MS Applied Data Science @ University of San Diego

I'm a data science graduate student with hands-on experience across NLP, time series, computer vision, and generative AI. I care about building things that are usable and evaluated honestly, not just technically interesting. I tend to gravitate toward smaller teams, unstructured data problems, and the kind of infrastructure work that makes everyone else's job easier (shared data pipelines, documented functions, reproducible workflows).

• End-to-End ML Pipelines - ingestion, feature engineering, model building/eval, monitoring
• Natural Language Processing - Topic Modeling, schema and annotation design, TF-IDF, vector embeddings
• Time Series - ARIMA/SARIMA, scenario forecasting, external regressors, decomposition and diagnostics
• Transformers/Generative AI — RAG, LLM integration, agentic workflows, HuggingFace ecosystem, vector databases
• Evaluation & Implementation — Doesn't matter how cool something is if it doesn't work and/or can't be integrated

Languages & Libraries Python R SQL Pandas NumPy Scikit-learn Matplotlib Seaborn NLTK SpaCy

ML & Deep Learning PyTorch TensorFlow HuggingFace Transformers BERT XGBoost

Generative AI & LLMs LangChain LangGraph RAG OpenAI API Anthropic API Prompt Engineering Fine-tuning

MLOps & Infrastructure MLflow PostgreSQL Streamlit FastAPI AWS SageMaker Azure Git

Applying reinforcement learning to the ED-to-ICU transfer pathway using the MIMIC-IV dataset, framed around the failure-to-rescue problem in clinical decision-making.

M.S. Applied Data Science — University of San Diego (Expected Apr. 2026) Coursework: Applied Predictive Modeling · Time Series Analysis · Machine Learning · Applied LLMs

B.A. Sociology — UCLA (2013)

Agentic AI Essentials — Ready Tensor

Publishment link: NLP Tutor RAG Assistant

Deeply curious, results-driven, always learning.