•  Implemented, trained, tested and deployed a multi-layer convolutional neural network using the TensorFlow Python package to evaluate the 3D spatial inclinations of spiral galaxies: https://github.com/ekourkchi/incNET-data . The model is deployed and available online at http://edd.ifa.hawaii.edu/inclinet

• Developing and deploying machine learning applications to forecast meteorological time series. In this project, I built a Bayesian machine learning model based on the Gaussian Process Regression (GPR) methodology to forecast the meteorological parameters such as terrestrial evapotranspiration (ET) and rainfall. This model takes an auto-regressive approach where each value in the time series is predicted based on a few previous data points. Our model benefits from the Particle Swarm Optimization (PSO) technique to optimize the hyper-parameters of the adopted GPR kernel. Codes and more details are available here: https://github.com/ekourkchi/cropNet

• Developed an online graphical user interface, "Galaxy Inclination Zoo", that enables the citizen scientists to participate in measuring the inclinations of spiral galaxies: http://edd.ifa.hawaii.edu/inclination/
• Developed a web application/API that relates distance and velocity of galaxies: http://edd.ifa.hawaii.edu/CF3calculator/
• Developed an algorithm to identify galaxy groups/clusters in the local universe, with a visualization tool in Python: https://github.com/ekourkchi/skyplot

400+ hours of hands-on course material, with 1:1 industry expert mentor oversight, and completion of one in-depth capstone project. Mastered skills in the machine learning stack, including data wrangling at scale, deep learning, and building and deploying large-scale AI systems.