Applied Data Science and Machine Learning Program, Massachusetts Institute of Technology (MIT)
Completed advanced coursework in classification, regression, prediction, and recommendation systems, applying concepts through hands-on projects., Gained proficiency in supervised, unsupervised, and deep-learning methods, establishing a strong foundation for developing data-driven solutions., Acquired hands-on experience with data preprocessing, feature engineering, and model evaluation for large-scale datasets.
Overview
2027
2027
years of professional experience
Work History
Graduate Research Assistant
University of Central Florida
08.2025 - Current
Developed reproducible Databricks + PySpark analysis workflows on large healthcare claims datasets to identify diabetes diagnosis, treatment, and self-management patterns; packaged outputs for research stakeholders.
Collaborated with faculty/data scientists to refine and validate cohort-identification logic for diabetes populations across multiple claim types, enhancing consistency in downstream analyses.
Managed data transformation + quality control for millions of records, ensuring repeatable, auditable analytical workflows with clear assumptions, consistent transformations, and thorough documentation.
Supported publication and policy-driven research by translating pipeline outputs into interpretable results artifacts (tables/figures/notes) and aligning analyses with real-world healthcare decisions.
Customer Service Staff
Publix Super Markets
06.2019 - Current
Resolved customer complaints and issues promptly, applying problem-solving methodologies to maintain a positive store atmosphere and improve overall customer satisfaction.
Managed high-volume financial transactions and daily bookkeeping functions, ensuring data accuracy and accountability for cash drawers and store deposits.
Managed product display and inventory data, ensuring organized shopping environment and supporting effective stock management.
Cyberbullying Detection Capstone Project
University Of Georgia
Athens
Implemented and evaluated neural NLP baselines (GRU/LSTM) and a transformer model (BERT) for multi-class cyberbullying detection; Fine-tuned pretrained bert-base-uncased using the Hugging Face Transformers training workflow.
Designed experiment plan (train/val/test split, hyperparameter sweeps, ablation studies) and achieved ~93% accuracy with the optimized BERT configuration on a ~47k-tweet dataset, outperforming RNN baselines.
Applied SHAP-based interpretability to analyze influential tokens/features driving cyberbullying predictions, improving understanding of model behavior and failure modes.
Lead Conversion Prediction Project
Remote
Built and compared classical ML models (Decision Tree, Random Forest) for conversion prediction; performed feature engineering and model selection aligned to business decision-making.
Tuned models with cross-validated grid search (GridSearchCV) and documented key conversion drivers to support resource allocation recommendations.
Delivered data-driven recommendations that informed resource allocation decisions and improved conversion strategies for the
company.
Education
Ph.D. - Big Data Analytics
University of Central Florida
Orlando, FL, United States
03-2029
Bachelor of Science - Data Science, Minor in Computer Science