Northeastern University
Richeng (Ryan) Piao · Assistant Teaching Professor of Economics · Director of AI Teaching Innovation, College of Social Sciences and Humanities
I study how algorithms reshape markets and how universities should respond. My research applies industrial organization and causal machine learning to platform pricing, antitrust exposure, and the labor-market effects of generative AI. My teaching builds the same rigor into the classroom: students earn their foundations by hand before they are allowed to scale with AI.
A college-level appointment under Northeastern's Curricular Transformation Initiative, supported by the Offices of the Provost and Chancellor. The charge is to build faculty AI capacity across the College of Social Sciences and Humanities — cultivating a community-of-practice cohort, designing programming that meets faculty wherever they are on the novice–expert spectrum, and documenting innovative classroom practice as exemplars that travel across disciplines.
Convening a faculty community-of-practice cohort, running workshops across the novice–expert spectrum, and documenting redesigned assignments as shareable exemplars. Appointed for AY 2026–27 under Northeastern's Curricular Transformation Initiative.
How I frame this work →Microeconomic Theory across two sections, plus Statistical & Machine Learning for Economics — both running on books written this year rather than purchased.
Courses and materials →Combining unsupervised learning on host behavior with fuzzy regression discontinuity to ask whether pricing algorithms raise rents — or simply make sophisticated sellers far more sophisticated.
Working papers →Three of my courses now run on books I wrote rather than books I assigned. The motivation was not novelty: it was that a commercial principles text costs a first-year student more than the course materials are worth to them, and that no existing book lets a student drag a supply curve and watch welfare change.
A full interactive textbook written to replace the commercial book my students were paying for. Figures are manipulable rather than static: move a curve and watch surplus, deadweight loss, and tax incidence respond. Every chapter ships with a podcast, an auto-graded homework bank, in-class activities, and a lecture deck.
The calculus-based intermediate text, rebuilt this year around a 37-meeting course map that sets scope up front instead of discovering it in December. Includes an audio edition and a chapter-by-chapter cut list so the schedule survives contact with a real semester.
One text serving two audiences. Shared foundations through estimation and inference, then the tracks split: undergraduates go toward SQL and predictive modeling, graduate students toward identification, instrumental variables, and regression discontinuity.
College Leadership
Building faculty capacity for AI-aware teaching across CSSH — moving courses from AI literacy to demonstrated AI readiness without surrendering disciplinary judgment.
Read more →Causal Inference
Algorithmic pricing and antitrust risk, platform governance shocks, and the labor-market footprint of generative AI — on reproducible, publicly auditable pipelines.
Read more →Applied Economics
Long-form analysis of how major technology firms actually use economics in operations: auction design, price discrimination, two-sided matching, platform market design.
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