I am Staff Research Scientist in Machine Learning at DeepMind.

I received a Diploma di Laurea in Mathematics from University of Bologna and a PhD in Machine Learning from École Polytechnique Fédérale de Lausanne (IDIAP Research Institute). I worked in several Machine Learning and Statistics research groups: the Empirical Inference Department at the Max-Planck Institute for Intelligent Systems (Prof. Dr. Bernhard Schölkopf), the Machine Intelligence and Perception Group at Microsoft Research Cambridge (Prof. Christopher Bishop) and the Statistical Laboratory, University of Cambridge (Prof. Philip Dawid).

My research interests are based around Bayesian & causal reasoning, graphical models, variational inference, time-series models, deep learning, and ML fairness and bias.


  • A causal Bayesian networks viewpoint on fairness. S. Chiappa and William S. Isaac. Privacy and Identity Management. Fairness, Accountability, and Transparency in the Age of Big Data. E. Kosta et al. Editors, Springer Nature Switzerland, IFIP AICT 547, pages 3–20, 2019.
  • Degenerate feedback loops in recommender systems. R. Jiang, S. Chiappa, T. Lattimore, A. György, and P. Kohli. ACM Conference on AI, Ethics, and Society, 2019. arXiv:1902.10730
  • Path-specific counterfactual fairness. S. Chiappa. AAAI-2019. [.pdf]
  • Causal reasoning from meta-reinforcement learning. I. Dasgupta, J. Wang, S. Chiappa, J. Mitrovic, P. Ortega, D. Raposo, E. Hughes, P. Battaglia, M. Botvinick, and Z. Kurth-Nelson, 2019. arXiv:1901.08162
  • Comparing interpretable inference models for videos of physical motion. M. Pearce, S. Chiappa, and U. Paquet. AABI 2018.

  • I am co-organizing the ICLR Workshop Safe Machine Learning: Specification, Robustness, and Assurance.
  • I am Program Chair of AISTATS 2020.