Richeng (Ryan) Piao

Teaching

Microeconomic Theory, Macroeconomics, Game Theory & Information Economics, Industrial Organization, Econometrics, Applied Statistics, Applied Data Analytics, and Statistical & Machine Learning for Economics.

Research

Industrial organization and platform economics; algorithmic pricing and antitrust; causal inference and machine learning; the labor-market and pedagogical effects of generative AI.

• Northeastern University

  August 2021

  Ph.D. in Economics — Industrial Organization

• University at Albany, State University of New York

  May 2012

  B.S. in Economics (Honors Program) and B.S. in Mathematics

• Director of AI Teaching Innovation

  August 2026 – June 2027

  College of Social Sciences and Humanities, Northeastern University

  • College-level appointment under Northeastern's AI Curricular Transformation and Readiness initiative (AICTR, formerly the Curricular Transformation Initiative), co-sponsored by the Offices of the Provost and Chancellor, to build faculty AI capacity across CSSH.
  • Convene and lead a faculty community-of-practice cohort that experiments with AI-aware teaching, exchanges discipline-specific practice, and evaluates the effectiveness of redesigned activities and assessments.
  • Design and deliver workshops and collegial programming that meet faculty across the novice–expert spectrum, grounded in the university AI Readiness Framework and guidance from the Task Force on AI and Assessment.
  • Built and maintain the CSSH “AI in Teaching” faculty resource hub: plain-language policy guidance, copyable syllabus statements, an AI-resilient assignment toolkit, and a gallery of interactive classroom examples.
  • Co-convene the collegewide AI showcase with the Director of AI Curricular Transformation, featuring faculty work and employer perspectives on AI in the workplace.

• Assistant Teaching Professor of Economics

  2024 – Present

  Department of Economics, College of Social Sciences and Humanities

Department of Economics, Northeastern University, Boston, MA

• Statistical & Machine Learning for Economics (ECON 3916)

  Spring 2026 – Present
  • Built a unified curriculum bridging classical econometrics and data science through a “Concept Extension” pedagogy that scales statistical foundations to high-dimensional machine learning.
  • Covered causal machine learning (Double ML, causal forests), high-dimensional regression, NLP for economists, and predictive modeling with pandas, scikit-learn, and EconML.
  • Delivered dual-modal instruction pairing theory seminars with computational laboratories in Google Colab.

• Applied Data Analytics in Economics (ECON 5200)

  Spring 2025 – Present
  • Developed the “Foundations First, Expansion Second” curriculum, which sequences manual Python coding (OLS and DiD derivation) before generative-AI tooling is introduced.
  • Designed the P.R.I.M.E. Prompting Framework, positioning students as junior analysts who use AI to scale analysis while retaining rigorous economic verification.
  • Integrated experiential learning with real-world BLS and FRED data in Python and Jupyter.

• Game Theory (ECON 1916)

  Fall 2025 – Present
  • New course preparation: designed a full curriculum on strategic decision-making, Nash equilibrium, dynamic games, and asymmetric information.
  • Built interactive in-class simulations and case studies connecting formal models to real strategic settings.

• Microeconomic Theory (ECON 2316)

  2023 – Present
  • Taught calculus-based intermediate theory: utility maximization, demand, cost minimization, equilibrium, and strategic decision-making.
  • Examined market imperfections — public goods, externalities, asymmetric information, imperfect competition — with emphasis on policy implications.

• Information Economics & Game Theory (ECON 4681)

  2020 – Present
  • Designed an applied curriculum on strategic interaction across economics, politics, and business.
  • Facilitated in-class games and case analysis covering Nash equilibria, evolutionary game theory, repeated interaction, and asymmetric information.

• Principles of Microeconomics (ECON 1116)

  2019 – Present
  • Active-learning lectures on supply and demand, elasticity, market structure, and externalities using current events.
  • Mini-projects including price-elasticity estimation from CPI and CES data; fieldwork assessing market competitiveness in real retail settings.

• Principles of Macroeconomics (ECON 1115)

  2017 – Present
  • Lectures on GDP, inflation, unemployment, and business cycles grounded in historical and contemporary crises.
  • Group projects using World Bank indicators, synthesized across groups in class; post-pandemic monetary policy including the IORB rate.

• Applied Econometrics (ECON 2560)

  Summer 2023

• Applied Statistics for Economics (ECON 2560)

  Fall 2022

• Money and Banking (ECON 3442)

  2016 – 2017

• Principles of Microeconomics — Interactive Open Textbook

  ECON 1116 · 2025 – Present
  • Complete draft of a 19-chapter, ~202,000-word interactive principles textbook with manipulable economic visualizations in place of static figures, written to replace a commercial textbook.
  • Every chapter ships as a unit: draft, podcast, auto-graded homework, quiz bank, interactive charts, in-class attendance activities, and lecture deck. All 19 chapters now have decks.

• Microeconomic Theory — Calculus-Based Text

  ECON 2316 · 21 chapters, rebuilt 2026
  • Rebuilt around a 37-meeting Fall 2026 course map that fixes scope against actual contact hours, with per-chapter cut lists so scheduling decisions are made in advance rather than improvised.
  • Includes an audio edition, a TA plan, and explicit protection for department-required topics (Cournot, Stackelberg, Bertrand, uncertainty and information asymmetry).

