Vitaly Meursault | Home

Vitaly Maria Meursault, PhD

Hi, I'm Vitaly

I'm a Machine Learning Economist at the Philly Fed

I also teach AI in Business at CMU Tepper

Reach me at vitaly.meursault@phil.frb.org

Disclaimer: This is my personal website. The views expressed here are my own and do not necessarily represent the views of the Federal Reserve Bank of Philadelphia or the Federal Reserve System.

My academic CV →
Personal photo

My work is at the intersection of Machine Learning (ML) and Economics. Two things I'm obsessed about are:

  • Natural Language Processing (NLP) in Economics (especially, how to ensure NLP measures are good)
  • Fair in ML for consumer finance (especially, how to fit together ML with fair lending policy with minimal adjustments to either)

I think that interdisciplinary research is very cool.

I studied History at school and Japanese on my own. I worked as a tour guide in beautiful St. Petersburg, and an interpreter at a car factory. Then I went to grad school for Financial Economics and picked up some Machine Learning skills. I'm into art history and love spending time with my cats. Translation is creation - I find joy in connecting ideas from different parts of life, making them click in new ways. I appreciate Ursula Le Guin's characters who take the long way home, experiencing new worlds along their way. "It is good to have an end to journey toward; but it is the journey that matters, in the end."

Recent Papers

All papers

  • From Diverse Table Scans to Cohesive Datasets with LLMs

    From Diverse Table Scans to Cohesive Datasets with LLMs

    Multimodal LLMs do surprisingly well at processing scans of historical tables almost from start to finish. They especially shine when we need to combine tables from various sources with varying table headers for similar concepts, like car ownership. LLMs are not the "best" method for any given case, but they make creating cohesive historical datasets much easier.

    Read paper →

    Authors:

    Ina Ganguli, Jeffrey Lin, Vitaly Meursault, Nicholas Reynolds

    Status:

    Slides

    Updated:

    Aug 2024

  • Patent Text and Long-Run Innovation Dynamics: The Critical Role of Model Selection

    Patent Text and Long-Run Innovation Dynamics: The Critical Role of Model Selection

    Text-based measures in economic research can be highly sensitive to model choice, potentially leading to contradictory conclusions. We demonstrate that domain-specific validation for model selection is critical for reliable analysis of technological change and innovation dynamics. As NLP models become increasingly powerful and accessible to economists, we can and should spend more time on selection and validation.

    Read paper →

    Authors:

    Ina Ganguli, Jeffrey Lin, Vitaly Meursault, Nicholas Reynolds

    Status:

    Working paper coming soon (click "Read paper" for abstract)

    Updated:

    Aug 2024

  • One Threshold Doesn’t Fit All: Tailoring Machine Learning Predictions of Consumer Default for Lower-Income Areas

    One Threshold Doesn’t Fit All: Tailoring Machine Learning Predictions of Consumer Default for Lower-Income Areas

    Machine learning (ML) models can help increase access to credit in lower-income areas if their introduction is paired with "fairness constraints," which are conceptually similar to the familiar Special Purpose Credit Programs (SPCP). Doing this at scale would require rethinking the protected attribute blindness requirements of the policy.

    Read paper →

    Authors:

    Vitaly Meursault, Daniel Moulton, Larry Santucci, Nathan Schor

    Status:

    Accepted at JPAM

    Updated:

    May 2024