Philip Popien

Philip Popien has a diverse work experience in the field of machine learning. Philip is currently serving as the Staff Machine Learning Engineer and Director of ML at Cloud to Street, where they are responsible for leading the machine learning team and developing deep learning models for flood water detection on satellite imagery.

Prior to this, Philip worked as a Senior Machine Learning Engineer and Team Lead at Cloud to Street, where they managed a team of three machine learning engineers and supervised the creation of deep learning models for flood water detection.

Before joining Cloud to Street, Philip worked as a Deep Learning Engineer at Vexcel Imaging GmbH. In this role, they developed Vexcel's Deep Learning library from scratch and trained models for low and high-resolution semantic segmentation.

Philip also gained experience as a Machine Learning Engineer at AUDI AG, where they conceptualized, developed, and demonstrated machine learning proof of concepts for internal customers, such as the production facility.

Additionally, Philip has held positions as a Co-Founder at Datenschmiede, where they contributed to the development of the company, and as a Machine Learning Working Student at foodspring, where they applied their skills in machine learning.

Philip's academic experience includes a Master's thesis at ÜberEnergy, where they created a proof of concept for an automatic smart heating control solution using machine learning. Philip programmed the system with Python.

Philip Popien completed their education at two institutions in Berlin. Philip earned a Bachelor of Science (B.Sc.) degree in Economics from Freie Universität Berlin, where they studied from 2009 to 2013. Philip then pursued further studies at Humboldt-Universität zu Berlin, earning a Master of Science (M.Sc.) degree in Statistics from 2013 to 2016. Additionally, they obtained a certification of Master of Science from Humboldt-Universität zu Berlin, although the specific month and year of obtaining this certification were not provided.