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Aleksandr Michuda

Address: New York, NY Email:
Github: Personal Website:

Summary

Economist with a passion for causal inference done rigorously and in conjunction with machine learning predictions and data science techniques. Adaptable and able to work with team members with different perspectives. Experience with presenting technical results to non-technical audiences.

Education

2020 PhD Agricultural and Resource Economics, UC Davis
2014 B.A./M.A. Economics CUNY Hunter College summa cum laude
2014 B.A. Philosophy CUNY Hunter College summa cum laude

Technical Skills

  • Python, R, Github, Bash, Stata
  • Causal Inference, Machine Learning Classification, Applied Econometrics

Languages and Skills

English: Fluent

Russian: Fluent

Ukrainian: Conversational

Experience

Swarthmore College - Visiting Assistant Professor - Department of Economics

  • August 2024 - Present
  • Teach economics, econometrics and data science to undergraduate students
  • Research on the entrance of ride-share platforms in developing countries
  • Engage with corporate partners through data user agreements (DUAs) to conduct analysis with restricted data

Cornell University - Assistant Research Professor - Center for Data Science for Enterprise and Society

  • August 2021 - August 2024
  • Research on the entrance of ride-share platforms in developing countries
  • Engage with corporate partners through data user agreements (DUAs) to conduct analysis with restricted data
  • Collaborate with faculty from Dyson Business School and the Information School

Uber - Data Science Intern - Economics and Pricing

  • September 2019 - December 2019
  • Worked on causal inference in business facing team
  • Evaluated policies using regression discontinuity design, difference-in-differences and treatment effect estimation
  • Generated spatial and dynamic visualizations of driver behavior using Python and SQL

BITSS Catalyst- Berkeley Initiative for Transparency in the Social Sciences

  • July 2017 - Present
  • Organized workshops that teach reproducibility and transparency in social sciences.
  • Taught anonymization of data as well as replication techniques.
  • Taught Jupyter Notebooks portion of dynamic documents (R Markdown, Jupyter Notebooks, Stata Markdown)

Research Assistant (Optimal Nutritional Interventions across Space and Time)

  • Advisor: Stephen Vosti
  • Responsible for developing a Python package that finds the optimal set of nutritional interventions across space and time
    • Using 24hr recall or household surveys
  • Estimated optimal interventions of effective coverage and lives saved in Cameroon
  • Developed dashboards for HKI (Hellen Keller International) to visualize Vitamin A intake in Kenya

Research Assistant (Disease Suppressive Crop Rotations)

  • Advisor: Rachael E. Goodhue July 2016 - September 2019
  • Responsible for data management and cleaning
  • Regression and ANOVA analysis using Stata and Jupyter Notebooks
  • Calibrating dynamic contract models in Python

Selected Works

Tennant, Elizabeth; Aleksandr Michuda, Joanna B. Upton, Andres Chamorro, Michael L. Mann, David Newhouse, Christopher B. Barrett. “Nowcasting shocks to human capital.” (2024)

Tabetando, Rayner, Djomo Choumbou Raoul Fani, Catherine Ragasa, and Aleksandr Michuda. “Land market responses to weather shocks: evidence from rural Uganda and Kenya.” European Review of Agricultural Economics 50, no. 3 (2023): 954-977.

Michuda, Aleksandr. “Urban Labor Supply Responses to Weather Shocks for Ugandan Uber Drivers.”

Bird, Samuel S.; Michuda, Aleksandr. “Ethnicity and voting at scale: Evidence from Uganda.” (2023) Submitted

Tjernström, Emilia, Dalia Ghanem, Oscar Barriga Cabanillas, Travis J. Lybbert, Jeffrey D. Michler, and Aleksandr Michuda. “A Group Random Coefficient Approach to Modeling Heterogeneous Returns to Technology Adoption.”

Gupta, Anubhab, Heng Zhu, Miki Khanh Doan, Aleksandr Michuda, and Binoy Majumder. “Economic Impacts of the COVID-19 Lockdown in a Remittance‐dependent Region.” American Journal of Agricultural Economics.

Cabanillas, Oscar Barriga, Jeffrey D. Michler, Aleksandr Michuda, and Emilia Tjernström. “Fitting and interpreting correlated random-coefficient models using Stata.” Stata Journal 18, no. 1 (2018): 159-173.

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