Thomas Di Martino is a Machine Learning Engineer in Paris with eight years of experience applying deep learning to remote sensing and production ML systems. Currently at Criteo, he designed robust autoencoder-based solutions and data pipelines to mitigate noisy 12-bit PSB traffic for bidding models, bridging research rigor with production constraints. He completed a part-time PhD at SONDRA/ONERA focused on SAR time-series change detection and has complemented that work as an ESA visiting researcher and voluntary satellite-imagery expert for humanitarian NGOs. Comfortable across end-to-end ML—from multimodal siamese networks and ConvNets to deployable Flask services—he blends academic depth with hands-on software delivery. A not-obvious strength is his track record of translating complex SAR temporal analysis into practical tools for non-profits and industry alike.
8 years of coding experience
2 years of employment as a software developer
Master’s Degree in Engineering Computer Science, Master’s Degree in Engineering Computer Science at CY Tech
Doctorat de philosophie Remote Sensing, Doctorat de philosophie Remote Sensing at CentraleSupélec
Heriot-Watt University Edinburgh Campus
Licence Mathématiques et informatique, Licence Mathématiques et informatique at CY Cergy Paris Université
Contributions:10 commits, 10 pushes, 1 branch in 9 months
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Thomas Di Martino - Machine Learning Engineer at NASA Lifelines