Skills

C/C++, Python, JavaScript, HTML/CSS, Bootstrap, Bash, Java, Jupyter, Node.js, Vue.js, MongoDB, REST API
MCUs (ARM Cortex-M), Communication Protocols (CAN bus, I2C, UART, SPI, PWM) Computer Networking (TCP/IP, MQTT, UDP), Arduino, Linux SBCs (Raspberry Pi, BeagleBone)
Google Cloud Platform (GCP), Linux Services, GUI (Tkinter, PyQT), Git, Data Analysis & Visualization (Pandas, NumPy, Matplotlib), Machine Learning (scikit-learn)
Technical Writing & Documentation, Creative Problem Solving, Interpersonal Communication

Air Quality Monitor

Used MicroPython with Raspberry Pi Pico with Kitronik Air Quality Datalogging Board to collect data
Data sent over MQTT to Linux SBC for data collection with InfluxDB (NoSQL) database
Webpage hosted with Node.js to view and analyze data over time

Implemented Random Forests (RF) ML Model to Identify Key Variables Contributing to Post-Wildfire Vegetation Regeneration
Used Jupyter to train RF model using scikit-learn and Numpy using GIS dataset
Analyzed frequency of features in top level nodes to proved insight into which variables contribute the most to vegetation recover

Used GPIO of RPI to control LED display
Collected USB data with microphone
Implemented Fast Fourier Transform (FFT) with Python to perform spectral analysis and correlate RGB to amplitude