Biography
Leading and proposing research projects on using AI/Machine Learning to address challenges across the spectrum of Airbus’ business units and activities by providing input to the development of future (n+2) products and processes. I’m interested in tackling new challenges, also going beyond my comfort zone.
I have experience in coordinating and leading international research activities, both inside a corporate structure and in a collaborative environment, often contributing to strategic planning activities. In addition, I have in-depth knowledge in AI/Machine Learning, explorative data analysis and image and video processing algorithms, with a focus on visual information processing and graph-like structures.
Besides my day job at Airbus, I’m also contributing to the education of the next generation of researchers as a part-time lecturer for Applied Machine Intelligence/Deep Learning for Multimedia at Technische Universität München (TUM).
Working at TUM’s Institute for Data Processing , I received a Dr.-Ing. (~PhD) degree in electrical engineering and information technology for my thesis on the application of multi-way (tensor) data analysis/machine learning methods in the context of QoE prediction models.
- Artificial Intelligence
- Machine Learning
- Visual Information Processing
- Computer Vision
- Machine Perception
Dr.-Ing. (PhD) in electrical engineering and information technology, 2014
Technische Universität München (TUM)
Experience
Most recent experience, more here
Airbus AI Research - Part of Central Research & Technology (CRT)
- Leading and proposing research projects on using AI to address challenges across the spectrum of Airbus’ business units for future products and improving of industrial processe
- Research on data- and non-data-driven AI concepts/algorithms for aerospace and defence related applications, focusing on visual information processing and graph-like structures
- Collaborating and coordinating with AI research groups and institutes outside Airbus
Applied Machine Intelligence / Deep Learning for Multimedia (part-time) (since 2019)
- Methods, algorithms, and underlying concepts for using deep learning to extract information from audio, visual, and textual content with an introduction to relevant frameworks
Quality of Experience (QoE) (2017-2018)
- Concepts and applications of QoE: algorithms/assessment methodologies with a focus on audio-visual stimuli in multimedia systems/services and on the use of machine learning methods in this context
Digital broadcast engineering (2015-2016)
- Covering both traditional linear digital broadcasting technology i.e. DVB, non-linear broadcasting via (adaptive) streaming e.g. DASH and interactive/Smart TV
R&D area Data and Security (since 03/2016)
- Proposing, starting, and leading a new research stream on machine learning (AI) at IRT
- Research on data-driven AI models for audio-visual content under-standing and applications of deep learning in broadcasting/streaming and video processing
- Leading development activities on audio-visual (social media) content analysis in editorial use, SmartTV-based user engagement assessment, and scalable second-screen framework
- Evaluation and benchmarking of cognitive services for use in public service media and of synchronisation delays in companion-screen primary-screen interaction
- Data science and AI consulting for public service media in D-A-CH (up to C-level)
- Contributing to strategic planning at European level for use of AI/Big Data in the (public service) media sector via EBU working groups and in EC context
Research
Topics - Current and past
Teaching
Current and past courses
Publications
Book
