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