Teaching

We teach courses for the Master's Programme in Data Science at the University of Helsinki, organize workshops and tutorials on statistical model fitting and amortized probabilistic inference, and provide teaching material such as interactive lecture notes and demos, and a guide to writing Master's theses.

Taught Courses at the University of Helsinki

Ongoing courses (academic year 2026/27)

  • Statistics for Data Science (DATA11007). Period I, Autumn term. Taught yearly from Autumn 2025. The core statistics course of the . . Interactive online lecture notes (). (2025 edition co-taught with Prof. .)
  • Statistics for Data Science, self-study version (DATA11007). Period III-IV, Spring term, online teaching. Taught yearly from Spring 2026. .
  • Probabilistic Cognitive Modelling (DATA20047). Period III, Spring term. Taught yearly from Spring 2023. .
  • Bachelor's thesis supervision group (English-speaking group). Autumn term. Supervised yearly from Autumn 2025. (2025 group instructed by Dr. Nasrulloh Loka; 2026 group co-supervised with Dr. Herilalaina Rakotoarison.)

Past courses

  • Autumn 2021 – Spring 2025: "Computational Statistics" (period I each autumn, with a self-study version in periods III-IV each spring; ; Autumn 2024 edition co-taught with Martin Jørgensen)
  • Autumn 2024, period I: "Seminar on Generative AI" (; co-taught with Nasrulloh Loka and Paul Chang)
  • Spring 2022, period III: "Computational Cognitive Neuroscience" (, ; co-taught with )
  • Spring 2021, period III-IV: “Seminar on Probabilistic Intelligence in Brains and Machines” (, )

Guide to Writing Master's Theses

I wrote a , mostly targeted at (my) students of the of the University of Helsinki—but possibly of broader interest.

Interactive Demos and Visualizations

We like to build visualizations and interactive demos of our methods and research:

  • One cause or two? An animated short film with interactive demos on our study of causal inference in multisensory perception ().
  • nanoACE playground. Our Amortized Conditioning Engine (ACE) performing probabilistic inference live in the browser, on tasks such as Gaussian process regression and active learning ().
  • OptimViz. Several derivative-free optimizers at work on standard test functions ().

International Tutorials and Workshops

We have been regularly giving classes, tutorials, and workshops at summer schools and for research groups at various institutions worldwide, for a duration that ranges from 20 minutes to 4+ hours. Typical topics include optimization (and our toolbox), Bayesian model fitting (and our toolbox), model comparison and, more recently, amortized probabilistic inference. 

Upcoming: a two-hour tutorial on amortized probabilistic machine learning at the (Paris, France, Dec 2026), with Dr. Herilalaina Rakotoarison and Dr. Julia Linhart (New York University).

As representative examples:

  • The module on computational modeling and statistical model fitting taught at the University of Geneva in Spring 2020, as part of the graduate course Trends in Computational Neuroscience. Slides and Python notebook ().
  • Lectures on optimization and Bayesian inference for statistical model fitting given in 2022 at the Barcelona summer school for Advanced Modeling of Behavior (). Slides and MATLAB code ().

Our model-fitting tools, including the Python versions PyBADS and PyVBMC, are collected with documentation and examples at .

List of past workshops:

  1. Joint Ma-Niv-Pillow lab meeting, Princeton University (Princeton, NJ, Sep 2015).
  2. , New York University (New York, NY, May 2016).
  3. , University of Geneva (Geneva, Switzerland, Sep 2016).
  4. , Northwestern University (Chicago, IL, Jun 2017).
  5. , Baylor College of Medicine (Houston, TX – in telepresence, Jul 2017).
  6. , University of Minnesota (Minneapolis, MN, Aug 2017). Code:
  7. NYU Training Program in Computational Neuroscience, New York University (New York, NY, Sep 2017).
  8. , University of Minnesota (Minneapolis, MN, Aug 2018). Code:
  9. Bristol Neuroscience Group, University of Bristol (Bristol, UK, May 2019). Code:
  10. , Columbia University (New York, NY, Sep 2019).
  11. Center for Neural Science, New York University (New York, NY, Sep 2019). Code:
  12. Psychology department, Stockholm University (Stockholm, Sweden, Nov 2019).
  13. Trends in Computational Neuroscience graduate course; Department of Neuroscience, University of Geneva (Geneva, Apr 2020). Website:
  14. IBL Computational Neuroscience course, International Brain Laboratory (Apr 2020).
  15. , Barcelona Biomedical Research Park (Barcelona, Spain, Sep 2022). Code: 
  16. Data Science in Action, University of Padua (Padova, Italy, Dec 2022); invited lecturer.