About Me

I’m a physicist/engineer converted into a computational statistician. I love statistical data analysis, programming and data visualization. I am currently a doctoral researcher at Helsinki University working on AI assisted Bayesian workflow and contributing to open source. I am a core contributor of ArviZ a project for exploratory analysis of Bayesian models. In addition to probabilistic modeling, I also enjoy teaching and technical writing.

I think that the culture in scientific research needs deep changes towards a more collaborative, open and diverse model. I am interested in open science, reproducible research and science communication. I want to pursue a career in probabilistic modeling and statistical research with special emphasis on openness and reproducibility.

In my spare time, I like playing board games and going to the beach to do water activities. I have been sailing and snorkeling regularly since I was little and more recently I added kayaking to the mix too! I generally spend the summer at the Costa Brava. Here I leave you a sneak peak of the views when nobody is around


Open source libraries

Here are highlighted some open source projects I contribute to, check out my GitHub profile for a complete list of the projects I contribute to.

  • ArviZ: Exploratory analysis of Bayesian models in Python or Julia
  • xarray-einstats: Label based statistics, linear algebra and einops for xarray objects.
  • PyMC: Friendly probabilistic programming in Python.
    • In addition to helping a bit with PyMC development, I am one of the main curator/reviewers of PyMC-examples, a collection of Jupyter notebooks about Bayesian modeling with PyMC.
  • mombf: Bayesian model selection and averaging for regression and mixtures for non-local and local priors.
  • exosherlock: Smooth your interactions with the NASA Exoplanet Archive using Python and pandas.

Projects and initiatives

  • CZI EOSS 4 grant: Bayesian Open Source Software for Biomedicine: Stan, ArviZ and PyMC
  • PyMC-Data Umbrella sprint: A series of webinars and sprint event to encourage and help people contribute to PyMC.
  • Season of Docs at ArviZ: GSoD is an initiative to help open source projects get in contact and hire technical writers.
  • PyMCon 2020: PyMCon 2020 is an asynchronous-first virtual conference for the Bayesian community


  • Mikkola, Petrus, et al. “Prior knowledge elicitation: The past, present, and future.” arXiv preprint arXiv:2112.01380 (2021).
  • Rossell, David, Oriol Abril, and Anirban Bhattacharya. “Approximate Laplace approximations for scalable model selection.” Journal of the Royal Statistical Society: Series B (Statistical Methodology) 83.4 (2021): 853-879.
  • Badenas-Agusti, Mariona, et al. “HD 191939: Three Sub-Neptunes Transiting a Sun-like Star Only 54 pc Away.” The Astronomical Journal 160.3 (2020): 113.
  • Foreman-Mackey, Daniel, et al. “emcee v3: A Python ensemble sampling toolkit for affine-invariant MCMC.” arXiv preprint arXiv:1911.07688 (2019).

Talks and conferences

  • PyMC-Data Umbrella sprint webinar: Contributing to PyMC documentation video and presentation material
  • Data Umbrella webinar: Bayesian modeling with PyMC3 video recording, slides
  • PROBPROG 2020: Backend agnostic exploratory analysis of Bayesian models. Poster presentation. All the materials are available on GitHub
  • StanCon 2020: ArviZ, InferenceData, and NetCDF: A unified file format for Bayesians. Slides and video presentation are available at GitHub, the slides are executable thanks to Binder!
    • Slides and video presentations are available in English, Catalan, French and Finnish.