Welcome!

The Trustworthy Information Systems Lab (TISL) tackles questions related to the design and deployment of trustworthy information and communication systems. Our research areas of interest are computer security, data privacy, optimization, and machine learning. Current research investigations of the group include data privacy and several aspects of trustworthy machine learning, such as fairness, privacy-preserving machine learning, and explainability.

๐Ÿ“ฃ News

  • Jun 1, 2026 - ๐Ÿ“„ Our paper "Where to Intervene? Benchmarking Fairness-Aware Learning on Differentially Private Synthetic Tabular Data" has been accepted at PETS 2026 and can be accessed at !
  • Apr 1, 2026 - ๐ŸŽ‰๐Ÿ“„ Our paper "Beyond Epsilon: A Principled QIF Framework for Local Differential Privacy" has been accepted at CSF 2026! Preprint coming out soon.
  • Apr 1, 2026 - ๐Ÿ” Hรฉber will be serving in the PC of the 27th Privacy Enhancing Technologies Symposium (PETS 2027).
  • Apr 1, 2026 - ๐ŸŽ‰๐Ÿ“„ Our paper "Quantifying the Privacy of Counterfactuals by Leveraging Membership Inference Attacks against Synthetic Data" has been accepted at FAccT 2026! Preprint coming out soon.
  • Mar 1, 2026 - ๐Ÿ” Hรฉber will be serving in the PC of the 53rd International Conference on Very Large Data Bases (VLDB 2027).
  • Mar 1, 2026 - ๐ŸŽ‰๐Ÿ“„ Our paper "How Tough Is Location Anonymization? Re-identifying 100K Real-User Trajectories in Japan" has been accepted at AsiaCCS 2026 and can be accessed at !
  • Feb 1, 2026 - ๐ŸŽ‰๐Ÿ“„ Our paper "Revisiting Locally Differentially Private Protocols: Towards Better Trade-offs in Privacy, Utility, and Attack Resistance" has been accepted at ICDE 2026 and can be accessed at !
  • Feb 1, 2026 - ๐ŸŽ‰๐Ÿ“„ Our paper "Understanding Disclosure Risk in Differential Privacy with Applications to Noise Calibration and Auditing" has been accepted at VLDB 2026 and can be accessed at !
  • Oct 6, 2025 - Ulrich Aรฏvodji is invited to serve as an examiner on Jade Garcia Bourrรฉe's PhD thesis committee
  • Jul 17, 2025 - ๐ŸŽ‰๐Ÿ“„ Our paper Taming the Triangle: On the Interplays between Fairness, Interpretability and Privacy in Machine Learning has been accepted for publication in Computational Intelligence
  • Jul 4, 2025 - ๐Ÿš€๐ŸŽ‰ Our Workshop on Algorithmic Collective Action has been accepted to NeurIPS 2025! โœจ
  • Jun 9, 2025 - ๐ŸŽ‰๐Ÿ“„ Our paper Towards Fair In-Context Learning with Tabular Foundation Models has been accepted to ICML 2025 Workshop on Foundation Models for Structured Data
  • May 1, 2025 - Ulrich Aรฏvodji is serving as Area Chair for NeurIPS 2025 - Position Paper Track
  • Nov 29, 2024 - Ulrich Aรฏvodji received an unrestricted gift fom Google to support work on the evaluation of advanced AI assistantsโ€™ ability to protect personal information
  • Nov 20, 2024 - Ulrich Aรฏvodji is serving as Area Chair for ACM FAccT 2025
  • Apr 30, 2024 - ๐Ÿ‘๐ŸŽ‰ Ulrich Aรฏvodji is a recipient of the FRQNT Research Support for New Academics Award!
  • Nov 20, 2023 - Ulrich Aรฏvodji is serving as Area Chair for ACM FAccT 2024
  • Oct 23, 2023 - ๐ŸŽ‰๐Ÿ‘ Patrik Joslin Kenfack, Meghana Bhange, Maryam Babaei, Ivaxi Sheth, and Dave Mbiazi won the ๐Ÿ† Kaggle AI Ethics competition with their essay "Exploring the Landscape of AI Ethics" ๐Ÿ“–โœจ
  • Oct 13, 2023 - ๐Ÿšจ๐Ÿ“ฐ Our work on Fairwashing is highlighted on Philosophy Tube
  • Apr 5, 2023 - ๐Ÿ‘๐ŸŽ‰ Patrik Joslin Kenfack is a recipient of Mila's Excellence Scholarships โ€“ EDI in Research
  • Dec 14, 2022 - Ulrich Aรฏvodji is serving as Area Chair for ACM FAccT 2023
  • Nov 11, 2022 - ๐Ÿ‘๐ŸŽ‰ Huge congratulations to Maryam Babaei and Meghana Bhange for winning the Top Female Team at the UN PET Lab's Hackathon! ๐Ÿ†๐Ÿ’ป๐ŸŒ๐Ÿ’ชโœจ
  • Sep 26, 2022 - Maryam Babaei joined our group as PhD student. Welcome Maryam!
  • Sep 1, 2021 - ๐ŸŽ‰ Ulrich Aรฏvodji joined ร‰TS Montreal's Departement of Software and Information Technology Engineering as an Assistant Professor! ๐Ÿš€๐Ÿ“š
  • Jan 19, 2021 - ๐Ÿšจ๐Ÿ“ฐ Our work on Fairwashing is highlighted in IEEE Spectrum
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๐Ÿ“œ Code of Conduct

TISL is committed to ensuring an enjoyable, positive, and respectful experience for all. We anticipate the collaboration of every member to maintain a secure environment for everyone. For a more detailed version, please refer to the Code of Conduct page.