Formal Research Experience
Research Assistant to Professor Michael Peters, UBC
(Jan-Apr 2021 via Econ 306, Formal May-Sept 2021)
Econ 492e 002 Directed Study
(Sep-Dec 2021, supervised by Prof. Peters)
Data-Driven Classification of Economics Departments
Classified economics departments using a modification of the Stochastic Block Model applied to placements outcomes of Ph.D. graduates via databases on econjobmarket.org.
https://support.econjobmarket.org/git_page/econjobmarket%2Bapi_documentation/mapinator/mapinator.md
Co-organized the second annual VSE Workathon with volunteers from across UBC to expand the placement dataset while raising funds for anti-discrimination causes.
https://w4s-2021.github.io/
Interviewed by econjobmarket.org CEO Prof. Joel Watson on my algorithm.
https://econjobmarket.org/videos
Upgraded the Mapinator dashboard with my work.
sage.microeconomics.ca
https://support.econjobmarket.org/git_page/econjobmarket%2Bapi_documentation/mapinator/mapinator.md
Co-organized the second annual VSE Workathon with volunteers from across UBC to expand the placement dataset while raising funds for anti-discrimination causes.
https://w4s-2021.github.io/
Interviewed by econjobmarket.org CEO Prof. Joel Watson on my algorithm.
https://econjobmarket.org/videos
Upgraded the Mapinator dashboard with my work.
sage.microeconomics.ca
The Mapinator Classification of Ph.D. Economics Departments
Developed during 492e directed study alongside VSE micro theory professors; presented research updates during the study.
Used the data-driven department classification to build a numerical estimator of payoffs in a directed search model over the econjobmarket.org network.
https://github.com/michaelpetersubc/Econ49n/blob/main/sbm/assortative_matching_revised/Dec3_slides.pdf
Peters, M. & Yu, J. (2021). The Mapinator Classification of Economics Departments. Retrieved December 13, 2021, from
https://montoya.econ.ubc.ca/papers/markets/markets.pdf
Used the data-driven department classification to build a numerical estimator of payoffs in a directed search model over the econjobmarket.org network.
https://github.com/michaelpetersubc/Econ49n/blob/main/sbm/assortative_matching_revised/Dec3_slides.pdf
Peters, M. & Yu, J. (2021). The Mapinator Classification of Economics Departments. Retrieved December 13, 2021, from
https://montoya.econ.ubc.ca/papers/markets/markets.pdf
Hungarian Algorithm
Created various implementations of the Hungarian Matching Algorithm for bipartite graphs for use by students in Econ 306 and Econ 514 at UBC.
https://github.com/jbrightuniverse/notebooks/tree/main/hungarian_algorithm
https://pypi.org/project/hungarianalg/
Proof that at least one column potential must be zero (with Alex Dong and Michael Peters):
Peters, M. (n.d.). Economic Applications of the Hungarian Algorithm. https://montoya.econ.ubc.ca/Econ514/hungarian.pdf
https://github.com/jbrightuniverse/notebooks/tree/main/hungarian_algorithm
https://pypi.org/project/hungarianalg/
Proof that at least one column potential must be zero (with Alex Dong and Michael Peters):
Peters, M. (n.d.). Economic Applications of the Hungarian Algorithm. https://montoya.econ.ubc.ca/Econ514/hungarian.pdf
Deferred Acceptance Model
Created a Python notebook on the Deferred Acceptance matching algorithm for students in future honours and M.A. cohorts to study.
Designed an interactive UI allowing students to interactively step through each component of the algorithm.
Designed a framework that allows students to input their own data into the algorithm using text files.
https://github.com/jbrightuniverse/VisualDeferredAcceptance/blob/main/deferred_acceptance.ipynb
Designed an interactive UI allowing students to interactively step through each component of the algorithm.
Designed a framework that allows students to input their own data into the algorithm using text files.
https://github.com/jbrightuniverse/VisualDeferredAcceptance/blob/main/deferred_acceptance.ipynb
Research Assistant to Professor Wei Li, UBC
(June 2021-Present)
Wrote a series of Python notebooks to solve Nash equilibria of complex strategic networks of simulated interacting agents numerically and symbolically. Created graphical visualizations of optimal strategies in the networks.
