Sebastià Franch-expósito
Lead Scientist at Tempus AI
New York, New York, United States
Summary
👤
Senior🎓
Top SchoolSebastià Franch-expósito is a translational oncology scientist and lead scientist at Tempus AI with nine years of experience bridging cancer biology, genomics, and clinical real-world data to drive AI-enabled clinical insights. He progressed through rigorous academic and research roles—including a multi-year postdoctoral program at Memorial Sloan Kettering—and now leads applied research teams translating multimodal molecular and transcriptomic signals into actionable diagnostics. His background combines a PhD-track in bioinformatics and formal training in biomedicine and biochemistry with hands-on lab and clinical research across European and U.S. centers. At Tempus he blends domain expertise with product-minded AI applications, focusing on making complex genomic signals usable in clinical workflows. Notably, he maintains an academic teaching footprint from his time as a consulting professor, signaling a commitment to training the next generation of bioinformaticians. Colleagues describe him as a pragmatic scientist who pairs deep mechanistic knowledge with practical data science to accelerate translational impact.
9 years of coding experience
5 years of employment as a software developer
UOC (Universitat Oberta de Catalunya)Grado en Bioquímica Bioquímica, Grado en Bioquímica Bioquímica at Universitat de les Illes BalearsMedicine and Translational Research PhD program Bioinformática, Medicine and Translational Research PhD program Bioinformática at Universitat de Barcelona
Spanish, Catalan, English
Stackoverflow
1reputation
0reached
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Github Skills (17)
region7
classification-model7
genome6
bioinformatics5
genomics5
computation4
classification4
segmentation4
sparklines4
deep-learning3
profiling3
gt2
data-science2
r2
data-visualization2
Programming languages (2)
Github contributions (5)
ait5/CNApp
Oct 2018 - Jan 2020
CNApp uses genome ranges files, from multiple genomic platforms, in a workflow-like approach through three main functionalities: (1) re-segmentation and scores computation, (2) genome region profiling and (3) classification model predictions.
Contributions:35 commits, 2 PRs, 121 pushes in 1 year 3 months
genomescoresbioinformaticsclassificationcomputation
ait5/Exercice_7
May 2017 - May 2017
Contributions:4 pushes, 1 branch in 1 day
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