Julien Martinelli

Julien Martinelli

Postdoctoral researcher in Machine Learning

Inria - Statistics in System Biology and Translational Medicine

News

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
  • 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:

Miscellanous

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

Interests

  • Biologically-Informed Machine Learning
  • Mechanistic Modeling
  • Bayesian Experimental Design
  • Human-In-The-Loop Machine Learning

Education

  • PhD in Computer Science, 2022

    Inria / Institut Curie / Ecole polytechnique

  • 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

Experience

 
 
 
 
 

Postdoctoral Researcher

Inria Bordeaux Sud Ouest - SISTM team

Mar 2024 – Present Bordeaux, France

Learning misspecified mechanistic models from heterogeneous patient data

  • Physics-Informed Machine Learning
  • Gaussian Processes
  • Mechanistic modeling, Mixed-Effect Models
 
 
 
 
 

Postdoctoral Researcher

Aalto University - Probabilistic Machine Learning Group

Feb 2022 – Feb 2024 Espoo, Finland

Human-In-The-Loop Machine Learning - Application to de novo drug design

  • Bayesian Inference
  • Bayesian Experimental Design, Bayesian Optimization, Gaussian Processes
  • Deep Generative Models
 
 
 
 
 

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

  • Learning ODE models of biological networks
  • Sparse regression
  • Mechanistic modeling of the circadian clock
  • PK-PD modeling
 
 
 
 
 

Intern

Inria EP Lifeware

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

Intern

Université de Paris - Laboratoire MAP5

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

Contact