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 for 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. Privacy-preserving data release leveraging optimal transport and particle gradient descent
    Konstantin Donhauser*, Javier Abad*, Neha Hulkund, and Fanny Yang
    International Conference on Machine Learning (ICML), 2024
  2. Detecting critical treatment effect bias in small subgroups
    Piersilvio De Bartolomeis, Javier Abad, Konstantin Donhauser, and Fanny Yang
    Conference on Uncertainty in Artificial Intelligence (UAI), 2024
  3. 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

Preprints

  1. Strong Copyright Protection for Language Models via Adaptive Model Fusion
    Javier Abad*, Konstantin Donhauser*, Francesco Pinto, and Fanny Yang
    arXiv preprint, 2024

Contact

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.