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Professor of Computer Science at University of Bonn / Fraunhofer IAIS / Lamarr Institute for ML an AI
I am a full professorfor Intelligent Learning Systems at the CS department of the University of Bonn, lead scientist for Machine Learning at Fraunhofer IAIS, and one of the directors of the Lamarr Institute for ML and AI. Having 20+ years of industrial and academic experience with theory and practice of data science, machine learning, and artificial intelligence, I am now primarily interested in hybrid learning systems, informed machine learning, and the prospects of quantum computing for combinatorial optimization, artificial intelligence, and machine learning.
Bielefeld University
PhD, computer science
January 1, 1998 – January 1, 2002
INRIA Grenoble - Rhones Alpes,
computer science
January 1, 1995 – January 1, 1996
Bielefeld University
MSc, computer science, physics
January 1, 1992 – January 1, 1997
Lamarr Institute
Co Director
July 1, 2022 – Present
Bonn, North Rhine-Westphalia, Germany
The University of Bonn
Professor for Computer Science / Intelligent Learning Systems
October 1, 2008 – Present
Cologne Bonn Region
Fraunhofer IAIS
Lead Scientist Machine Learning
October 1, 2008 – Present
Cologne Bonn Region
Deutsche Telekom Laboratories
Senior Research Scientist
December 1, 2005 – October 1, 2008
York University
Postdoctoral Researcher
May 1, 2004 – December 1, 2005
University of Bielefeld
Postdoctoral Researcher
June 1, 2002 – May 1, 2004
Cultural Fit Analysis
The candidate's background is heavily academic and research-oriented, with significant experience in universities and research institutes. While this demonstrates a strong intellectual curiosity and a drive for innovation, the direct cultural fit for a fast-paced, industry-focused Big Data Engineer role is not immediately clear. The absence of specific industry projects or contributions outside of research and consulting roles makes it difficult to assess alignment with typical corporate development cultures. The target role 'Big Data Engineer' requires a strong focus on implementation, scalability, and production systems, which is not explicitly highlighted in the provided experience.
Soft Skills & Operational Fit
The candidate's extensive experience in research, teaching, and project management suggests strong analytical, problem-solving, and leadership skills. Their roles as a professor and lead scientist indicate an ability to mentor, guide teams, and manage complex initiatives. However, the provided data does not offer specific insights into their day-to-day operational fit within a corporate Big Data engineering team, particularly regarding agile methodologies or direct collaboration with product teams.