I am a PhD Research Assistant at the SPRING Lab at EPFL (Switzerland) supervised by Carmela Troncoso.

My research focuses primarily on the privacy aspects of large-scale data processing systems centred around questions such as: How to evaluate the privacy properties of opaque data processing systems? What are the privacy limits of machine learning-based applications? What learning tasks are solvable under good privacy and good utility simultaneously?

Before joining the SPRING Lab, I previously worked as a privacy researcher for Privitar a London-based scale-up. Privitar develops enterprise software that implements privacy-enhancing technologies and aims to makes these technologies available to organisations at scale.

I hold a Master’s degree in Neural Information Processing (Biomathematics) from the University of Tübingen (Germany). During my undergraduate and graduate studies I conducted research in Applied Machine Learning under, amongst others, Philipp Berens and Mathias Bethge.

Education

  • PhD in Engineering, Ècole Polytechnique Fédéral de Lausanne, Switzerland, (2019 - Today)
  • Master of Science, University Tübingen, Germany (2014-2016)
  • Bachelor of Science, University Erlangen, Germany (2011-2014)

Work Experience

  • Research Scientist, Privitar, London, United Kingdom (2016-2019)

Academic Service & Invited Reviews

  • Program Commitee Member PETS (2023, 2021, 2020 & 2019)
  • Invited Reviewer NeurIPS Workshop on Privacy in Machine Learning (PriML 2021)
  • Invited Reviewer ICLR Workshop on Synthetic Data Generation: Quality, Privacy, Bias (2021)
  • External Reviewer CCS (2019)
  • Invited Reviewer “Rethinking data and balancing digital power” by the Ada Lovelace Institute (2022)

Awards & Grants

  • Graduate Grant, Studienstiftung des Deutschen Volkes (2011-2016)
  • “Looking beyond the EU data strategy: Where next for data use and regulation?”, Panel discussion at CPDP (2023)
  • “Synthetic Data as a Privach Mechanism - A cautionary tale”, Invited lecture in the “Health Sciences and Technology” program at MIT (2022)
  • “Synthetic Data - A Privacy Mirage”, Tech Talk at the Brussels Privacy Hub (2021)
  • “Why are Organisations Slow to Adopt PETs? Differential Privacy as a Case Study”, EPFL IC Summer Research Institute (2018)

Patents

Computer-implemented privacy engineering systens and method. McFall J.D., Cabot, C.C., …, Stadler, T. et al., US Patent Application, Singapore Granted Patent (2017)

Method or system for querying a sensitive dataset. Cabot, C.C., Guinamard, K.F.P, … Stadler, T. et al., US Patent Application (2018)

Publications

Why the search for a privacy-preserving data sharing mechanism is failing. Stadler, T., & Troncoso, C., Nature Computational Science (2022)

Synthetic data – Anonymisation Groundhog Day. Stadler, T., Oprisanu, B., & Troncoso, C., USENIX Security (2022)

Deploying decentralized, privacy-preserving proximity tracing. Troncoso, C., Bogdanov, D., Bugnion, E., … Stadler, T. et al., Communications of the ACM (2022)

Preliminary Analysis of Potential Harms in the Luca Tracing System. Stadler, T., Lueks, W., Kohls, K., Troncoso, C., arXiv preprint (2021)

Decentralized privacy-preserving proximity tracing. Troncoso, C., Payer, M., Hubaux, J.P., Salathé, M., Larus, J., Bugnion, E., Lueks, W., Stadler, T. et al., arXiv preprint (2020)

Early evidence of effectiveness of digital contact tracing for SARS-CoV-2 in Switzerland. Salathé, M., Althaus, C., Anderegg, N., … Stadler, T. et al., Swiss Medical Weekly (2020)

A research agenda for digital proximity tracing apps., Von Wyl, V., Bonhoeffer, S., Bugnion, E., Puhan, M. A., Salathé, M., Stadler, T. et al., Swiss Medical Weekly. (2020)

Erythromelalgia Mutation Q875E Stabilizes the Activated State of Sodium Channel Nav1.7., Stadler, T., O’Reilly, A. O., & Lampert, A., Journal of Biological Chemistry (2015)