I’m a PhD Student in Machine Learning at EPFL in the MLO group, where I am supervised by Prof. Martin Jaggi.

Prior to that, I completed my Master and Bachelor in Computer Science at ETH Zürich working with the LAS group and Prof. Andreas Krause. I also was a visiting Student Researcher at the Apple MLR team in Paris, supervised by Prof. Marco Cuturi. My Bachelor thesis was under the supervision of Prof. Martin Vechev on robustness of neural networks. See a list of my publications so far here.

During my studies, I had the chance to spend two semesters abroad: one at École Polytechnique in France, and one at the University of Toronto in Canada. Moreover, I was able to do an internship at Spacemaker AI for which I gave an interview here.

Feel free to get in touch via mail:
alexander . hagele [AT] epfl.ch


  • October 2023: Starting my PhD at EPFL :)
  • April 2023: I’ve presented BaCaDI as an oral presentation at AISTATS 2023 (notable paper award, 32 / 1689 submissions) in Valencia (link to the conference). Slides are available here.
  • April 2023: I’m in Paris working with the Apple MLR team for the next months – please reach out if you’re around!
  • January 2023: For a course project on data visualization, I created a blog post on Visualizing folktables, a benchmark dataset for fairness in ML. The goal of the post is to provide an overview and exploration of the dataset, hopefully being of value for researchers working with folktables.
  • January 2023: Our paper on BaCaDI: Bayesian Causal Discovery with Unknown Interventions was accepted to AISTATS 2023!
  • September 2022: I’m in Paris at École Polytechnique (well, Palaiseau) for the next few months for a semester abroad before the end of my Master. Please send me a message if you’re around and want to chat! :)
  • July 2022: Presenting our work on Bayesian Causal Discovery to the Causality Discussion Group.
  • July 2022: BaCaDI was accepted to the CRL Workshop at UAI. See you August 5 in Eindhoven!
  • June 2022: Starting my internship at EPFL! Feel free to reach out if you’re around :)
  • June 2022: Our preprint for BaCaDI: Bayesian Causal Discovery with Unknown Interventions is out.