 2019: Organizer of the Reading Group on Stochastic Methods in Engineering.
• Summer term 2018: Tutorials in Advanced Stochastic Finite Element Methods.
• Winter 2016/17 and 2017/18: Tutorials in Structural Reliability.

Others

• July 2022: Guest lectures for the Summer School on Recent Advancements in Computational and Learning Methods for Inverse Problems. For Ph.D. Students.

Supervised students

Ph.D.’s theses (unnofficial co-supervisor)

• Since 2025: Arttu Häkkinen: Physics-informed machine learning for indoor temperature modeling and control @Danfoss+LUT.
• 2024: Angelina Senchukova: Flexible priors for rough feature reconstruction in Bayesian inversion @LUT.
• 2024: Rafael Floch: Dimension reduction methods for Bayesian inversion with applications in image reconstruction @DTU.

Master’s theses and study projects

• 2018: Junyi Jiang: Traditional and approximate Bayesian computations with applications to random fields @TU Munich.
• 2018: Alexander von Ramm (study project): Submodel selection in hydrological modeling: a Bayesian approach @TU Munich.
• 2017: Matthias Willer: Subset simulation and moving particles methods for reliability analysis @TU Munich.

Bachelor’s theses

• 2019: Adam Misik: Data mining based complexity reduction of system state data from electrical power nets @TU Munich.