**Vassar College**

**Fall 2020 – on leave**

**Courses that I’ve taught**

MATH 126 Calculus IIA: Integration Theory

MATH 127 Calculus IIB: Sequences and Series

MATH 141 Introduction to Statistical Reasoning

CPMU / MATH 144 Foundations of Data Science

MATH 220 Multivariable Calculus

MATH 240 Introduction to Statistics

MATH 241 Probability

MATH 290 Field Work

MATH 301 Data Confidentiality

MATH 341 Statistical Inference

MATH 347 Bayesian Statistics (in Fall 2017, Spring 2019 and Fall 2019, this course was offered through LACOL; find out more on the subpage Online/Blended Education)

MATH 298/399 Independent Study; current and previous topics include:

- Bayesian Estimation of Future Realized Volatility
- Bayesian Inference with Python
- Bayesian Methods for Sparse Data
- Bayesian Non-Parametric Models
- Bayesian Time Series
- Bayesian Variable and Model Selection
- Identification Risks of Partial Synthetic Data
- LACOL Course Developer
- Python for Data Science
- Topics of Data Science
- Tree-Based Meth/Synthetic data

**Duke University**

Instructor, STA 101 Data Analysis and Statistical Inference, Summer 2014

Recipient of Certificate of College Teaching, Graduate School, Duke University, May 2015

**Short courses/workshops**

**– Joint Statistical Meetings 2020**

Bayesian Thinking: Fundamentals, Computation, and Hierarchical Modeling, November 2020 (co-instructor: Jim Albert)

**– Blended Learning in the Liberal Arts Conference**

Teaching a Shared/Hybrid/Online Course using Zoom, May 2019 (slide deck)

*– U.S. Bureau of Labor Statistics*

Introduction to Bayesian Inference in R, October 2018

*– CIRJE, University of Tokyo*

The Dirichlet Process and DP Mixture Models, January 2018