I’m a Computer Science student at James Madison University who enjoys building AI-driven systems, automation pipelines, and applied machine learning solutions.
I’m especially interested in work that connects machine learning with real software systems and real users.
I’ve worked on industry-facing AI projects across CRM platforms, document intelligence, and automation tools, with experience at Salesforce, Wayfair, and early-stage AI startups. I enjoy tackling problems involving messy data, system design decisions, and figuring out how ML ideas actually hold up in practice.
- Applied machine learning and ML systems
- AI automation and agent-based workflows
- LLM-powered systems including retrieval and embeddings
- Data analytics and decision support tools
- Quantum computing and quantum machine learning (research-focused)
| Project / Role | What I Worked On | Tech | Link |
|---|---|---|---|
| Salesforce AI Studio Fellow | Built CRM-integrated AI features for sales and marketing workflows, including lead scoring, customer risk signals, personalized insights, and natural language access to data. | Python, Machine Learning, Generative AI, Vector Embeddings | Repo |
| Wayfair AI Automation Externship | Developed AI agents to automate trend discovery and competitor monitoring, and built data pipelines to support category-level market analysis and decision-making. | Python, n8n, AI Agents, Data Analysis | Private |
| Outamation AI Document Intelligence Externship | Designed an end-to-end document intelligence pipeline for large mortgage files using OCR and retrieval-based methods, focusing on structured data extraction from unstructured documents. | Python, OCR, LlamaIndex, RAG | Private |
| Quantum LSTM Research for Financial Forecasting | Conducted an empirical study comparing classical LSTM models with quantum-enhanced variants (QLSTM and QK-LSTM) for financial time-series forecasting. Evaluated hybrid quantum models across Qiskit and PennyLane, demonstrating competitive performance with fewer parameters under NISQ constraints. | Python, PennyLane, Qiskit, Quantum ML, C++ | Repo |
| QuantumPedia x QWorld Quantum ML Intern | Contributed to research on privacy-preserving quantum machine learning models for financial systems and helped develop tooling for distributed quantum ML experimentation. | Qiskit, PennyLane, Quantum Machine Learning | Private |
More academic, ML, systems, and AI projects are available across my repositories.
Languages: Python, Java, C, JavaScript, TypeScript
Machine Learning & Data: scikit-learn, Pandas, NumPy, PyTorch, TensorFlow, Keras
LLMs & AI Systems: RAG pipelines, vector embeddings, retrieval systems, prompt orchestration, agent-based workflows
Quantum Computing: Qiskit, PennyLane, hybrid quantum–classical models
Databases & Storage: ChromaDB, MySQL, PostgreSQL, DynamoDB
Dev & Cloud Tools: Git, Linux, AWS, Vercel, n8n, Jupyter, Docker (basic)
