News

• A work accepted for publication in Journal of Cheminformatics!

• New work on Proxy-informed Bayesian transfer learning with unknown sources, stay tuned for the preprint!

• A work accepted at NeurIPS 2024 Workshop on Bayesian Decision-making and Uncertainty on Computation-Aware Robust Gaussian Processes

• New preprint on Preferential Amortized Black-Box Optimization

• New work on Heteroscedastic Preferential Bayesian Optimization with Kernel Density Based User Modelling, stay tuned for the preprint!

Biography

I am a postdoc at Inria Bordeaux, in the SISTM team. I work on the interplay between data-driven and mechanistic models for Systems Biology in a Bayesian framework, a field I like to call Biologically-Informed Machine Learning.

Before that, I was a postdoc at Aalto University, in the Probabilistic Machine Learning group, working with Samuel Kaski, Vikas Garg and Markus Heinonen. I was working on Bayesian Inference, Bayesian Optimization, and the integration of human feedback into ML algorithms, with an emphasis on Drug Design.

And prior to that, I did my PhD at Inria and Institut Curie, under the supervision of François Fages and Annabelle Ballesta. We developed methods at the boundary of Systems Biology and Machine Learning to infer mechanistic models from time series data. A major contribution of the manuscript was to apply such methods to investigate the impact of systemic regulators (e.g. temperature, nutrient exposure, hormones) on the cellular circadian clock, in an attempt to personalize Cancer Chronotherapies. The manuscript can be found here and the slides here.

Teaching

• 2022-2023: Teaching Assistant • M. Sc. • Aalto University
  • Advanced Probabilistic Methods
• 2019-2021: Teaching Assistant • 2nd year B. Sc. • Université de Paris
  • Real Analysis (42h)
  • Multivariate Functions (18h)
• 2018-2019: Teaching Assistant • 1st year B. Sc. • Université de Paris
  • Mathematics and Calculus (60h)

Reviewing service

I serve as a reviewer for journals and conferences in different domains:

• Machine Learning ( JMLR, PAMI, NeurIPS, ICML, ICLR, AISTATS)
• Computational Biology ( TCBB, CMSB, ECCB, CSBIO).

Miscellanous

For a few years now, I have been cultivating a passion for indoor bouldering, with a mild success thus far 🫠