Jeremy Grignard

PhD in Computer Science, Data and Artificial Intelligence

2019-2022

École polytechnique - Institut Polytechnique de Paris

Master of Science in Applied Mathematics - Data Science

2017-2018

Université Paris-Saclay - École polytechnique

Engineering Degree in Computer Science & Big Data

2014-2017

École Centrale d’Électronique de Paris, Heriot-Watt University

First year of health studies

2012-2014

University Pierre et Marie Curie, Paris

Scientific High School Diploma

2010-2012

Lycée Saint-Louis, Paris

Data Scientist

March 2022 - today

Institut de Recherches Servier

• Developing computational science methods to improve the early drug discovery

PhD student, Data Scientist

March 2019 – March 2022

Institut de Recherches Servier & Inria Saclay

• Applied computational methods to address scientific problems encountered in pharmaceutical research projects and support decision making

Research Intern, Data Scientist

April – September 2018

ONERA Toulouse

• Developed a deep learning pipeline for detecting abnormal flight trajectories using high-dimensional data (ADS-B), analyzed flight trajectories, inferred air traffic control orders

Research Intern, Computer Scientist

April – September 2017

Inria Saclay

• Designed and implemented a learning protocol to infer reaction and influence rules from temporal data. Evaluated the method on cell cycle model and DREAM challenge datasets

Computer Science Intern

June – July 2015 & 2016

Banque PSA Finance

• Designed and implemented two new modules (rights and alerts) for a global IT platform

Computational methods to improve the early drug discovery

PhD Manuscript - NNT : 2022IPPAX045 - Institut Polytechnique de Paris, 2022. [Link]

Jeremy Grignard

Mathematical modeling of the microtubule detyrosination/tyrosination cycle for cell-based drug screening design

In PLOS Computational Biology, 2022. [Link]

Jeremy Grignard, Véronique Lamamy, Eva Vermersch, Philippe Delagrange, Jean-Philippe Stephan, Thierry Dorval, François Fages

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

In CMSB - Proceedings of the seventeenth international conference on Computational Methods in Systems Biology, Lecture Notes in BioInformatics. Springer-Verlag, 2019. [Link]

Julien Martinelli, Jeremy Grignard, Sylvain Soliman, François Fages

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

In ICML - Workshop on Computational Biology, 2019. [Link]

Julien Martinelli, Jeremy Grignard, Sylvain Soliman, François Fages

Detecting Controllers' Actions in Past Mode S Data by Autoencoder-Based Anomaly Detection

In SID - 8th SESAR Innovation Days, 2018 [Link]

Xavier Olive, Jeremy Grignard, Thomas Dubot

Building Energy Optimization by Parameterization of Sources and Exchange Surfaces

In SDC - 5th Sustainable Development Conference, 2017 [Link]

Jeremy Grignard, Alexia Witkowicz, Nicolas Donnaint, Lydia Sadoudi, Kyota Lannelongue

ANRT Success Strory CIFRE - La lutte contre le cancer n’a pas de frontières

CANCER ET COOPÉRATION TRANSFRONTALIÈRE, Lyon, 06/28/2022

Jeremy Grignard

PhD Defense - Computational methods to improve the early drug discovery

Palaiseau, 06/17/2022

Jeremy Grignard

Pegasus: a knowledge graph to support drug discovery

Neo4j Health Care & Life Sciences Workshop, 2021 [Link Workshop] - [Link Video]

Neo4j Graph Data Platform Webinars, 2022 [Link Video]

Jeremy Grignard, François Fages, Thierry Dorval

An end-to-end pipeline to normalize and maximize the phenotypic information from high-content data for drug screening applications

5th Annual CytoData Society Meeting, 2020 [Link Workshop] - [Link Video]

Jeremy Grignard, François Fages, Thierry Dorval

Mathematical modeling of the microtubule detyrosination/tyrosination cycle for cell-based drug screening design

Séminaire GT-BIOSS, 2022 [Link]

Jeremy Grignard, Véronique Lamamy, Eva Vermersch, Philippe Delagrange, Jean-Philippe Stephan, Thierry Dorval, François Fages

Learning Mechanistic Models from Data for Screening Experiment Design and Drug Discovery

École thématique CNRS - Modélisation Formelle de Réseaux de Régulation Biologique, CNRS, PORQUEROLLES, 2019 ; École de recherche CIRM - Réseaux et Biologie Moléculaire, CIRM, Marseille, 2020 ; Journée d’accueil - Institut Polytechnique de Paris, 2019

Jeremy Grignard, François Fages, Thierry Dorval

A statistical unsupervised learning algorithm for inferring reaction networks from time series data

École thématique CNRS - Modélisation Formelle de Réseaux de Régulation Biologique, CNRS, PORQUEROLLES, 2019 ; École de recherche CIRM – Réseaux et Biologie Moléculaire, CIRM, Marseille, 2020

Julien Martinelli, Jeremy Grignard

Computational Modeling, Artificial Intelligence, And Knowledge Graphs To Improve The Early Drug Research Relevance

Servier SEEDPODS-Day - Innovation through sharing, 2021

Jeremy Grignard, François Fages, Thierry Dorval

PEGASUS - Federating Knowledge Graph For Disruptive Early Drug Discovery Applications - Data driven rational decision support to the project

Servier R&D Insightful Week 2021

Jeremy Grignard, François Fages, Thierry Dorval

Pegasus: A Knowledge Graph to Support Drug Discovery

Symposium Servier – Applications of Artificial Intelligence to New Drug Development, 2020 ; Neurology Immuno Inflammation Research Conference, 2021 ; Servier Corporate Strategy & Executive Director, 2021

Jeremy Grignard, François Fages, Thierry Dorval

Computational Modeling for the Early-Stage Drug Discovery Pipeline.

Servier Research Executive Commitee, 2020 ; Neurology Immuno Inflammation Research Conference, 2021

Jeremy Grignard, François Fages, Thierry Dorval

Present my research work in four minutes and exchange

France Culture, La Méthode Scientifique, La recherche montre en main, 2022 [Link - Intervention 40 min 48 sec]

Jeremy Grignard