About me

I am a PhD candidate in Machine Learning at Mila, Université de Montréal advised by Yoshua Bengio. My research focuses on developing biologically grounded machine learning approaches to model the response of cells to biological perturbations, with the goal of providing insights and recommendations to experimentalists.

Currently, I am working on structured models of cell transcriptional dynamics at genome scale, aiming to uncover genetic interactions from single-cell transcriptomic data. This work was partially carried out during my visit to the Theis Lab at Helmholtz Munich. Earlier in my PhD, I led the development of a lab-in-the-loop approach for guiding drug combination screening assays, which was prospectively validated through a collaboration with Relation Therapeutics and Scripps Research. I also investigated the applicability of causal inference methods to transcriptomic data.

I hold a Master’s degree in Machine Learning from École Normale Supérieure Paris-Saclay and I graduated from École polytechnique in Paris. More details can be found in my résumé.

I grew up in Paris and Bergerac before moving to Montréal. Outside the lab, I enjoy improvision theatre, climbing (top rope, lead and bouldering), and discovering hidden corners of Montréal.

Publications

• A scalable gene network model of regulatory dynamics in single cells
  P Bertin, JD Viviano, A Tejada-Lapuerta, …, FJ Theis, Y Bengio
  (preprint) 2025 [pdf]

• Causal machine learning for single-cell genomics
  P Bertin^*, A Tejada-Lapuerta^*, S Bauer, H Aliee, Y Bengio, FJ Theis
  (Nature Genetics) 2025 [pdf]

• RECOVER identifies synergistic drug combinations in vitro through sequential model optimization
  P Bertin, J Rector-Brooks, D Sharma, …, JP Taylor-King, Y Bengio
  (Cell Reports Methods) 2023 [pdf]

• DEUP: Direct Epistemic Uncertainty Prediction
  S Lahlou, M Jain, H Nekoei, VI Butoi, P Bertin, …, Y Bengio
  (TMLR) 2023 [pdf]

• TorchXRayVision: A library of chest X-ray datasets and models
  JP Cohen, JD Viviano, P Bertin, …, H Bertrand
  (PMLR) 2022 [pdf]

• Mapping the fine-scale organization and plasticity of the brain vasculature
  C Kirst, S Skriabine, A Vieites-Prado, T Topilko, P Bertin, …, N Renier
  (Cell) 2020 [pdf]

• Analysis of gene interaction graphs for biasing machine learning models
  P Bertin, M Hashir, M Weiss, G Boucher, V Frappier, JP Cohen
  (MLCB) 2019 [pdf]

Teaching

• AI4Genomics Bootcamp instructor
  Instructor for two consecutive years (2020 and 2021) in the AI for Genomics Bootcamp supported by Mila and IVADO.
  • Designed and delivered a 3-hour lecture: Challenges of Machine Learning for Transcriptomics [slides]
  • Supervised and evaluated student projects throughout the 12-week program.

Besides Research

• Entrepreneurs Cohort
  Mila Entrepreneurship Lab
  I completed the Winter 2025 Cohort program, which included training workshops and a final pitch presentation in front of entrepreneurs and investors.

• Science Reviewer
  MUHC Research Ethics Board
  I review on a monthly basis research proposals related to artificial intelligence for the Research Ethics Board of the McGill University Health Center (MUHC).

Master’s Thesis

• Analysis of 3D microscopic brain images at high resolution [pdf]
  Aramis Team, Inria, Paris. Supervised by Stanley Durrleman and Nicolas Renier.
  Tackled the analysis of graphs embedded into 3D space in order to study the variability and plasticity of vessel networks in the adult mouse brain.