As part of my undergraduate Senior Design Project, my team contributed to the NASA Psyche Mission. Our collaboration led to the development of an advanced data mining tool, extracting key datasets from existing Jet Propulsion Laboratory journals. Employing techniques like the Kalman filter, we refined dataset attributes for comprehensive analysis. Optimized through data cleaning methods like Principle Component Analysis, we employed a deep belief network to discern operational nuances of a hall effect thruster in various environments. These insights provided value in testing before deploying to a non-terrestial environment.

Engaged as a student researcher at Pennsylvania State University under Dr. Abdallah, I bolstered ongoing research in physical layer network security. The research aimed to fingerprint devices by analyzing quadrature errors created by microscopic manufacturing errors imprinted onto transceiver devices, allowing for network verification to prevent attacks like the man-in-the-middle attack. My contributions extended to redesigning and developing a real-time data collection system, harnessing software-defined radio to precisely capture and validate signals created by nRF24L01+ transceivers. Additionally, in collaboration with graduate research, I successfully reverse engineered Ettus B200/N210 Software Defined Radio drivers, showcasing my proficiency in driver analysis.

Over the course of two weeks, my team and I engineered an audio streaming proof of concept, enabling seamless audio dissemination from a single host to multiple clients. The system features automatic metadata synchronization, providing real-time updates on the current track, music timestamp, and album details. Leveraging encoded data and web sockets, the solution ensures accelerated transmission speeds and minimized audio loss. This project encompasses my passion for music in the creation of sharing multimedia sources across multiple devices with low latency.

This project seamlessly merges art and technology, transforming intricate 3D images into an engaging visual journey. Captivated within the realm of visual programming, I developed a simulation tool that parses any 3D model into a complex neural network interpretation. Leveraging geometric and polar functions to add depth and style to the simulation, the application creates a beautiful design with the full capability of traversing the 3D network, resembling an artistic interpretation of synapses firing throughout the network.

Continuing a profound fascination with visualizing the artistic aspects of data modeling, I created an enhanced visual design program aimed at illustrating relationships among localized data points within a 2D space. To optimize frame rates, I implemented Quad Trees for storing positional data in each frame. This enabled the smooth movement of points across a vector field generated using Perlin noise, forming connections between neighboring nodes through lines. Furthermore, I incorporated logic to automatically detect the formation of triangular cycles. My overarching objective was to push the boundaries of real-time constraints while handling intricate logic surrounding big data.