Students of the SecInt doctoral college work on one of the interdisciplinary projects listed below. Each project is expected to run for four years and is supervised by at least two professors from the corresponding research areas.

  1. Provable Security and Privacy for Human-Centric Systems

    (Matteo Maffei, Ezio Bartocci)
  2. Runtime Assurance of Cyber-Physical Systems with Machine Learning Components

    (Ezio Bartocci, Andreas Kugi)
  3. Quantitative Invariants for Probabilistic Programs

    (Laura Kovacs, Efstathia Bura)
  4. Robust Machine Learning Methods for the Detection of Anomalies in Network Traffic

    (Tanja Zseby, Martina Lindorfer)
  5. Machine Learning for the Detection of Malicious and Privacy-invasive Behavior

    (Martina Lindorfer, Georg Weissenbacher)
  6. Safe, Learning-based Robots

    (Andreas Kugi, Thomas Gärtner)
  7. Trustworthy Machine Learning

    (Thomas Gärtner, Laura Kovacs)
  8. Adaptive Verification for Security Features of Machine Learning-based Systems

    (Semeen Rehman, Matteo Maffei, Tanja Zseby)
  9. Adversarial Attacks and AI Accelerators

    (Georg Weissenbacher, Tanja Zseby)
  10. Statistical Verification of Security Properties for Cyber-Physical Systems

    (Efstathia Bura, Ezio Bartocci, Semeen Rehman)