Education

  • 2012-2017, PhD Candidate at the Gatsby Computational neuroscience Unit. Supervisor: Maneesh Sahani

  • 2009-2011, Master’s Degree in Cognitive Science (Cogmaster), at ENS-EHESS-PARISV, France. Master’s thesis: Study of the implication of cognitive control in adaptation to variable environments (with Etienne Koechlin)

  • 2006-2009, Engineering Diploma at Ecole Centrale de Nantes (France), major: Signal processing.




Talks

  • Gaussian Variational Inference for Diffusion Processes Revisited, 18th of November 2022, Seminar, Gatsby Unit, London.

  • Dual parameterization of sparse variational Gaussian processes, 11th of April 2022, Gaussian Process Seminar Series, virtual. [link]

  • Optimization methods for variational Gaussian processes, 4th of November 2021, Guest lecture, Imperial College, London.

  • Dual parameterization of sparse variational Gaussian processes, 20th of October 2021, G-Research, London.

  • Overview of Secondmind’s opensource toolboxes, 16th of September 2021, Gaussian Process and Uncertainty Quantification Summer School (virtual) [link]

  • Sparse methods for Markovian Gaussian processes, 14th of January 2021, Secondmind.ai Seminars (virtual) [link].

  • Gaussian Processes for time-series, 19th of November 2020, Cambridge Gaussian Process meetup (virtual) [slides].

  • Doubly Sparse Variational Gaussian Processes, 7th of February 2020, invited talk at the Bayes Centre, University of Edinburgh.

  • Doubly Sparse Variational Gaussian Processes, 31st of October 2019, invited talk at the UCL center for Artificial intelligence.

  • Sparse Variational Gaussian Processes, 9th International Congress on Industrial and Applied Mathematics, Valencia, Spain, 17th of July 2019. Symposium on New trends in the developpment of Gaussian process for representing and learning data.

  • Banded Matrix Operators for Gauss-Markov Models in the Automatic Differentiation Era, 20th of November 2018, invited talk at the Max Planck Institute for Intelligent Systems.

  • Scalable transformed additive signal decomposition by non-conjugate gaussian process inference., 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP).

  • Sparse Variational Gaussian Processes for Non-Conjugate Latent Factor Models., Janelia Junior Scientist Workshop on Machine Learning and Computer Vision 2017.

Teaching

  • RLSS 2022: Reinforcement Learning Summer School, 11th to 15th of July 2022. Teaching assistant.

  • BAMB! 2022: Barcelona Summer School for Advanced Modeling of Behavior, 1rst to 8th of September 2022. Teaching assistant. Topics: Introduction to Neural Networks with Jax.

  • Aalto University, Special Course in ML, Data Science and AI: Gaussian processes - theory and applications, 11.01.2021-19.02.2021 [link]. Co-lecturer: variational inference.

  • BAMB! 2019: Barcelona Summer School for Advanced Modeling of Behavior, 4th to 10th of September 2019. Teaching assistant. Topics: Introduction to Tensorflow, Model comparison.

  • UCL Graduate School, Dimensionality reduction, Instructor, 2013-2015.

  • UCL Graduate School, Introduction to scientific programming in Python, Instructor, 2014.

  • Gatsby Unit, UCL, Theoretical Neuroscience, Teaching Assistant, 2013.

  • Gatsby Unit, UCL, Probabilistic and Unsupervised Learning, Teaching Assistant, 2013.

  • Gatsby Unit, UCL, Approximate Inference and Learning in Probabilistic Models, Teaching Assistant, 2013.



Projects and other activities

I participated in the development of the scientific application Daydreaming, with financial help from the city of Paris through Science en poche. I started a scientific blog that I plan to revive soon. With the Gatsby Unit we participated in a Kaggle hosted seizure detection challenge and ranked 9/200 and we presented our work at a seizure focused conference. I’ve been involved in consultancy work with the company Swhere applying machine learning techniques to solve business problems.