David M. Kohl Chair and Professor at Virginia Tech. Director of the Kohl Centre and the Data Science for the Public Good Program. Endowed chair, center director, and institution-builder with twenty years of research, teaching, and academic leadership at R1 universities.
Data science with impact and with purpose — economics operating at the intersection of technical innovation and human systems.
Le Wang is the David M. Kohl Chair and Professor at Virginia Tech, where he directs the Kohl Centre — an interdisciplinary hub he revitalized from the ground up — and the Data Science for the Public Good Program. His guiding vision, "data science with impact and with purpose," brings together rigorous economic analysis, modern machine learning methods, and experiential student learning, all oriented toward real-world impact.
His research spans labor economics, education, health, inequality, and intergenerational mobility, with particular methodological depth in distributional analysis, causal inference, and causal machine learning. He has published 44 peer-reviewed articles in leading outlets including the Journal of Political Economy, Journal of Econometrics, Journal of Business & Economic Statistics, and Journal of Applied Econometrics, alongside interdisciplinary work in natural language processing and early childhood development. His scholarship has been recognized with the Kuznets Prize, the Emerald Liberati Award for Outstanding Author Contribution, and the VPR Award for Excellence in Transdisciplinary Research.
A dedicated educator, Professor Wang has won multiple Outstanding Professor Awards and a Presidential Professorship. He designed one of the nation's early 4+1 programs integrating machine learning and causal inference into applied economics at the University of Oklahoma and has built award-nominated experiential learning ecosystems at Virginia Tech. He currently teaches Ph.D.-level causal inference and has mentored more than 30 doctoral and master's students placed across academia, government, and industry.
Beyond research and teaching, he serves as Co-Editor of the China Economic Review and the Journal of Labor Research, Associate Editor of Econometric Reviews, and a member of the Board of Trustees of the Southern Economic Association. He is a Research Fellow of the IZA Institute, the Global Labor Organization, and the HCEO Global Working Group at the University of Chicago.
Dr. Wang's research spans applied microeconomics and econometrics, with substantive focus on income distribution, intergenerational mobility, labor markets, education, health, and public policy. Methodologically, he works at the intersection of distributional analysis, causal inference, and modern machine learning. Dr. Wang has published 44 peer-reviewed articles in outlets including the Journal of Political Economy, Journal of Econometrics, Journal of Business & Economic Statistics, and Journal of Applied Econometrics.
Transformed the Kohl Centre from a defunct entity into a comprehensive interdisciplinary hub integrating research, teaching, and external partnerships. Launched, led, and coordinated interdisciplinary research initiatives that elevate academic excellence across campus. Launched multiple experiential learning programs. Manages multiple foundation accounts, and sponsored research funding. Built strategic partnerships with government agencies, industry, and community stakeholders.
Directs a USDA funded flagship experiential learning program that has supported 30 student fellowships, connecting students with public agencies, nonprofits, and industry partners for project-based learning. Programs nominated for the University Exemplary Program Award and Diggs Teaching Scholar Program.
Designed and launched one of the nation's early programs integrating machine learning and causal inference into applied economics. Oversaw all aspects from curriculum development and admissions to research advising and job placement.
Grounded in transparency, shared governance, and careful stewardship. Views academic leadership as relying on persuasion rather than command, trust rather than hierarchy.
Professor Wang teaches at every level — from undergraduate principles to Ph.D. econometrics — with a consistent emphasis on integrating rigorous theory with modern quantitative and data analytical tools and real-world application. His teaching evaluations include perfect scores at both undergraduate and graduate levels.
As an advisor, he has chaired or co-chaired more than 30 Ph.D dissertations and theses. His former students hold positions at institutions including the Federal Housing Finance Agency, the World Bank, Amazon, TransUnion, and numerous universities across the U.S. and abroad.
Chosen by graduating MA class at UNH (2009, 2011, 2013)
University of Oklahoma — pedagogy & curriculum innovation
Pioneered 4+1 ML + causal inference economics program
Advanced treatment of causal inference methods for doctoral students, covering potential outcomes, instrumental variables, regression discontinuity, difference-in-differences, synthetic control, and modern causal machine learning approaches.
Applied statistics course using R for data science, covering data wrangling, visualization, statistical modeling, and decision-making frameworks for management and policy contexts.
Integrated course combining predictive analytics, machine learning algorithms, and modern causal inference methods with hands-on R programming for applied economists.
Core Ph.D. econometrics sequence covering probability, statistical inference, and the linear and nonlinear regression models with applications.
Consistently outstanding evaluations including perfect scores at both undergraduate and graduate levels across four institutions.
Applied tutorials in R and Quarto covering econometric methods, data science tools, and reproducible research workflows — built from coursework and research practice.
I welcome inquiries about research collaboration, speaking engagements, and academic leadership opportunities.
Full academic CV with complete publication list, grants, advising record, and service.
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