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I’m an applied economist with a PhD from Georgia Tech and an MS in Geographic Information Science. My work sits at the intersection of economics and spatial analysis: I study questions where geography changes the answer, and where getting the analysis right requires building measures that don’t exist off the shelf.

Past projects have included assessing regional labor market readiness for semiconductor manufacturing investment across 380+ U.S. metro areas, constructing satellite-derived nightlights measures to improve bilateral trade flow predictions across 174 countries, and estimating how COVID-19 disrupted domestic trade through Colombian trucking networks using a spatial autoregressive model. I’m seeking full-time positions in applied economics research, workforce policy, and quantitative analysis at policy research organizations, federal agencies, and research consulting firms.

Research Interests: Spatial econometrics, regional labor markets, workforce policy, occupational mobility, international trade, measurement design, GIS-integrated economic analysis.

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Career Overview

I grew up in St. Louis, MO, originally from Kyiv, Ukraine. I completed a BS in Economics from Arizona State University in 2014, where I minored in Mathematics and German and earned an International Business Certificate. I then earned an MSc in Economics and Finance from the Barcelona School of Economics at Universitat Pompeu Fabra before beginning doctoral studies at Georgia Tech in 2017, where I completed an MS in Economics (2019), an MS in Geographic Information Science and Technology (2022), and a PhD in Economics (2024). My dissertation, “Spatial Dimensions of Economic Modeling: Interdisciplinary Approaches to Labor, Trade, and Networks,” covered all three areas of my current research.

As a Research Analyst at Carnegie Mellon University’s Block Center, I co-authored “Build It and They Will Come?,” an empirical study of regional workforce readiness for CHIPS Act investment. That project required defining what skill compatibility between occupations actually means before any estimation could begin, which led to the occupational requirement sufficiency measure: a directional skill-matching metric that quantifies whether workers in one occupation meet the requirements of another, accounting for asymmetric mobility barriers and wage structures. I also built the Workforce Insights Tool in RShiny so regional stakeholders could run their own scenario analyses on local labor supply.

Prior to that, I contributed to an NSF-funded multi-university project on critical technology assessment, where I developed the analytical framework for a national workforce supply identification model drawing on BLS, BEA, and Census data organized by NAICS and SOC classifications across all U.S. MSAs. I use R, Stata, Python (Pandas, ArcPy), SQL, ArcGIS Pro, and QGIS in my research, and am proficient in English and Russian.