Philip Popien


Philip is the Machine Learning Engineering Manager at Cloud to Street, leading the development of Cloud to Street's Machine Learning algorithms on optical and radar sensors. After receiving a Master of Science in Statistics, Philip applied, implemented and researched Deep Learning techniques for Computer Vision at Audi and Vexcel Imaging. He is passionate about Machine Learning competitions, where he won the Open AI Tanzania Challenge for Building Segmentation and achieved an all-time highest Kaggle rank of #58 out of 100,000 members. His implementations of AutoAugment and Fast-SCNN are widely used in the Machine Learning community. Philips aspiration is using Machine Learning to have a large positive impact on the world and is heavily influenced by the Effective Altruism movement.

Kaggle page
Blog post about AutoAugment

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