• Data Science & Statistical Learning for Economists

  ECON 3916 / 5200 · dual-track
  • One text serving an undergraduate and a graduate course: shared foundations through measurement, sampling, estimation, and inference, then track-specific chapters.
  • 3916 continues to SQL and multivariate OLS prediction; 5200 to difference-in-differences, instrumental variables, and regression discontinuity, before both converge on bias–variance, regularization, and classification.

• Assessment & Course Automation Infrastructure

  2025 – Present
  • Built a reproducible generator for auto-graded homework and quiz banks across five item types with LaTeX math support, covering every chapter of the principles sequence.
  • Developed an API-driven Canvas LMS deployment pipeline that builds complete course modules — gated prerequisites, weighted gradebook structure, quiz imports, consistent styling — programmatically rather than by hand.

• Lecture, Animation & Media Pipelines

  2025 – Present
  • Built a reveal.js lecture-viewer application with presenter mode, dual-monitor sync, pacing indicators, and WCAG-compliant themes, replacing slide-deck software across every course.
  • Authored in-class attendance activities and a podcast for every chapter, with live in-lecture polling wired to a datastore so class responses render on the slide.
  • Produce Manim-based economics animations and a pipeline that converts textbook chapters into teaching videos.

• CSSH AI in Teaching Resource Hub

  2026
  • Designed and published a faculty-facing hub translating university AI policy into plain language, with copyable syllabus statements, AI-resilient assignment redesign patterns, a disclosure template, and a gallery of live interactive classroom examples.

Working Paper

• Algorithmic Pricing and Market Conduct: Evidence from Airbnb Smart Pricing

  2026

  Uses a sharp regression discontinuity around Airbnb's September 2023 platform-wide rollout of redesigned pricing tools across eight major U.S. cities. Price-level point estimates are positive in all eight cities (0.5–1.9 percent), but inference with only eight city clusters cannot rule out zero, bounding the effect between −0.5 and +1.9 percent. Heterogeneity tracks local regulatory stringency.

Research in Progress

• Platform API Pricing and Open-Source Software Ecosystems

  How do platform API pricing changes affect the open-source software built on them?

• Antitrust & Algorithmic Pricing Consultation

  2025

  Patrick Malone & Associates, P.C.

  • Advised legal practitioners on antitrust claims involving Airbnb and algorithmic pricing software.
  • Provided expert input on econometric identification (RDD and fuzzy RDD) and structural demand estimation to quantify consumer-welfare effects in digital platform markets.

• Director of AI Teaching Innovation, College of Social Sciences and Humanities

  2026 – 2027
  • Lead faculty AI capacity-building across CSSH under Northeastern's AI Curricular Transformation and Readiness initiative; convene the faculty community-of-practice cohort and co-convene the collegewide AI showcase (full description under Academic Appointments).

• Chair, Diversity, Equity, Inclusion & Belonging (DEIB) Committee

  2025

  Department of Economics, Northeastern University

  • Led the department's first DEIB climate survey assessing faculty and student belonging.
  • Launched the “XChange Lunch” series to build cross-cultural mentorship and community.

• Member, CSSH AI Readiness Committee

  2025 – Present
  • Contribute to college-level strategy on AI pedagogy and curriculum adaptation across CSSH.

• Member, Economics Graduate Admissions Committee

  2025 – Present
  • Review graduate applications to identify and recruit high-potential candidates.

• Faculty Advisor & Research Mentor

  2025 – Present
  • Directed Studies supervisor for undergraduate research projects (Spring and Summer 2025); faculty member of the Economics Graduate Students Association.

Languages & Tools

Python (pandas, NumPy, scikit-learn, statsmodels, EconML, Matplotlib), R (ggplot2, caret, glmnet), SQL, Stata, Jupyter, Git/GitHub, LaTeX, Tableau, AWS.

Methods

Structural demand estimation (BLP), difference-in-differences and event study, regression discontinuity (sharp and fuzzy), instrumental variables, panel methods, causal machine learning (Double ML, causal forests), high-dimensional regression, clustering, NLP.

Teaching Technology

Canvas LMS and its REST API, H5P interactive content, QTI assessment authoring, reveal.js, Next.js/React, Manim, Remotion, PollEverywhere, Canva.

Languages

Native in Chinese and Korean; fluent in English.

• Google Data Analytics Professional Certificate2025
• IBM Data Science Professional Certificate2021
• Ph.D. Fellowship, Northeastern University2012 – 2021
• Gordon Karp Award for outstanding achievement in Economics, University at Albany2012
• Dean's List, University at Albany2009 – 2012

• AI Curricular Transformation and Readiness (AICTR) Director Kickoff Retreat, Northeastern University — September 2026.
• CATLR: “Navigating Uncertainty in Higher Education” and “Teaching in Uncertain Times” — 2025.
• ADVANCE Office of Faculty Development: Faculty Fellowship Inspiration Session — 2025.