Fangzhu Yang

Fangzhu Yang

Ph.D. Candidate in Economics

Johns Hopkins University


Welcome to my homepage! I am a Ph.D student in the Department of Economics at Johns Hopkins University. My research interests are labor economics, family and gender economics, and adopting machine learning methods to empirical applied-microeconomic topics. I will join Bates White Economic Consulting as an Economist in Fall 2024.


  • Labor Economics
  • Economics of Family and Gender
  • Machine Learning


  • Ph.D. in Economics, 2018 - 2024

    Johns Hopkins University

  • M.S.E in Applied Math and Stats, 2016 - 2018

    Johns Hopkins University

  • B.A. in Economics and Mathematics, 2014 - 2016

    Brandeis University

  • B.A. in Economics and Mathematics, 2012 - 2013

    Shanghai University of Finance and Economics

Working Papers

Product Design Using Generative Adversarial Network: Incorporating Consumer Preference and External Data

The development of Generative AI enables large-scale automation of product design. However, this automated process usually does not incorporate consumer preference information from a company’s internal dataset. Meanwhile, external sources such as social media and user-generated content (UGC) websites often contain rich product design and consumer preference information, but such information is not utilized by companies in design generation. We propose a semi-supervised deep generative framework that integrates consumer preferences and external data into product design, allowing companies to generate consumer-preferred designs in a cost-effective and scalable way. We train a predictor model to learn consumer preferences and use predicted popularity levels as additional input labels to guide the training of a Continuous Conditional Generative Adversarial Network (CcGAN). The CcGAN can be instructed to generate new designs of a certain popularity level, enabling companies to efficiently create consumer-preferred designs and save resources by avoiding developing and testing unpopular designs. The framework also incorporates existing product designs and consumer preference information from external sources, which is particularly helpful for small or start-up companies who have limited internal data and face the “cold-start” problem. We apply the proposed framework to a real business setting by helping a large self-aided photography chain in China design new photo templates. We show that our proposed model performs well in generating appealing template designs for the company.

Work in Progress

How Do Racial Filters in Online Dating Apps Change Racial Homogamy?


Economics of Poverty/Inequality

Spring 2023

Gender Economics

Fall 2022, Spring 2022

Economic Development in Sub-Saharan Africa

Fall 2022


Fall 2021

Market Design, Economics of Matching

Spring 2021

Elements of Microeconomics

Spring 2020

Microeconomic Theory

Fall 2019, 2020