Data Scientist & Developer
Machine Learning • Predictive Modeling • Data Stream Classification • Concept Drift
I work on machine learning, predictive modeling, and data-driven systems — and build tools to deploy them. I'm interested in how data can answer hard questions and where AI meets real-world problems.
Currently pursuing my M.Sc. in Data Science with a research focus on supervised classification in data streams and managing concept drift. Graduate of the Apple Developer Academy with 7+ years in math and data education.
Open to opportunities.
| Area | Description |
|---|---|
| ML & Predictive Modeling | Training models for forecasting, classification, and pattern discovery — from small datasets to large-scale pipelines |
| Data Streams & Concept Drift | Research on supervised classification in streaming data and maintaining model accuracy amid shifting distributions |
| Data Analytics & Statistics | Exploratory analysis, statistical testing, experimental design, and visualization |
| Data-Driven Software | Converting models into usable applications — background from Apple Developer Academy |
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Deep learning pipeline classifying dermatoscopic images into 7 skin lesion categories using CNNs on the HAM10000 dataset. Achieved 77.7% validation accuracy.
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Large-scale sentiment classification of 1.6M tweets using Apache Spark and distributed ML on Databricks. Logistic Regression achieved 77.96% accuracy.
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iOS app that disrupts automatic thinking through daily micro-activities, promoting mindfulness and present-moment awareness through sensory prompts.
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Galaxy-themed iOS app built at Apple Developer Academy to help writers overcome creative block through timed challenges and 100+ research-backed prompts.
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Complete relational database design for a full e-commerce platform — ER modeling, normalization to 3NF, and SQL query suite.
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Comprehensive R toolkit for experimental data analysis — parametric and non-parametric tests, ANOVA, post-hoc analysis, and assumption checking.
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View all projects on my portfolio →