Summary
Jesper Wulff is a Full Professor of Economics and Business Economics at Aarhus University specializing in applied econometrics, statistical methodology, and business analytics, with 11+ years of academic experience. He develops techniques for statistical and causal inference applied to organizational science and regularly teaches advanced machine learning topics, including deep neural networks and generative AI with LLMs. His work appears in top journals across management, finance, and public administration, and he serves as Associate Editor and Method Advisor for The Leadership Quarterly. Jesper combines rigorous econometric training (PhD, Aarhus) with cross-disciplinary data science credentials (University of Essex, Rice, UW), enabling pragmatic bridges between theory, ML tools, and real-world organizational problems. He has been recognized with multiple best paper awards, the Award for Responsible Research in Management, and a university teaching award, reflecting both research impact and pedagogy. Fluent in German and experienced in international placements and policy work, he brings a rare mix of technical depth, communication skills, and practical stakeholder engagement.
11 years of coding experience
13 years of employment as a software developer
Certificate, Data Science, Certificate, Data Science at University of Washington
Master's Degree, Social Science Data Analysis, Master's Degree, Social Science Data Analysis at University of Essex
Visiting student, Political Science and Government, Visiting student, Political Science and Government at Freie Universität Berlin
Specialization Certificate, Principles of Computing, Specialization Certificate, Principles of Computing at Rice University
Doctor of Philosophy (Ph.D.), Economics, Applied Econometrics, Doctor of Philosophy (Ph.D.), Economics, Applied Econometrics at Aarhus Universitet
Visiting Scholar, Department of Global Business and Trade, Visiting Scholar, Department of Global Business and Trade at Wirtschaftsuniversität Wien
Danish, German, English