teaching
STAT 41800 — Computational Methods in Data Science
Spring 2026 — Present
This course introduces essential computational methods in modern data science, focusing on simulation, resampling, Bayesian data analysis, and the utilization of large language models (LLMs) in data science workflows. Students learn foundational simulation techniques, key resampling methods including bootstrapping and cross-validation, and the practical and responsible use of LLMs in data science pipelines. The course culminates in capstone projects where students apply statistical and computational tools to evaluate AI systems using real research datasets.
Course Resources:
- Course Website — Textbook, interactive slides, and Jupyter notebooks
STAT 35000 — Introduction to Statistics
Fall 2022 — Present (Course Coordinator)
A data-oriented introduction to the fundamental concepts and methods of applied statistics. Topics include exploratory data analysis, experimental design, probability distributions and simulation, sampling distributions, confidence intervals and hypothesis tests for one and two samples, inference for contingency tables, regression, and correlation. Essential use is made of statistical software throughout. Prerequisite: two semesters of college calculus.
Course Resources:
- Course Website (Regular Session) — Sphinx-based digital textbook, simulations, and procedures flowchart
- Course Website (Winter Session) — Compressed 10-day format
- Video Learning Platform — 84 indexed lectures with interactive timeline and search
- LatticeAI — Course-constrained AI assistant (source code)
Previous Teaching
- STAT 30100 — Elementary Statistical Methods (Fall 2022, Summer 2017, 2015–2017)
- STAT 22500 — Introduction to Probability Models (Fall 2017 – Fall 2019, Head TA 2019–2022)
- Calculus, College Algebra, & Mathematical Ideas — Teaching Associate, CSUN (2012–2015)