I first studied in Paris (France) at Lycée Saint-Louis and ENSAE Paris where he earned a BSc and a French Diplôme d’Ingénieur (MSc) studying mainly mathematics and statistics, but also theoretical physics and economics. During these years, I interned as a Data Scientist at Sidetrade (Paris) and Amazon EU (Luxembourg). In 2017, I headed to the University of Oxford, where I completed an MSc in Statistics and Machine Learning. I joined Cambridge and the Cardiovascular Epidemiology Unit in 2018 for a PhD in Health Data Science supervised by Prof. Michael Inouye and supported by the MRC-DTP. My PhD, completed in 2022, looked at machine learning tools used to predict protein-protein interactions and the carbon footprint of computational research. I am now a Research Associate in Biomedical Data Science in the department and the Inouye lab. I am also a College Post-Doctoral Associate at Jesus College, Cambridge, and an Associate Fellow of Advance HE. Full CV here.
• Combining machine learning, genomics and medical imaging to better understand diseases, in particular cardiovascular ones.
• Helping clinicians leverage artificial intelligence tools for patient care.
• Sustainability in research: how to quantify and reduce the carbon footprint of computational science. We developed the Green Algorithms project to promote best-practices.
• Biostatistics modelling for clinical studies (human and veterinary medicine).
For more details and updates on these projects, see my website and my Twitter feed.
Of special interest:
• Lannelongue, L., Grealey, J., Inouye, M., 2021. Green Algorithms: Quantifying the Carbon Footprint of Computation. Advanced Science 8, 2100707. https://doi.org/10.1002/advs.202100707
• Lannelongue, L., Grealey, J., Bateman, A., Inouye, M., 2021. Ten simple rules to make your computing more environmentally sustainable. PLoS Comput Biol 17, e1009324. https://doi.org/10.1371/journal.pcbi.1009324
• Lannelongue, L., Inouye, M., 2022. Construction of in silico protein-protein interaction networks across different topologies using machine learning. bioRxiv. https://doi.org/10.1101/2022.02.07.479382
Full list of publications on Google Scholar.