Research

I am interested in the environmental impact of computer-based research. We developed the Green Algorithms project to push forward the discussion around this issue. More info here and on www.green-algorithms.org

I’m also interested in radiogenomics in cardiovascular diseases: how can we leverage modern imaging data (such as cardiac MRI), machine learning models and genetic information to better understand cardiovascular diseases.

During my PhD, I focused on better understanding and standardising the prediction of Protein-Protein Interactions (PPIs) using Machine Learning. More

I also contribute to clinical studies to advise on statistical methodology.

Selected publications

A complete list of publications is available on the “publications” tab and on Google Scholar.

• L. Lannelongue, H. G. Aronson, A. Bateman, E. Birney, T. Caplan, M. Juckes, J. McEntyre, A. D. Morris, G. Reilly and M. Inouye, “GREENER principles for environmentally sustainable computational science”, Nature Computational Science, June 2023.

• L. Lannelongue, and M. Inouye, “Construction of in silico protein-protein interaction networks across different topologies using machine learning”, bioRxiv, February 2022.

• L. Lannelongue, J. Grealey and M. Inouye, “Green Algorithms: Quantifying the Carbon Footprint of Computation”, Advanced Science, May 2021.

• L. Lannelongue, J. Grealey, A. Bateman and M. Inouye. “Ten Simple Rules to Make Your Computing More Environmentally Sustainable.” PLOS Computational Biology, October 2021

• J. Grealey, L. Lannelongue, W. Saw, J. Marten, G. Meric, S. Ruiz-Carmona and M. Inouye, The carbon footprint of bioinformatics, Molecular Biology and Evolution, February 2022.

Selected talks (for an overview of the Green Algorithms projects)

A full list of talks with links to slides and recordings (when available) can be found here.

For (computational) biologists:
The environmental impact of computational biology
EMBL-EBI's training webinar, November 2022
[recording and slides]

For data scientists:
The carbon footprint of machine learning: how bad is it and what can we do about it?
Invited talk, SGAI-2022 International Conference on AI (Cambridge, UK), December 2022
[slides]

For trainers:
Training the next generation of sustainable computational scientists
Keynote talk at the "Education in Biology" track at ISMB 2022 (Madison, WI, USA), July 2022
[slides]

Science communication

• L. Lannelongue, Carbon footprint: the (not so) hidden cost of high performance computing, ITNOW, January 2022.

• L. Lannelongue, Data scientists déconnectés - machine learning et covid-19, Variance ENSAE, July 2021.

• L. Lannelongue, J. Grealey and M. Inouye Green algorithms for health data science, HDR-UK, March 2020.