iOS Applications — Swift & SwiftUI
Designed and developed iOS applications as part of Apple’s Swift Development program. Built responsive user interfaces using SwiftUI while applying software engineering principles including MVC architecture, data persistence, networking, and debugging. Focused on creating intuitive user experiences and translating technical requirements into functional mobile applications.
Technologies: Swift, SwiftUI, Xcode
Investment Analytics Models — Greenwood Project
Developed investment analytics models to support data-driven decision-making and forecasting. Gathered stakeholder requirements, analyzed financial data, automated reporting workflows, and created visualizations to communicate insights. Leveraged AI-assisted documentation techniques to organize findings while maintaining human review of assumptions and recommendations.
Technologies: Python, Excel, Jupyter Notebook
Trading Strategy Simulator — Greenwood Project
Designed and evaluated trading strategy simulations using Python and Jupyter Notebook. Analyzed historical market scenarios, tested model assumptions, and assessed strategy performance under varying conditions. Collaborated with stakeholders to translate business objectives into technical solutions and validate results through testing and analysis.
Technologies: Python, Jupyter Notebook, Data Modeling
Natural Language Processing & Text Classification
Developed machine learning models to classify and analyze text data as part of Computer Science and Linguistics coursework. Applied natural language processing techniques, feature engineering, data preprocessing, and model evaluation to investigate how machine learning systems interpret language. Documented model limitations, misclassifications, and opportunities for improvement while exploring the challenges of ambiguity in human communication.
Technologies: Python, pandas, scikit-learn, Jupyter Notebook, NLP, Machine Learning
Prompt Sensitivity MVP
Designed a machine learning evaluation project to examine how AI systems respond to ambiguous or uncertain inputs. Analyzed classification outcomes, confidence levels, and model behavior to identify situations where automated predictions may require human review. Explored the relationship between model performance, uncertainty, and responsible AI decision-making while developing a framework for evaluating reliability and risk.
Technologies: Python, scikit-learn, Machine Learning
AI Evaluation & Text Classification Analysis
Evaluated machine learning classification models using accuracy, precision, recall, F1-score, confusion matrices, and confidence thresholds. Investigated failure cases, analyzed sources of error, and explored methods for improving model reliability through human oversight and structured evaluation processes. This project strengthened my understanding of responsible AI development, model validation, and ethical decision-making in AI-assisted systems.
Technologies: Python, scikit-learn, Machine Learning, Model Evaluation, Responsible AI