Julien Martinelli

Julien Martinelli

Postdoctoral researcher in Machine Learning

Aalto University - Probabilistic Machine Learning


I am a postdoctoral researcher at Aalto University, in the Probabilistic Machine Learning group, supervised by Samuel Kaski, Vikas Garg and Markus Heinonen. My work focuses on integrating human feedback into machine learning algorithms. We are specifically interested in applications involving Molecular Drug Design.

I successfully defended my PhD on February 18th, 2022. The manuscript can be found here and the slides here.

My thesis was conducted at Inria/Institut Curie, under 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.

  • Teaching:

    • 2019-2021: Teaching Assistant • 2nd year BSC • Université de Paris
      • Real Analysis (42h)
      • Multivariate Functions (18h)
    • 2018-2019: Teaching Assistant • 1st year BSC • Université de Paris
      • Mathematics and Calculus (60h)
  • Reviewing service:


  • Network Inference
  • Variable Selection
  • Applications in Medicine


  • PhD in Computer Science, 2022

    Inria / Institut Curie / Ecole polytechique

  • Master's Degree 2nd year - Random modelling, Finance and Data Science, 2018

    Université de Paris

  • Master's Degree 1st year - Applied Mathematics, 2017

    Université de Paris



Postdoctoral Researcher

Aalto University - Probabilistic Machine Learning Group

Feb 2022 – Present Espoo, Finland
Human-In-The-Loop Machine Learning

PhD Student

Inria EP Lifeware - Inserm U900 Syspharma - Ecole polytechnique

Oct 2018 – Feb 2022 Saclay / Saint-Cloud
On learning mechanistic models from time-series data with application to cancer chronotherapies


Inria EP Lifeware

Apr 2018 – Sep 2018 Saclay
Mechanistic model learning from time-series data


Université de Paris - Laboratoire MAP5

Jun 2017 – Aug 2017 Paris
Random matrix theory and applications to community detection in large graphs


Quickly discover relevant content by filtering publications.

Model Learning to Identify Systemic Regulators of the Peripheral Circadian Clock

A Statistical Unsupervised Learning Algorithm for Inferring Reaction Networks from Time Series Data

On Inferring Reactions from Data Time Series by a Statistical Learning Greedy Heuristics

Accelerating metabolic models evaluation with statistical metamodels - application to Salmonella infection models