Data & Analytics
ETL pipelines, analysis, and dashboards for decision-making (Pandas, NumPy, visualization).
CS Undergrad @ Howard • Graduating May 2026
I build data & ML systems that turn messy data into usable products.
I’ve worked on Retrieval-Augmented Generation (RAG), NLP, and analytics pipelines—shipping tools like a legal Q&A assistant and a census analytics dashboard.
Python
SQL
NLP • RAG
PyTorch
ChromaDB
AWS
Education
Howard University
B.S. in Computer Science
Graduation: May 10, 2026
GPA: 3.7/4.0
What I do
A quick snapshot of how I work end-to-end.
Machine Learning
Build and evaluate models with strong baselines, metrics, and iteration loops (scikit-learn, PyTorch).
NLP + RAG
Retrieval pipelines, embeddings, and evaluation to improve answer quality (Transformers, ChromaDB).
Featured Projects
A few highlights — see the full list on the Projects page.
MNIST Digit Classification - Machine Learning Implementation
Implemented a K-Nearest Neighbors classifier for handwritten digit recognition achieving 97%+ accuracy on the MNIST dataset. Applied systematic hyperparameter optimization using grid search and cross-validation techniques.
Python, Scikit-Learn, NumPy, Pandas, Matplotlib
Hybrid Movie Recommender System
hybrid-movie-recommender-system
Python, Scikit-Learn, Tkinter