Can Pouliquen

Quick bio

Hello, I am a final year PhD student in machine learning at the Ecole Normale Supérieure de Lyon under the supervision of Mathurin Massias, Titouan Vayer and Paulo Gonçalves in OCKHAM, a joint team of Inria¹ and CNRS², led by Rémi Gribonval. My research lies at the intersection of optimization, sparsity, statistics and deep learning. I also have a deep interest in the Python programming language and its applications.

Previously, I was a research intern in deep learning in NeuroAI, a CNRS lab in Toulouse³, under the supervision of Andrea Alamia and Rufin VanRullen where I worked on the oscillatory dynamics of neural networks with predictive coding feedbacks.

Before that, we may have met in Berlin, Montpellier, Lyon, Istanbul, Beyrouth, Amman or Cairo. Hit me up if that's the case !

Outside of research, I enjoy long-distance mountain running, athletic strength sports and hiking.

Research

I work on machine learning methods for structure discovery (i.e. learning to unveil hidden relationships in data). Currently, I am trying to bridge the gap between optimization-based approaches and deep learning-based approaches to do so.

Here is a list of links to my publications:

• Schur's Positive-Definite Network: Deep Learning in the SPD cone with structure.
  Can Pouliquen, Mathurin Massias, Titouan Vayer
  ICLR, 2025.


• Implicit differentiation for hyperparameter tuning the weighted Graphical Lasso.
  Can Pouliquen, Paulo Gonçalves, Mathurin Massias, Titouan Vayer
  GRETSI, 2023.


• Can sparsity improve the privacy of neural networks ?
  Antoine Gonon, Léon Zheng, Clément Lalanne, Quoc-Tung Le, Guillaume Lauga, Can Pouliquen
  GRETSI, 2023.

Teaching

ENS de Lyon: Convex optimization M1 (2023-2024)
TA of Mathurin Massias, 12h

CPE Lyon: Optimization algorithms M1 (2023-2024)
TA of Marion Foare, 20h

Ecole Centrale de Lyon: Probability theory L3 (2023-2024)
TA of Céline Helbert and Christophette Blanchet, 32h

Talks

• Deep Learning in the SPD cone with structure.
  Several poster and oral presentations.


• Machine Learning, optimization, and interactions.
  Scientific outreach for an audience of non-experts, June 2024.


• Implicit differentiation for hyperparameter tuning the weighted Graphical Lasso.
  GRETSI oral, August 2023.


• Neuroscience inspired machine learning.
  OCKHAM seminar, June 2023.


• Estimating dynamic functional connectivity graphs.
  LIP PhD seminar, May 2023.

Contact

To get in touch:

• Email: can.pouliquen \AT\ ens-lyon \DOT\ fr
• Office: ENS Lyon, site Monod, 46 allée d'Italie, 69007, Lyon, France