Senior Computer Science student at University of North Carolina at Charlotte with a focus in AI and Robotics. While being a full time student I work as a Partner Ops Intern at Hexagon ALI where I also interened as a Software Development Intern in Summer 2024.
HTML 5
C++
Java
AWS
Python
SQL
Award winning Hack@Davidson project that allows users to look up non-profits.
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Social media app for UNC Charlotte students developed in software engineering team.
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Project apart of my undergraduate research that acts as a graph network of research data.
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Project using classification/regression models to predict NFL players careers.
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Non-Profit Seeker
Non-Profit Seeker is an award winning project of Hack@Davidson for "Technology of Best Use". Through using data provided by the DOL 5500, we used a set of "indicators" in order to help us rate how good a non-profit is. The equation that was formaulated takes in a mutlitiude of metrics made public through the DOL 5500 in order to rate a given non-profit. After the formulas were created, a website was made which makes these non-profits searchable in an easy, user friendly manner.
Niner Social
Social media app created for UNC Charlotte students developed within a 5-person team utizlizing SCRUM methodology and the MERN stack. Worked as a software engineer on the team in which I developed the login process for users that allowed users to both login and sign up. Also worked on a portion of the project that created a timeline of all posts in synchronous order of date uploaded.
theAdvisor
TheAdvisor is a project that I have worked on for a good majority of my undergruduate with the main focus of large-scale entity matching working with high volume data. Utlizing inexact matching algorithms such as k-mer hashing and levenshtein distance I was able to match entites between two datasets that would have taken 15 years but was reduced to 2 days through algorithm development and the use of parallel computing.
Predicting NFL Players Career Success
Project was apart of a former study that was to be replicated. Utlizing both classification and regression models was tasked to try and create a study of predicting if an NFL player will play in the NFL/ how long they will play in the NFL. Was able to uncover new features that help benefit both models accuracy as well as metrics that better represent the models performance than previously used such as confussion matricies, F1-Score, etc.
Lets Connect!
919-879-4929
davislspradling@gmail.com