About Me

I am currently a tenure track Assistant Professor (W1) of Econometrics and Statistics at the Institute of Finance and Statistics at the Department of Economics at the University of Bonn.

More broadly, I’m a data scientist with a PhD in econometrics/statistics, and expertise in causal inference, machine learning, and modern statistical computing. I did my PhD at Universitat Pompeu Fabra, Barcelona.

Research

Research interests: I am interested in developing practical statistical methods with a focus on unobserved heterogeneity.

Why should we care? Unobserved heterogeneity is pervasive in economics and business:

  • Heterogeneous treatment effects and other parameters
  • Explanatory variables not captured by data
  • Unobserved “fundamental” characteristics of agents
  • Measurement error

Ignoring this heterogeneity in non-experimental settings may lead to crippling bias and invalid inference.

Accordingly, I focus on developing methods that are robust to such unobserved factors and can be used in challenging observational data settings. See the research page page for my work.

Teaching

I teach data science at both undergraduate and graduate levels. My teaching approach emphasizes modern, hands-on learning and good theoretical understanding using open-source tools, reproducible workflows, and accessible explanations. Full course materials, including lecture notes, code, and exercises, are available openly on my GitHub and the teaching page.

Shortcuts to some recent classes with full materials:

Fundamentals of Monte Carlo Simulations in Data Science

Fundamentals of Monte Carlo Simulations in Data Science

Description: Learning the practice of Monte Carlo simulations for evaluating causal, ML, and inference methods.
Access Materials GitHub

Advanced Econometrics (Econometrics II)

Course: Advanced Econometrics (Econometrics II)

Description: Going beyond basics: more theory; causal inference and machine learning basics; more contexts. Empirical examples in Python.
Access Materials GitHub

Econometrics with Unobserved Heterogeneity

Course: Econometrics with Unobserved Heterogeneity

Description: A course on methods for estimating parameters of interest in settings with unobserved heterogeneity. Topics include linear models with heterogeneous coefficients, nonparametric models with unobserved heterogeneity, and quantile and distribution regression.
Access Materials GitHub

Blog

I occasionally blog about topics in data science, econometrics, and programming. Posts range from technical walkthroughs to small curiosities I encounter in my work.

Check out the most recent posts: