Assistant Professor of Quantitative Marketing
HEC Paris
HEC Paris
I am an Assistant Professor in the Marketing Department at HEC Paris and a Chairholder at the Hi!PARIS Center on Data Analytics and Artificial Intelligence for Science, Business and Society. I joined from Goethe University in Frankfurt in 2021.
My research interests meet at the interface between empirical quantitative marketing, mangement economics, and information systems - specificially, my research focuses on online advertising and privacy issues in the digital economy. Methodically, my research is based on quantitative empirical modeling, applied econometrics, distributed statistical computing, causal machine learning, as well as large-scale field- and lab-experiments.
During my Ph.D., as a post-doctoral scholar and afterward, I have been a frequent visiting scholar at the Wharton School of the University of Pennsylvania and the Graduate School of Business at Stanford University. My research has been published in top-tier academic journals such as the Journal of Marketing Research, and the International Journal of Research in Marketing, as well as management-oriented journals. In my research projects, I often collaborate with the industry to answer research questions at scale.
I frequently review for leading academic journals such as Nature, Management Science, Marketing Science, and Information Systems Research.
In 2017, I was nominated as a Junior Faculty Consortium Fellow at the International Conference on Information Systems (ICIS) and in 2022, I was nomiated as a ISMS Early-Career Scholar. From 2019-2025, I am a co-investigator on an European Research Council (ERC) Advanced Grant studying the impact of Online Tracking Restrictions on the Online Advertising Market.
Alongside my mentee Yufei Shen, I was honored with the Enginius Doctoral Dissertation Award (2nd Place) at EMAC 2024.
Quantitative and Data-driven Methods in Marketing
Customer Lifetime Value-Based Customer Acquisition
Demand Estimation
Pricing and Willingness-to-Pay
Advertising Effectiveness
Data Privacy and Regulation
Empirical Modelling
Applied Econometrics
Distributed Statistical Computing
Causal Machine Learning
Large-scale Field- and Lab-Experiments
Statistical Computing: R, Python, Stata
Cloud Computing: Amazon Web Services (AWS), Amazon Simple Storage (S3), Amazone Elastic Map Reduce (EMR), Amazon Elastic Cloud (EC2), Amazon Redshift
Big Data Analysis: Apache Hadoop, Apache Spark
Web Analysis: Google Analytics, Google Tag Manager, Adobe Analytics, Adobe Tag Manager
Online Experimentation: Adobe Test & Target, Optimzely
Languages: English | German | French