Graduate Student Instructor, University of Michigan

Experimental Design and Analysis

(For graduate students) Summer 2021, Summer 2022
This course introduces experimental methods. Students learn basic principles for successful experimentation: picking a good problem; subject recruitment; designing and conducting experiments (both laboratory and online); collecting and analyzing data, and reporting results.

Data Science for Social Good

(For graduate students) Summer 2021, Summer 2022
This course analyzes the motivations and incentives for people to contribute to public goods. Students learn how to apply causal inference techniques and social science theories to nudge pro-social behavior.

Introduction to User Modeling

(For undergraduate students) Fall 2018, Fall 2021, Fall 2022
This course provides an integrated overview of techniques to model user behavior from microeconomics theory, behavioral economics and computer science.

Choice Architecture

(For graduate students) Winter 2018, Winter 2019
This course introduces topics in behavioral economics, including anchoring effect, confirmation bias, narratives, overconfidence, intertemporal choice, prospect theory and framing, which help future information system professionals, designers and managers understand human decision rules and their associated biases.

Programs, Information and People

(For undergraduate students) Winter 2022
This course overs the fundamental elements of Python programming and how to access data on the internet.

Introduction to Information Studies

(For undergraduate students) Fall 2017, Fall 2020, Winter 2021
This course provides the foundational knowledge necessary to begin to address theoretical, cultural and practical issues associated with the Information Revolution.