Summary
Manu Tom is a geospatial data scientist and computer vision engineer with a decade of experience building and deploying machine learning systems for remote sensing, environmental modelling, and climate risk analytics. He holds a PhD from ETH Zurich and has translated research into operational platforms at institutions including NASA JPL, where he containerized and serverlessly deployed a continent-scale river routing model on AWS for only USD 2.77 of compute. His technical toolkit spans Python, C++, MATLAB, high-performance and cloud computing, Docker, and multi-sensor fusion, and he has delivered models and toolboxes for stakeholders across academia, government and industry (ESA, UNESCO, NASA). Manu’s work bridges deep learning research—representational fusion for cross-sensor generalization and semantic segmentation—with practical validation campaigns (drone, terrestrial, radar) and cost-conscious cloud engineering. Now at CLIMADA Technologies, he focuses on geospatial climate risk analytics, combining hands-on coding with domain knowledge to turn satellite data into actionable insights. An uncommon thread in his profile is consistently shipping reproducible, production-ready research: pretrained models, benchmark datasets and cost-optimized cloud deployments.
10 years of coding experience
10 years of employment as a software developer
Bachelor of technology Electronics and Communication Engineering, Bachelor of technology Electronics and Communication Engineering at College of Engineering Trivandrum
Master of science Electrical Engineering Information Technology and Computer Engineering, Master of science Electrical Engineering Information Technology and Computer Engineering at RWTH Aachen University
Doctor of science. Computer Vision and Remote Sensing, Doctor of science. Computer Vision and Remote Sensing at ETH Zürich
Hindi, Tamil, German, Malayalam, English