Patrik Kenfack

Patrik Kenfack is a PhD Candidate at ÉTS Montréal and Mila working on algorithmic fairness, responsible AI and foundation models for structured data.

Hey, I’m Patrik 👋🏿

PhD Candidate in Computer Science · ÉTS Montréal & Mila

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I study bias mitigation in machine learning under constraints on sensitive information — what fairness can mean when you cannot see, or are not allowed to use, the attributes you would need to measure it. I am advised by Ulrich Aïvodji and Samira Ebrahimi Kahou.

I recently completed a Mitacs internship at Layer6 AI, where I worked on designing fair tabular foundation models. Before ÉTS Montréal I was a research assistant at the Machine Learning and Knowledge Representation Lab at Innopolis University, advised by Adil Khan, working on fairness in ensemble learning, GANs, continual learning and representation learning. I hold an MSc in Computer Science and a BSc in Mathematics and CS from the University of Dschang.

Research interests

Algorithmic Fairness Tabular Foundation Models Responsible AI Applied Machine Learning

News

1. Jun2026
   📄 Paper🎤 Talk

   I presented our recent paper Training Fair Tabular Foundation Models at FMSD @ ICML 2026; Accpeted as spotlight oral.

2. May2026
   🛠️ Service

   Reviewing for workshop Foundation Models for Structured Data @ ICML 2026

3. Apr2026
   🏆 Award

   Recipient of the ETS internal scholarship

4. Mar2026
   🛠️ Service

   Reviewing for ACL 2026.

5. Fev2026
   🛠️ Service

   Reviewing for FAcct.

6. Jan2026
   📄 Paper

   Our paper Towards Fair In-Context Learning with Tabular Foundation Models is accepted at TMLR

7. Jan2026
   🛠️ Service

   Reviewing for CVPR

8. Nov2025
   💼 Intership

   Excited to have started a Mitacs internship at Layer6 AI, where I research on designing Fair Tabular Foundation Models.

9. Nov2025
   📄 Paper

   Our paper Adaptive Group Robust Ensemble Knowledge Distillation is accepted at TMLR

10. Jun2025
    📄 Paper

    Our paper Towards Fair In-Context Learning with Tabular Foundation Models is accepted at ICML 2025 FMSD workshop

11. May2025
    📄 Paper

    Our recent paper Towards Fair In-Context Learning with Tabular Foundation Models is out!

12. Mar2025
    🏆 Award

    Secured second place for the 3-Minute Research Presentation Contest at the 2025 ETS student researcher conference

13. Mar2025
    📄 Paper

    Two papers accepted at SCSL @ ICLR 2025. 1. GradTune: Last-layer Fine-tuning for Group Robustness Without Group Annotation. 2. Towards personalized healthcare without harm via bias modulation

14. Feb2025
    🛠️ Service

    Reviewing for FAcct.

15. Oct2024
    📄 Paper

    Our paper Adaptive Group Robust Ensemble Knowledge Distillation was accepted at Neurips2024 AFME workshop.

16. Aug2024
    📄 Paper

    Our paper Fairness Under Demographic Scarce Regime was accepted at TMLR.

17. Aug2024
    🛠️ Service

    Invited to serve as a reviewer at ICLR 2025.

18. Jul2024
    🛠️ Service

    Invited to provide an emergency review at Neurips 2024.

19. Jun2024
    📄 Paper

    Our Survey on Fairness Without Demographics was accepted at TMLR.

20. Apr2024
    🛠️ Service

    Selected to join the Mila EDI committee.

21. Jan2024
    🛠️ Service

    Reviewer for FAcct.

22. Aug2023
    🏆 Award

    Our TISL lab team won a $10,000 prize at the 2023 Kaggle AI Report Competition. Our report, Exploring the Landscape of AI Ethics, secured first place in the AI Ethics category.

23. Mar2023
    🛠️ Service

    Reviewer for ICCV 2023.

24. Apr2023
    🏆 Award

    Awarded the Excellence Scholarships – EDI in Research from Mila, supporting equity, diversity, and inclusion in AI research.

25. Feb2023
    🛠️ Service

    Reviewer for FAccT 2023.

26. Jan2023
    🎤 Talk

    Presented our work Learning Fair Representations Through Uniformly Distributed Sensitive Attributes at SaTML 2023.

Happy to talk about research or potential collaborations — reach me on LinkedIn or at kenfackjoslin@gmail.com.