Edited, adjusted, and solved recursive Bellman equations for the model.
Edited, adjusted, and solved recursive Bellman equations for the model.
Research Assistant to Professor Jesse Perla, UBC
(June 2021-Present)
Quantitative Economics with Julia
Maintained a new set of notebooks on applying the Julia programming language to macroeconomic theory.
Rewrote code, edited content for clarity, assisted new contributors and upgraded software.
Released notebooks to the public, which were used in the Econ 602 Ph.D. macro theory course at the VSE.
Modified lectures in February 2022 to operate with Julia 1.7.1 release.
https://github.com/QuantEcon/lecture-julia.myst
https://julia.quantecon.org
Rewrote code, edited content for clarity, assisted new contributors and upgraded software.
Released notebooks to the public, which were used in the Econ 602 Ph.D. macro theory course at the VSE.
Modified lectures in February 2022 to operate with Julia 1.7.1 release.
https://github.com/QuantEcon/lecture-julia.myst
https://julia.quantecon.org
Econ Layers
Created and published an open-source Python package of tools for economic applications of deep neural networks.
Designed elements for use in solving high-dimensional asset pricing and growth models.
Created test suites and utilities for software in the package.
https://github.com/HighDimensionalEconLab/econ_layers
Designed elements for use in solving high-dimensional asset pricing and growth models.
Created test suites and utilities for software in the package.
https://github.com/HighDimensionalEconLab/econ_layers
High Dimensional Econ Lab
Worked alongside several teams of academic collaborators and VSE Ph.D. students.
Extensively simplified and tested code for ease of use via structures from Econ Layers.
Identified and supported bugfixes in new releases of the "jsonargparse" Python library.
Identified and supported bugfixes in "Grid.Ai" cloud computing system.
Tested and deployed a flexible command-line interface for configuring neural networks.
e.g. https://github.com/HighDimensionalEconLab/symmetry_dynamic_programming
Extensively simplified and tested code for ease of use via structures from Econ Layers.
Identified and supported bugfixes in new releases of the "jsonargparse" Python library.
Identified and supported bugfixes in "Grid.Ai" cloud computing system.
Tested and deployed a flexible command-line interface for configuring neural networks.
e.g. https://github.com/HighDimensionalEconLab/symmetry_dynamic_programming
Personal Research Projects
Commuter Schedule Optimization: a Utility-Oriented Approach
September-December 2021, Econ 490
A counterfactual harnessing real-time bus arrival data to minimize local commuter waiting times using a theoretical model of the disutility of waiting given a strategy rule for how early to get to bus stops. Interactive dashboard: https://yubot.jphoton.repl.co
Competitive McCall Search
March 2021
McCall job search model using the Hungarian Algorithm as a representation of wage distributions for competing agents.
https://github.com/jbrightuniverse/CompetitiveMcCallSearch/blob/main/CompetitiveMcCallSearch.ipynb
https://github.com/jbrightuniverse/CompetitiveMcCallSearch/blob/main/CompetitiveMcCallSearch.ipynb
Generalized Price of Anarchy
January 2021
High-dimensional extensions of the classic Price of Anarchy problem.
https://github.com/jbrightuniverse/Generalized-Price-of-Anarchy/blob/main/Generalized%20Price%20of%20Anarchy.ipynb
https://github.com/jbrightuniverse/Generalized-Price-of-Anarchy/blob/main/Generalized%20Price%20of%20Anarchy.ipynb
2D Geometric Model of N-Dimensional Lotteries in Python
November 2020
Program to visualize high-dimensional economic lotteries and solve their utility-based statistics using extensions of theories from Econ 304 at UBCV.
https://github.com/jbrightuniverse/ExpectedUtilityVisualizer/blob/master/Geometric_Lotteries.pdf
https://github.com/jbrightuniverse/ExpectedUtilityVisualizer/blob/master/Geometric_Lotteries.pdf