Javier Abad

PhD student at AI Center
(joint with Julia Vogt and Bjoern Menze)

I am a Doctoral Student in Machine Learning and a Fellow at the ETH AI Center in Zurich, advised by Prof. Fanny Yang, Prof. Julia Vogt, and Prof. Bjoern Menze. Through my research, I aim to develop ML models that can be trusted and applied in high-stakes decision-making, with an emphasis on medical applications. I am particularly excited about questions related to causal reasoning, representation learning, interpretability, and privacy in machine learning.

Previously, I led a project on conformal prediction under the guidance of Adrian Weller MBE, and researched interpretability methods for causal inference with Prof. Mihaela van der Schaar, both at the University of Cambridge. I was also a Research Scientist at Featurespace, researching and implementing ML models to fight financial crime.

Recent papers

  1. Hidden yet quantifiable: A lower bound for confounding strength using randomized trials
    Piersilvio De Bartolomeis*, Javier Abad*, Konstantin Donhauser, and Fanny Yang
    International Conference on Artificial Intelligence and Statistics (AISTATS), 2024


  1. Privacy-preserving data release leveraging optimal transport and particle gradient descent
    Konstantin Donhauser*, Javier Abad*, Neha Hulkund, and Fanny Yang
    arXiv preprint, 2024


The easiest way to reach me is by emailing javier.abadmartinez@ai.ethz.ch. I am also looking forward to supervising motivated students in my fields of expertise. If you are interested, feel free to drop me a line.

You can also find me on LinkedIn, Google Scholar and X.