CV

Work experience

• June 2023 - : Postdoc Researcher
  • University of Copenhagen, Computer Science Department
  • Project: Efficient Machine Learning, funded by SustainML and enRichMyData
  • Collaborator: Raghavendra Selvan
• April 2019 - September 2019: Research Intern
  • IRT Saint Exupéry, DEEL Group
  • Project: Polynomial Optimization for Robustness Certification
  • Supervisors: Edouard Pauwels, Victor Magron, Jean-Bernard Lasserre

Education

• 2019 - 2022: PhD in Applied Mathematics and Computer Science,
  • Université Paul Sabatier, France
  • Thesis: Robustness Verification of Neural Networks using Polynomial Optimization [link, slides]
  • Supervisors: Edouard Pauwels, Victor Magron, Jean-Bernard Lasserre
• 2017 - 2019: Master in Statistics and Machine Learning,
  • Université Paris-Saclay, France
  • Thesis: Polynomial Optimization Methods for Robustness Certification Problems in Deep Neural Networks [link]
  • Supervisors: Edouard Pauwels, Victor Magron, Jean-Bernard Lasserre
• 2013 - 2017: Bachelor in Mathematics
  • Wuhan University, China
  • Thesis: Sur les Extensions Galoisiennes de Degré Infini [link]
  • Supervisor: Jean-Robert Belliard

Skills

• Mathematical Programming
• Machine Learning
• Python & Julia & Matlab

Activities & Events

• Conferences

  • SaTML, Copenhagen, Denmark, 09/04-11/04/2025
  • D3A 2.0, Nyborg, Denmark, 22/10-23/10/2024
  • Conference on “Generative AI in Life Science”, Copenhagen, Denmark, 15/04-16/04/2024
  • D3A 1.0, Nyborg, Denmark, 01/02-02/02/2024
  • Nordic AI Meet, Copenhagen, Denmark, 02/10-03/10/2023
  • 35th NeurIPS, online, 06/12-14/12/2021 [poster]
  • 34th NeurIPS, online, 06/12-12/12/2020 [poster]

• Workshops & Seminars

  • Workshop on “AI and Topology”, Holte, Denmark, 08/04-10/04/2024
  • Workshop on “Verifiable and Robust AI”, Sønderborg, Denmark, 06/11-10/11/2023
  • ELLIS RobustML Workshop, Helsinki, Finland, 23/09-24/09/2023
  • ANITI Jury Audition, Toulouse, France, 25/03/2022 [poster]
  • POEMA Workshop 4, online, 07/02-11/02/2022
  • POEMA Industrial Workshop, online, 13/12-17/12/2021
  • POEMA Workshop 3, online, 29/01-17/02/2021
  • Workshop on “Machine Learning in Certified Systems”, online, from 14/01-15/01/2021 [poster]
  • POEMA Workshop 2, online, 10/10-11/12/2020
  • POEMA Online Learning Weeks, online, 27/05-16/09/2020
  • POEMA 1st Workshop, Florence, Italy, 15/01-17/01/2020
  • Workshop on “Computational Aspects of Geometry”, Toulouse, France, 06/11-08/11/2019

• Seminars

  • ML Section Talk, hybrid, 24/03/2025 [slides]
  • ML Section Talk, hybrid, 19/02/2024 [slides]
  • ML Section Talk, hybrid, 13/12/2023 [slides]
  • SustainML Reading Group, hybrid, 21/06/2023 [slides]
  • BrainPOP Seminar, online, 08/12/2021 [slides]
  • Reading Group led by Monique Laurent, online, 11/08/2021 [slides]
  • BrainPOP Seminar, online, 11/09/2020 [slides]

• Summer Schools

  • Summer school on “Biomedical Image Analysis: from Acquisition to Fairness and Bias”, Svendborg, Denmark, 12/08–16/08/2023
  • Summer school on “Missing Data, Augmentation and Generative models”, Kobæk, Denmark, 14/08–18/08/2023
  • Summer School on “Moments and Positive Polynomials and their Applications”, Toulouse, France, 27/06-30/06/2022

Supervision

• Master students

  • Valdemar Jacobi Henriksen (co-supervised with Raghavendra Selvan), Spring 2025. Thesis: Fair and Parameter-Efficient Finetuning in Medical Image Analysis.
  • Laust Skov Hansen (co-supervised with Raghavendra Selvan), Spring 2025. Thesis: Group Influence Approximations for Model Agnostic Coreset Selection.
  • Philipp Hinz, Spring 2024. Thesis: Development and Implementation of a Reinforcement Learning Agent for the Web-based Game Polyburn.
  • Aske Rory Ching, Niklas Joost Borge (co-supervised with Raghavendra Selvan), Spring 2024. Thesis: Towards adversarially robust dataset compression.

• Bachelor students

  • Zeyu Deng, Spring 2025. Thesis: Can Uncertainty be Used for Coreset Selection?
  • Hallgrimur Thorsteinsson, Valdemar Jacobi Henriksen (co-supervised with Raghavendra Selvan), Autumn 2023. Thesis: Adversarial Fine-tuning of Compressed Neural Networks for Joint Improvement of Robustness and Efficiency.

Teaching Experience

• Machine Learning for Science (MLS), co-lecturer with Raghavendra Selvan, Spring 2025.

Funding

• EU Horizon 2024 Grant to DATAPACT: Compliance by Design of Data/AI Operations and Pipelines, co-applicant with Raghavendra Selvan, Autumn 2024.

Review Activities

MICCAI 2024, IMA 2024, ICLR 2025, ICML 2025, NeurIPS 2024/2025, AISTATS 2025, Array 2025.