Hi, my name is Gerges (جرجس), which is the arabic pronunciation of Georges, which is the french spelling of George, so everyone calls me George. I currently work as an applied scientist at Amazon, where I have been building and deploying machine learning and forecasting models for AWS workforce planning, and more recently for data center operations. Before joining Amazon, I was at Pacific Northwest National laboratory, doing all things related to Ultrasonics including measurement techniques, modeling and simulation, signal and image processing, machine learning applications for electroencephalograms (EEG). The intersection of computer science, mathematics, and physics is my happy place. When I’m not working, I am trying to get better at them, or out hiking.
PhD in Electrical Engineering, 2014
Michigan State University
Masters of Science in Information Networking, 2009
Carnegie Mellon University
BE in Computer and Communication Engineering, 2007
American University of Beirut
By Andrew Ng
This specialization includes 5 courses:
by Andrew Ng
In addition to completing the class, I fully re-wrote the class assignments (starter code + instructions) in python Jupyter notebooks, which allows interested students to submit the assignments using python instead of the native Matlab assignments (this does not apply to students taking class in exchange for a certificate).
by Fei-Fei Li, Justin Johnson, and Serena Yeung
Online class audit only (lectures + programming assignments)
Supporting regulatory guides for the nuclear power plant industry in relation to using ultrasound computational models for non-destructive inspection.
Building statistical methods to assess damage detection reliability and machine learning models for damage detection
Applications of ultrasounds in nuclear power plants including water detection in dry cask storage, defect detection in reactors, and health monitoring of radiation detectors.
Building sensor systems for reliable detection of fatigue cracks in multilayered aircraft fuselage.
Building signal processing and machine learning pipelines for defect detection and classification within steam generator tubes.