BINGHAMTON UNIVERSITY
Fall 2026, on leave
Courses I've taught
MATH 447 Probability Theory
MATH 488S/590S Bayesian Statistics
VASSAR COLLEGE
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 (teaching and learning material from Spring 2021)
MATH 242 Applied Statistical Modeling
MATH 290 Field Work
MATH 301 Data Confidentiality / Statistical Data Privacy
MATH 341 Statistical Inference
MATH 347 Bayesian Statistics (teaching and learning material from Fall 2019; in Fall 2017, Spring 2019, Fall 2019 and Spring 2022, this course was offered through LACOL; find out more on the subpage Online/Blended Education)
MATH 388 Statistical Data Privacy
MATH 298/399 Independent Study; current and previous topics include:
Attribute Disclosure Risk Evaluation for Synthetic Data
Bayesian Estimation of Future Realized Volatility
Bayesian Inference with Python
Bayesian Methods for Sparse Data
Bayesian Network Analysis
Bayesian Non-Parametric Models
Bayesian Time Series
Bayesian Variable and Model Selection
Identification Risks of Partial Synthetic Data
LACOL Course Developer
Machine Learning Email Classification
Pedagogical Partnership
Python for Data Science
Think Bayes
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
– ASA Traveling Course, Philadelphia and Southern Ontario Chapters
Introducing Bayesian Statistical Analysis into Your Teaching, Fall 2023 (co-instructor: Kevin Ross) (resources)
– eCOTS 2022
Introducing Bayesian Statistical Analysis into Your Teaching, May 2022 (co-instructor: Kevin Ross) (resources)
– USCOTS 2021
Introducing Bayesian Statistical Analysis into Your Teaching, June 2021 (co-instructor: Kevin Ross) (resources)
– Joint Statistical Meetings 2020
Bayesian Thinking: Fundamentals, Computation, and Hierarchical Modeling, November 2020 (co-instructor: Jim Albert)
– National Center for Science and Engineering Statistics (NCSES) Lecture Series
Data Privacy: Bayesian Data Synthesis and Differential Privacy, June 2020
– 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