My Projects
Work Projects
Conference Room Schedule Tablet
Description: Designed and developed a tablet-based calendar system for conference rooms that streamlines scheduling and prevents double-booking. The solution integrates with an existing signage management platform, utilizing a wall-mounted Android tablet to run a custom display that continuously pulls reservation data to indicate current meetings and display the day's schedule.
My Role: I led the design and development of the interface, ensuring the tablet was both user-friendly and seamlessly integrated into the conference room environment.
Details: This project was completed during my employment as an IT Support Assistant at FSU Athletics in Spring 2023. I worked on this project independently after being assigned it by my supervisor due to recurring scheduling conflicts with conference rooms. The project required me to collaborate with the facilities team to properly mount the tablets and ensure they had reliable power and network connections.
Interactive Displays for Baseball Legacy Room
Description: I developed an application that makes API calls to our signage management system, dynamically updating the layouts on the TVs in the Baseball Legacy Room to display players' stats and positions.
My Role: I collaborated on the design and integration of interactive elements, enabling users to tap buttons on the tablet to switch display layouts on the TVs that highlight specific baseball positions.
Details: This project was completed during my employment at FSU Athletics in Fall 2023. I worked as part of a three-person team with our Director of IT and another IT Support Assistant. We were assigned this project to enhance the visitor experience in the newly renovated Baseball Legacy Room. The project involved extensive coordination with the Baseball coaching staff to ensure the displays presented accurate historical information about former players.
Academic Projects
Text Analysis, Classification, and Prediction
Course: LIS4930 A.I Applications
Description: A comprehensive data analysis project that involved three major components:
- Text Analysis: Used NLTK for tokenization, POS tagging, and word frequency analysis of product reviews, extracting meaningful insights from unstructured text.
- Sentiment Analysis: Applied VADER sentiment analyzer to quantify the emotional tone of guitar product reviews, calculating compound scores to determine overall customer satisfaction.
- Email Classification: Built a Naive Bayes classifier to distinguish between spam and non-spam emails with 94% accuracy, including detailed performance metrics.
Skills Applied: Natural Language Processing (NLP), data preprocessing, statistical analysis, machine learning classification, Python programming with pandas, NLTK, and scikit-learn.
View on GitHub
Neural Networks and Image Classification
Course: LIS4930 A.I Applications
Description: Built a convolutional neural network (CNN) from scratch using TensorFlow and Keras to classify images from the CIFAR-10 dataset into 10 categories (airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck).
Technical Details:
- Implemented a multi-layer CNN architecture with convolutional layers, max pooling, dropout for regularization, and dense layers
- Processed and normalized 50,000 training images and 10,000 testing images (32x32 pixels, RGB)
- Configured Stochastic Gradient Descent optimizer with momentum for efficient model training
- Achieved significant accuracy improvement through multiple training epochs
- Deployed the model to classify new images through custom prediction functions
Skills Applied: Deep learning, neural networks, TensorFlow/Keras, image processing, model architecture design, hyperparameter tuning.
View on GitHub