Pushing research and product in
- AI Automation
- Human-AI Interaction
- AI Applications
- and Decision Optimization,
Daniel typically delivers on time and in budget using methods of
- Information Visualization
- Human-Computer Interaction
- Cloud Engineering
- Machine Learning
- Data Science
- Network Science
- Computer Graphics, and
- Natural Language Processing
while rigorously enforcing
- DevOps/Continuous Integration
- and the Agile Kanban method.
He also publishes results in scientific communities, files patents and crafts prototypes for business or academic conferences.
Daniel holds a B.Sc. and M.Sc. in Information Engineering from the University of Konstanz, Germany for delivery of theses on
- Visual Pattern Mining in Geographic Information Systems, and
- Social-aware Matrix Factorization Methods in Recommender Systems (think Netflix meets Facebook).
Prior to joining the academic research group of Prof. Dr. Ulrik Brandes as a PhD candidate in 2013, Daniel was managing director and tech lead at a software company where he tailored complex front- and backend solutions to various clients in automotive, telecommunication and retail industries in Germany and Switzerland. During this time, he doubled revenue and constantly cleared successfully on all deliveries with his staff. He has been invited to visit IBM Research, Cambridge, USA as an Intern in July 2015 and shortly after joined the tribe for good in January 2016.
Daniel currently operates in role of a Senior Research Software Engineer in the Visual AI Lab, leading with code and clarity to succeed in global research challenge collaborations with immediate business impact and C-Level and/or public visibility.