Software Engineering • Backend Systems • Machine Learning • Entrepreneurship

I enjoy turning complex problems into practical, scalable solutions.
Currently building my skills across software engineering, backend development, machine learning, and cloud technologies.

I'm a Computer Science major with a minor in Business Management at Grambling State University, passionate about building software that solves real-world problems.

My interests sit at the intersection of software engineering, backend systems, machine learning, technology consulting and product development. I enjoy understanding how systems work under the hood, designing APIs and data-driven applications, and experimenting with machine learning to solve problems that traditional software approaches may not address.

I'm particularly interested in:

• 🧑‍💻 Software & Systems Engineering
• ⚙️ Backend Development & APIs
• 🤖 Machine Learning & AI
• ☁️ Cloud & Infrastructure
• 📊 Data-driven Applications
• 🚀 Product Development & Entrepreneurship

I also enjoy taking initiative outside of the classroom. I've worked with students, engineers, and technical teams on projects ranging from machine learning research and educational programs to hackathons and full-stack applications.

A mobile application designed to make traveling easier by combining trip planning, real-time travel assistance, budgeting, translation, and social features into one platform.

My focus: Backend Engineering

Key features:

• 🗺️ Trip discovery and itinerary planning
• 💰 Expense tracking and travel budgeting
• 🌎 Automatic local-language translation
• 📍 Real-time location alerts
• 🤝 Find people for rides, meals, and experiences
• 📈 Travel trend monitoring

Tech: React Native • Expo • TypeScript • Expo Router • NativeWind • Node.js • APIs

A machine learning project exploring whether technical indicators and historical market patterns can improve predictions of future market movement.

The initial model relied heavily on candlestick-pattern signals and performed poorly on test data. I iterated on the approach by incorporating additional technical indicators such as moving averages, stochastic indicators, and RSI, resulting in a substantial improvement in predictive performance.

What I learned:

• Feature engineering can have a major impact on model performance.
• A model should not depend on a single category of signals.
• Testing and iteration are critical when working with noisy real-world data.

Tech: Python • TensorFlow/Keras • Pandas • NumPy • Matplotlib • Machine Learning

Research project exploring how pretrained NLP models perform when applied across different domains.

The project compares sentiment-analysis models using datasets from financial text and Yelp reviews, investigating how domain differences can affect model performance and exploring Structural Correspondence Learning (SCL) as a potential approach to domain adaptation.

Focus areas:

• Natural Language Processing
• Transfer Learning
• Domain Adaptation
• Pretrained Transformers
• Model evaluation

Tech: Python • Hugging Face • Scikit-learn • Pandas • NLP

Built a convolutional neural network to classify handwritten digits using the MNIST dataset.

The project helped strengthen my understanding of:

• Convolutional neural networks
• Training and validation
• Loss functions
• Optimization
• Model evaluation

Tech: Python • PyTorch • NumPy • Matplotlib

Implemented a ranked-choice voting system in C using candidate structures, preference arrays, vote counting, and runoff logic.

The project strengthened my understanding of:

• C programming
• Arrays and data structures
• Algorithmic problem solving
• Memory and program organization

Tech: C

I'm continuously expanding my engineering toolkit, with a current focus on:

• ⚙️ Backend architecture & system design
• ☁️ Cloud infrastructure
• 🔌 API design and distributed systems
• 🤖 Machine learning & NLP
• 📱 Mobile application development
• 🧠 Data structures & algorithms
• 🏗️ Object-oriented design and software architecture

Worked with 100+ high school students during a five-week AI program alongside industry professionals.

Topics included:

• Machine Learning
• Natural Language Processing
• Convolutional Neural Networks
• Recurrent Neural Networks

I also helped design and deliver leadership development workshops focused on personal growth, communication, and leadership skills.

Collaborated with other students to organize and host conferences and hackathons aimed at strengthening the university's technology community and encouraging students to pursue careers in technology.

I'm always interested in meeting other developers, engineers, builders, and people working on interesting ideas.

Build. Learn. Iterate. Repeat.