Teaching

I typically teach “Machine Learning for Public Policy Lab” and Machine Learning in Practice at Carnegie Mellon University designed to provide students training and experience in solving real-world problems using machine learning, with a focus on problems from public policy and social good. I co-taught “Designing Better Human-AI Futures“, a first-year seminar course for the College of Humanities and Social Sciences in 2022.

• I’ve created and taught Machine Learning for Public Policy courses at the University of Chicago from 2015-2019 as well as the Machine Learning & Public Policy Lab in Winter 2017 and a Data Analytics for Campaigns class in Winter 2015.

Co-Created and Teaching (with Julia Lane and Frauke Kreuter) Applied Data Analytics for Policy courses for Government Agencies since 2015.

I frequently do talks, workshops, and trainings for organizations on the following topics:

• Ethics, Fairness, Bias, and Equity in AI and Machine Learning: Hands-on tutorial on Dealing with Bias and Fairness in ML Systems  
• Fair and Equitable Decisions-Making using AI
• The Future of Machine Learning/AI/Data Science
• How to Scope Actionable Goal-Driven Data Science Projects
• The Value of Data-Driven Decision-Making for Governments and Non-Profits