Nabarun Deb

Nabarun Deb is an Assistant Professor of Econometrics and Statistics at the University of Chicago Booth School of Business. His research focuses on nonparametric statistical methods, optimal transport theory, and dependent data analysis including network and Ising models. Formerly, he was the inaugural PIMS Kantorovich Initiative Postdoctoral Fellow at the University of British Columbia (2022-2023) and completed his PhD in Statistics at Columbia University (2017-2022).

• Education:
  • PhD in Statistics, Columbia University (2022)
  • Master of Statistics, Indian Statistical Institute, Kolkata (2017)
  • Bachelor of Statistics, Indian Statistical Institute, Kolkata (2015)
• Research:
  • Specializes in statistical machine learning with applications to optimal transport, kernel methods, and network models.
  • Develops methodologies for causal inference and nonparametric testing under complex dependence structures.
  • Active in theoretical advances for high-dimensional Bayesian regression and measure transportation.
• Publications:
  • Published in top venues including NeurIPS, JMLR, Annals of Applied Probability, JASA, and Bernoulli.
  • Core research themes: optimal transport applications, network data fluctuations, and generative modeling via PDEs.
  • Recent work explores covariate shifts on approximate manifolds and empirical process trade-offs in nonparametric learning.
• Awards:
  • PIMS Kantorovich Initiative Postdoctoral Fellowship (2022-2023) at University of British Columbia.
• Teaching:
  • Teaches Business Statistics and Econometrics and Statistics Colloquium at Booth.
  • Course schedule spans 2024-2026 quarters with emphasis on applied statistical methods.