I’m currently working as an ML engineer at Fundamental.

Before that, I was a researcher at KU Leuven, working with Johan Suykens on kernel methods and duality. I have a PhD in Machine Learning from Télécom Paris, supervised by Florence d’Alché-Buc and Zoltan Szabo (slides, manuscript).

Research Interests

• Scalable kernel methods, random features, spectral approximations
• Fairness and constrained optimization in machine learning
• Structured prediction and functional outputs
• Representation learning, dimensionality reduction

News

• 06/26 I just started a new position as ML engineer at Fundamental.
• 06/25 “Accelerating Spectral Clustering under Fairness Constraints” accepted at ICML (paper).
• 03/25 I just started a new position as data engineer at Dataminded.
• 06/24 “Learning in Feature Spaces via Coupled Covariances: Asymmetric Kernel SVD and Nyström method” accepted at ICML (paper).
• 03/24 I was part of the organizing team of the DEEPK workshop on deep learning and kernel methods at Leuven.
• 10/23 Talk at the MIND/SODA team seminar: “Robustness and sparsity through Moreau envelopes in kernel-based settings” (slides).
• 06/23 “Extending Kernel PCA through Dualization: Sparsity, Robustness, and Fast Algorithms” accepted at ICML (paper).
• 06/22 “Functional Output Regression with Infimal Convolution” accepted at ICML (paper).