Martin Spindler is a Professor of Data Science, Statistics & Econometrics at the University of Hamburg and the founder of Economic AI. Specializing in the theory and application of Machine Learning and AI — especially Causal Machine Learning — he holds degrees from the University of Regensburg and University of Munich, where he also earned his PhD. A regular visiting scholar at MIT, Martin established Economic AI to help businesses harness state-of-the-art solutions for a competitive edge.

Sven Klaassen is the Head of Software Development at Economic AI. He obtained a Master in Business Mathematics and a Ph.D. in Economics from the University of Hamburg. In 2022 Sven was a visiting scholar at MIT and has been working actively in research on the combination of Machine Learning and Causal Inference. Currently, he is maintaining and extending the open-source package DoubleML.

Philipp Bach is Head of Trainings & Executive Education and is passionate about teaching basic and advanced topics of Causal ML. He currently holds the position of a post-doctoral researcher at the University of Hamburg. His research focuses on implementations and applications of cutting-edge approaches of Causal ML.

Jan Rabenseifner is a Data Scientist at Economic AI. He is currently pursuing his Ph.D. in Statistics at the University of Hamburg. His research interests lie in the fields of Causal Inference, Forecast Evaluation in high-dimensional settings, Deep Learning, and Machine Learning.

Lucas Moreira Gomes is a Data Scientist at Economic AI. His work is primarily centered on Graph Neural Networks (GNNs), Collusion Detection, and Large Language Models (LLMs). He focuses on leveraging geometric deep learning and natural language processing to solve complex structural and behavioral challenges in modern data environments.

Our training programs are led by a rotating collective of specialized tutors, selected for each course based on their specific domain expertise. This dynamic approach ensures that every workshop benefits from our team's combined depth of knowledge, fostering a collaborative learning environment where theoretical rigor meets practical application.