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Dr.-Ing. | Visual and Spatial Learning Research Lead @ FZI | KIT | UC Berkeley | uOttawa
Research Scientist and Group Leader in visual and spatial Learning for autonomous systems. My work centers on scalable training data generation and cross-sensor domain adaptation - bridging HD maps, LiDAR, and camera perception to train robust real-world models under limited annotation. I have led research groups, principal-investigated six-figure industrial grants, published across NeurIPS, CVPR, IROS, RAL, T-ITS and T-IV, and deployed perception systems on autonomous research vehicles across multiple European Cities.
University of California, Berkeley
Doctor of Philosophy - PhD Exchange, Robotics & Physical AI
August 1, 2022 – October 1, 2022
HECTOR School of Engineering & Management
MBA Fundamentals Program , Business Administration and Management
May 1, 2020 – December 1, 2022
Karlsruhe Institute of Technology (KIT)
Doctor of Philosophy - PhD (Dr.-Ing.), Autonomous Driving
January 1, 2020 – January 1, 2026
University of Ottawa
Master of Science, Exchange, Computer Science
January 1, 2015 – January 1, 2016
Karlsruhe Institute of Technology (KIT)
Master of Science (M.Sc.), Electrical Engineering and Information Technology
January 1, 2014 – January 1, 2018
Karlsruhe Institute of Technology (KIT)
Bachelor of Science (B.Sc.), Electrical Engineering and Information Technology
January 1, 2011 – January 1, 2014
FZI Research Center for Information Technology
Research Scientist and Group Leader (Visual and Spatial Learning)
November 1, 2025 – Present
University of California, Berkeley
Visiting Scholar (Robotics)
August 1, 2022 – October 1, 2022
FZI Research Center for Information Technology
Research Scientist and PhD Candidate (Machine Learning & Computer Vision)
May 1, 2019 – October 1, 2025
atlatec GmbH (acquired by Bosch in 2022)
Software Engineer (Deep Learning & Computer Vision)
October 1, 2018 – April 1, 2019
Karlsruhe Area, Germany
Mercedes-Benz AG
Master Thesis (Deep Learning & Computer Vision)
November 1, 2017 – August 1, 2018
Region Stuttgart
FZI Forschungszentrum Informatik
Student Assistant ( 3D-Mapping & Object Classification)
May 1, 2016 – October 1, 2017
Karlsruhe und Umgebung
University of Ottawa
Research Project (Indoor 3D-Scene Understanding)
January 1, 2016 – April 1, 2016
University of Ottawa
Project Course (Vehicle2X-Communication)
January 1, 2016 – April 1, 2016
Mercedes-Benz Research & Development North America, Inc.
Research Intern (Autonomous Driving)
February 1, 2015 – July 1, 2015
Sunnyvale, California
Daimler AG
Working Student (Object Tracking and Sensor Fusion)
November 1, 2014 – January 1, 2015
Daimler AG
Bachelor Thesis (Object Tracking and Sensor Fusion)
April 1, 2014 – October 1, 2014
FZI Forschungszentrum Informatik
Student Assistant (Robotics)
May 1, 2013 – October 1, 2013
Institute of Micro- und Nanoelectronic Systems, KIT
Student Assistant (Sensor circuits)
July 1, 2012 – May 1, 2013
Cultural Fit Analysis
The candidate's background is heavily rooted in academic research and specialized autonomous driving domains. While this provides deep technical expertise, the breadth of experience outside of this niche, particularly in diverse industry settings or product-focused ML engineering, appears limited. The long tenure in research roles might indicate a preference for exploratory work over rapid iteration and deployment cycles common in many industry ML roles. The candidate's education and experience at prestigious institutions suggest a fit for high-performance, research-driven environments.
Soft Skills & Operational Fit
The candidate's experience as a Research Scientist and Group Leader, coupled with project leadership roles, suggests strong problem-solving, critical thinking, and collaboration skills. Their academic pursuits at top-tier institutions (KIT, UC Berkeley) indicate a high degree of intellectual curiosity and a structured approach to complex challenges. The descriptions of leading industrial research projects imply an ability to manage operational aspects and deliver results within a research context.