Computer Vision — created an image classification pipeline for 4 terabytes of image data. Used regex parsing via Python for data preprocessing, Keras for scratch training of LeNet architectures and transfer learning using pretrained VGG, ResNet, and Inception networks. Natural Language Processing — created a sentiment analysis system utilizing Scikit-learn. Used stop word reduction, data balancing, regex replacement. Anomaly Detection — programmed IOT embedded sensors via Python and C++, wrote bash scripts for automated data collection, used Scikit-learn for anomaly detection.
Developing a predictive analytics system utilizing IOT sensors and machine learning. Programmed Java plugins to connect a System Architecture Modeling Tool via OpenAPI calls. Developed descriptive architectures in UML, SysML, and UPDM to manage complex systems.
Programmed in LabVIEW to control NI DAQ cards to determine resonant frequencies of a wireless charger. Wrote hardware validation procedures.
Wrote standards procedures for composite pole pilots and updated various existing distribution standards procedures.
Created a stock calculator that uses HTTP calls to take data from the Alphavantage database and calculate a linear regression and stock return.\n
Created a image classifier via Tensorflow.js and Mobilenet.
GPA: 3.361