Teaching & Mentoring

I teach graduate and undergraduate courses in Bayesian modeling, statistical computing with R, probability and inference, and statistical demography. I have taught at UMass Amherst, the National University of Singapore, UC Berkeley and Columbia University.

Training materials in Bayesian modeling

• Bayesian modeling for RMNCH coverage indicators: course modules and recordings on applied Bayesian statistical modeling
• Bayesian modeling in demography
• Bayes goes PAA 2026: materials from a workshop with Monica Alexander

Courses at UMass Amherst

Bayesian modeling

• Bayesian Methods in Clinical Trials (BIOSTATS 690B): new, Spring 2027 (course description)

  Bayesian statistical methods are increasingly central to the design and analysis of clinical trials. Regulatory agencies, including the U.S. FDA and the EMA, now actively encourage Bayesian approaches, particularly in settings involving historical data borrowing, adaptive designs and complex trial architectures.

  This course provides an introduction to Bayesian methods as applied to clinical trials, with emphasis on practical, methodological and regulatory considerations that arise in modern drug and device development. The course begins with a review of Bayesian inference, followed by modules on using prior information in clinical trials and Bayesian (adaptive) trial design. Advanced modules, selected based on student interest and course pace, may include Bayesian approaches to using real-world data in clinical trials, dose-finding, and Bayesian methods for master protocols for basket and platform trials. Throughout the course, students engage with published research papers, implement methods in R, and critically evaluate real trial designs and analyses.

• Introduction to Applied Bayesian Statistical Modeling (BIOSTATS 590B): Spring 2026

• Applied Bayesian Statistical Modeling (BIOSTATS 730): Spring 2017, Spring 2018, Fall 2022, Fall 2024

• Bayesian Computation in Biostatistics (BIOSTATS 697G): Spring 2016

Probability and inference

• Fundamentals of Probability and Statistical Inference (BIOSTATS 601): Fall 2026

Statistical computing with R

• Introduction to Statistical Computing with R (BIOSTATS 597D/530; PUBLIC HEALTH 490R): 2018–2020, 2025–2026
• Intermediate Statistical Computing with R (BIOSTATS 597E/631): Fall 2019, Fall 2020, Fall 2025
• Advanced Statistical Computing with R (BIOSTATS 597A/632): Fall 2019, Fall 2020, Fall 2025
• Advanced R for Data Science (BIOSTATS 690R): Fall 2018

Other

• Topics in Biostatistics and Data Science in Public Health (BIOSTATS 690P): Spring 2021 (course development, coordination and co-teaching)

Courses at other institutions (2009–2015)

National University of Singapore, Department of Statistics and Applied Probability

• Applied Regression Analysis (ST5202): Spring 2010, Spring 2011
• Regression Analysis (ST3131): Spring 2010, Spring 2012
• Demographic Methods (ST3244): Fall 2010, Fall 2011, Spring 2013
• Applied Time Series Analysis (ST3233): Fall 2013, Fall 2014
• Survival Analysis (ST5212): Spring 2015

UC Berkeley, Department of Demography

• Advanced Demographic Analysis (Demography 211): Spring 2014

Columbia University, Department of Statistics

• Linear Regression Models (W4315): Spring 2009

Mentoring

Students at UMass Amherst

• Lucas Godoy Garraza (PhD, UMass Amherst, 2024). Approaches to Causal Inference Using Bayesian Machine Learning and Nonparametric Methods with Applications to the Assessment of Effects of Family Planning Programming.

• Chuchu Wei (PhD, UMass Amherst, 2024). Improved Use of Compositional Data Subject to Self-reporting Errors in Bayesian Models with Applications to the Estimation of Family Planning Indicators.

• Herbert Susmann (PhD, UMass Amherst, 2022). Bayesian Hierarchical Temporal Modeling and Targeted Learning with Application to Reproductive Health.

• Zhengfan Wang (PhD, UMass Amherst, 2022). Development of Bayesian Temporal Models for Global Health Estimation in Data-Limited Settings.

• Emily Peterson (PhD, UMass Amherst, 2019). Bayesian Methods for the Assessment of Reporting Errors for Data-Sparse Population-Periods with Applications to Estimating Mortality.

• Barbara Mottey (MS, UMass Amherst, 2019). Spatial analysis of birth weight in Ghana.

• Greg Guranich (MS, UMass Amherst, 2019). To Space or to Limit? An Assessment of Levels and Trends in Unmet Need for Limiting and Spacing from 1990 to 2030 in low- and middle-income countries using a Bayesian Hierarchical Time Series Model.

Other students and postdocs

• Monica Alexander (PhD, UC Berkeley), now Associate Professor at the University of Toronto
• Fengqing Chao (PhD, National University of Singapore, 2017), now Assistant Professor at the Chinese University of Hong Kong, Shenzhen
• Niamh Cahill (postdoctoral fellow, UMass Amherst, 2016–2017), now Professor at Maynooth University in Ireland
• With Niamh, PhD students at Maynooth University:
  • Shauna Mooney (PhD, 2025). Advancing the Use of Service Statistics for Estimating Modern Contraceptive Use through Bayesian Modelling Approaches.
  • Hannah Comiskey (PhD, 2023). Estimating Modern Contraceptive Supply Shares at National and Subnational Administration Levels Using Demographic and Health Survey Data, with an Application to the Calculation of Estimated Modern Contraceptive Use.