My teaching connects probability, statistics, machine learning, and computational practice.
I have designed and delivered internal lecture series for research colleagues, taught exercise and laboratory sessions, contributed to assessment, and supervised academic and industrial student projects. My teaching connects mathematical tools, research, and reproducible computation.
Lecture series designed and taught
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2021
High-Dimensional Probability and Statistics for Data Science
KTH Royal Institute of Technology
Independently developed and delivered an internal series of fourteen two-hour lectures for research colleagues. Topics included concentration inequalities, random vectors, random matrices, chaining, and statistical-learning applications.
14 × 2-hour meetings Original syllabus Research-paper discussions -
2020
Statistical Machine Learning
KTH Royal Institute of Technology
Independently designed and delivered an internal series of eight one-hour lectures on statistical learning, kernel methods, and large-scale and stochastic optimization.
8 × 1-hour meetings Original syllabus Statistics and computation
Course instruction and materials
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2018–2020
EL2820 Modelling of Dynamical Systems
KTH Royal Institute of Technology
Led exercise sessions and laboratory work, graded assignments and examinations, and contributed to course assessment. I co-authored and revised the 2019 exercise booklet and developed a Jupyter-based homework assignment connecting system identification theory with numerical experimentation.
Exercises and laboratories Grading and examinations Course booklet Jupyter homework
Teaching practice
Mathematics and current research
Course meetings connect foundational results with selected papers so that students see how abstract tools are used in active research.
Computation as part of reasoning
Numerical experiments and notebooks are used to test assumptions, visualize behaviour, and complement analytical arguments.
Flexible course delivery
During the 2020 COVID disruption, I recorded my own exercise sessions and provided hybrid support for students working remotely.
Student supervision
Bachelor’s thesis supervision
- 2020 Daniel Hirsch and Tim Steinholtz KTH Royal Institute of Technology
- 2019 Emil Backman and David Petersson KTH Royal Institute of Technology
- 2019 David Mainwaring and Jonathan Österberg KTH Royal Institute of Technology
- 2018 Mikaela Åstrand and Oscar Bergqvist KTH Royal Institute of Technology
Master’s and research supervision
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2023–2024
Archith Athrey — learning-based stochastic control
Co-supervised his master’s research at TU Delft, contributing to problem formulation, theoretical development, methodology, and analysis. The collaboration continued beyond the thesis and led to a joint publication at the 2024 European Control Conference.
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2018–2019
Carl-Johan Larsson and Fredrik Håkansson — industrial master’s thesis
Supervised the thesis User-Based Predictive Caching of Streaming Media in connection with Spotify. I primarily mentored the machine-learning component, including recurrent-neural- network modelling, trajectory prediction, and evaluation using large-scale mobility data.
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2025–2026
Alexandre Alouadi, PhD candidate
Research mentor and collaborator on two projects forming part of his doctoral research. The collaboration included problem formulation, theoretical development, methodology, experimental design, and preparation of joint manuscripts.