The Minds
Behind the Science
A PhD-led collective of researchers, mathematicians, and engineers dedicated to the science of causal discovery.

Prof. Dr. Martin Spindler
Director & Founder
Martin Spindler is a Professor of Data Science, Statistics & Econometrics at the University of Hamburg and the founder of Economic AI™. Specialising 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.

Dr. Sven Klaassen
Head of Software Development
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.

Dr. Philipp Bach
Head of Trainings & Executive Education
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, M.Sc.
Data Scientist
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, M.Sc.
Data Scientist
Lucas Moreira Gomes is a Data Scientist at Economic AI™. His work is primarily centred 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 behavioural challenges in modern data environments.
Join the Frontier
of Causal Discovery
We are regularly looking for highly qualified interns, working students (Bachelor & Master), and PhD candidates to join our research-driven industry projects.
“Mastery is the transition from predicting what happens to understanding why it must.”
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