Ge[o]rges Dib

When we try to pick out anything by itself, we find it hitched to everything else in the universe.
— John Muir

Experience

 
 
 
 
 
February 2019 – Present
Seattle, WA

Research Scientist

Amazon Web Services HR Analytics

 
 
 
 
 
August 2017 – September 2018
Richland, WA

Research Scientist

Pacific Northwest National Laboratory

 
 
 
 
 
September 2014 – August 2017
Richland, WA

Research Associate

Pacific Northwest National Laboratory

 
 
 
 
 
May 2010 – July 2010
Chennai, India

Intern

Indian Institute of Technology, Madras

 
 
 
 
 
August 2009 – August 2014
East Lansing, MI

PHD Researcher

Michigan State University

 
 
 
 
 
August 2008 – May 2009
Pittsburgh, PA

Research assistant

Carnegie Mellon University

 
 
 
 
 
June 2006 – August 2006
Berkeley, CA

Intern

University of California at Berkeley

Accomplish­ments

Deeplearning.ai Specialization

Coursera

By Andrew Ng

This specialization includes 5 courses:

  • Neural networks and deep learning
  • Improving deep neural networks: Hyperparameter tuning, regularization and optimization
  • Structuring machine learning Projects
  • Convolutional neural Networks
  • Sequence models
Jul 2018 – Jul 2018

Deeplearning.ai Bootcamp

Deeplearning.ai

I was selected to participate in an intensive 2-day practical deep learning bootcamp organized by deeplearning.ai. We worked in a team of 4 on selecting, training, testing, and demonstrating an object detection detection model. The bootcamp went through all the steps required to build a successful model, starting for understanding client requirements to deploying the model on an android phone.
Feb 2018

Machine Learning

Coursera

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).

Find the python assignments on Github

Star

Aug 2017 – Dec 2017

Deep Learning (CSCI 497J/597J)

Western Washington University

by Brian Hutchinson
Jun 2017

CS231n Convolutional Neural Networks for Visual Recognition

Stanford

by Fei-Fei Li, Justin Johnson, and Serena Yeung

Online class audit only (lectures + programming assignments)

Sep 2015

The Finite Element Method for Problems in Physics

Coursera

by Krishna Garikipati

Recent Publications

Second harmonic generation using nonlinear ultrasonic waves have been shown to be an early indicator of possible fatigue damage in …

The Pacific Northwest National Laboratory (PNNL) is conducting confirmatory research for the U.S. Nuclear Regulatory Commission for …

Guided wave structural health monitoring uses sparse sensor networks embedded in sophisticated structures for defect detection and …

Projects

Completed

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Neural Interactive Machine Learning

Building a brain-computer interface for rapid image labeling

Validation of Ultrasound NDE Simulation Models

Supporting regulatory guides for the nuclear power plant industry in relation to using ultrasound computational models for non-destructive inspection.

Machine Learning and Statistical Signal Processing for Guided Wave Structural Health Monitoring

Building statistical methods to assess damage detection reliability and machine learning models for damage detection

Ultrasound Sensors and Methods for Nuclear Power Plant Environments

Applications of ultrasounds in nuclear power plants including water detection in dry cask storage, defect detection in reactors, and health monitoring of radiation detectors.

Giant Magnetoresistance Sensors for Aircraft Inspection

Building sensor systems for reliable detection of fatigue cracks in multilayered aircraft fuselage.

Eddy Current Inspection Autoanalysis of Steam Generator Tubes in Nuclear Power Plants

Building signal processing and machine learning pipelines for defect detection and classification within steam generator tubes.

Wireless Senor Networks for Structural Health Monitoring

Building hardware interfaces for integrating ultrasound guided waves sensors with wireless sensors.