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Last updated May 2026

Basics

Education

  • 2022 - Present

    Madison, WI

    PhD
    University of Wisconsin-Madison
    Joint Economics & Finance
  • 2022 - 2024

    Madison, WI

    MS
    University of Wisconsin-Madison
    Economics
  • 2021 - 2022

    Austin, TX

    MS
    University of Texas at Austin
    Data Science
  • 2014 - 2018

    Toronto, ON

    BA Hons.
    University of Toronto
    Economics

Work

  • 2024 - Present
    Research Assistant to Prof. Jean-Francois Houde
    University of Wisconsin-Madison
  • 2022 - Present
    External Academic Consultant
    Bank of Canada
  • 2021 - 2022
    Analyst
    Bank of Canada
  • 2018 - 2021
    Research Assistant
    Bank of Canada

Awards

  • 2026
    Young Scholars Best Paper Award
    Georgia Tech–Atlanta Fed Household Finance Conference
  • 2025
    Best Third Year Finance Research Paper Scholarship
    University of Wisconsin-Madison
  • 2024, 2025
    Juli Plant Grainger Summer Research Fellowship
    University of Wisconsin-Madison
  • 2022 - 2026
    Doctoral Fellowship
    Social Sciences and Humanities Research Council
  • 2020
    Women in Economics Scholarship
    Bank of Canada
  • 2018
    Best Undergraduate Paper
    Canadian Economics Association
  • 2017
    President's Scholar Program Scholarship
    University of Toronto

Publications

  • 2026
    Adverse Selection and Learning in Consumer Credit Market
    Working Paper
    This paper highlights a trade-off in credit markets between regulatory safeguards for informed consent and the informational frictions they can amplify. In our empirical setting, we find that requiring lenders to garner explicit consent prior to raising clients' credit limits induces adverse selection. We find disproportionately higher take-up among riskier borrowers, as measured by increased utilization, delinquency, and charge-offs, which worsens the risk profile of accounts that receive a credit limit increase. In response to the policy, we find that lenders decreased the size of credit limit increases, yet simultaneously gave more frequent limit increases. We develop a model of lender credit limit provision to study the role of adverse selection and learning. We show that learning from acceptance decisions can rationalize lenders' increased frequency of credit limit increases, while adverse selection can rationalize the decline in the size of credit limit increases.

Skills

Coding
Python
Julia
Stata
Matlab
R
HTML/CSS
Computing
Azure
AWS
Slurm-based HPCs
Data
Web-scraping
Modeling
Simulation

Languages

English
Native
Mandarin
Native
French
Fluent
German
Beginner