After completing my studies in Information and Multimedia Technology in Erlangen, I started my Ph.D. in 2009 at the Institute of Multimedia Communication and Signal Processing. In 2015, I transitioned to a role as a software project manager, focusing on the series development of a smart front-camera system for highly automated driving. By 2022, I moved to Berlin to build up the AI Lab, which nowserves as a competence center for artificial intelligence.

What drives me to work in such a dynamic and fast-paced field like AI at AUMOVIO is the chance to learn and grow every single day. The field offers deep cross-domain insights, with highly motivated people working at the forefront of technology. My role at the lab is all about connecting people and building a vision, ensuring that we organize and guide efforts to bring AI into the heart of new products and services.

AI has already revolutionized the automotive industry with more efficient processes, better optimized products, and advanced comfort and safety functions for vehicles. Its future potential is immense, from the possibility of maintaining independent mobility for aging people to the seamless use of AI technologies from anywhere. Here in Berlin, our lab is becoming the hub connecting research worldwide and predevelopment inside our company. It’s a place where we cultivate talent and innovation, aiming to supply the best of the best to AUMOVIO’s future.

My background is in AI and Natural Language Processing (NLP). I have previously worked as an expert and application architect, successfully delivering large-scale AI solutions across multiple industries, including banking, insurance, retail and healthcare.

At AUMOVIO, I lead multiple projects focused on Generative AI and Large Language Models (LLMs). These projects align with AUMOVIO’s strategy of becoming an “AI-empowered Company” and fostering “AI-empowered Employees.” The former is achieved by automating tasks related to product development and improving the quality and reliability of product testing. For the latter, I leverage the latest advances in LLMs and agentic workflows to enable AUMOVIO employees to quickly find the right information and streamline complex workflows.

Although AUMOVIO is not primarily a NLP company, I chose to work here because I can apply my expertise in interdisciplinary settings, combining it with the company’s know-how in mobility product development. At AUMOVIO, I feel that I am at the forefront of transforming modern mobility through AI. Additionally, I have the opportunity to expand my knowledge in other cutting edge AI topics, such as autonomous driving and explainable AI, by collaborating with the company’s experts in these areas.

I’ve always been motivated by the challenge of applying AI and software solutions to real-world problems, particularly in the fast-evolving field of autonomous driving. At AUMOVIO, I am able to work at the forefront of AI innovations that are shaping the future of mobility.

In projects like “just better DATA”, we are developing AI-based methods for smart data loops, aimed at enhancing data collection and processing at the edge. This initiative reduces the need for large data volumes, providing high-quality training data for AI models and improving autonomous driving systems.

In my role, I contribute by optimizing data workflows and integrating AI-driven solutions into the development of new products and services at AUMOVIO. AI is already making a huge impact in safety, efficiency and driver assistance systems, and its future potential lies in enabling fully autonomous vehicles and smart traffic ecosystems.

Being part of AUMOVIO’s dynamic AI development efforts, particularly in this location, allows me to help advance cutting-edge technologies that will define the future of mobility.

I am responsible for developing intelligent interiors for autonomous vehicles as part of PPP projects such as KARLI and Stadt:UP. Through this research, our team is helping AUMOVIO create intelligent and sophisticated in-cabin HMI consoles that can lead us to become the industry leader in smart displays.

Being a part of the AI domain offers me the opportunity to contribute to technological breakthroughs that can change industries, society and everyday life. Autonomous mobility, in particular, is at the forefront of innovation and has had a significant impact on the automotive sector through advanced driving assistance systems (ADAS), in-car assistants and fully autonomous vehicles.

Working at the chair of Human-Centered Technologies for Learning allows me to focus on developing interactive in-cabin HMI systems that are both innovative and user-friendly. I am grateful for the chance to work with this team and contribute to changing the world for the better.

In collaboration with TU Berlin, Germany, I am currently pursuing my PhD while being part of TU Berlin’s Intelligent Multi-Robot Coordination Lab. After obtaining my Master's degree, specializing in advanced control of mobile robots, I worked as a teaching and research assistant for two years.     

My research interests lie at the intersection of advanced control theory and modern machine learning technologies. I am particularly interested in addressing the problem of intelligent robot navigation in dynamic and uncertain environments, with a focus on improving performance and safety. I am grateful for the opportunity to work at AUMOVIO and contribute to developing innovative solutions to real-world challenges.

My tasks focus on the future of autonomous mobility, particularly on designing and regularizing probabilistic and generative deep learning models for dynamic environment prediction and planning.

This research helps AUMOVIO to unlock new capabilities, for example, by infusing probabilistic prediction models with domain knowledge through the KI WISSEN project, or generating interesting evaluation scenarios using generative models within the NXTAIM project.

AI is a rapidly-evolving research field with immense potential, not just for autonomous mobility but for a range of robotic applications. With the AUMOVIO AI Lab, we are able to discover this potential and drive impactful innovation across industries.

I studied Computer Science at TU Berlin, Germany, specializing in Cognitive Science, for both my bachelor's and master's degrees. Currently, I am pursuing my PhD, which focuses on further research in local-to-global explainable AI (xAI) in the computer vision domain, and bringing xAI approaches to our internal projects. My PhD is collocated with the cognitive science group at the University of Bamberg, Germany.     

As part of my research, I am primarily focused on enhancing the interpretability of AI models. My work involves generating explanations for a Deep Neural Network trained on histopathological data, with an emphasis on improving its interpretability. At AUMOVIO, I aim to elevate and fine-tune local xAI techniques and provide xAI knowledge to internal stakeholders working with diverse multimodal datasets. This includes aiding in model enhancement and debugging processes by employing both local and global approaches to provide a comprehensive understanding of model-internal processes.     

At the AI Lab in Berlin, I am part of a dynamic and innovative team. Working at a company that prioritizes AI research makes it possible for me to further improve my skills and experience in xAI and other related fields.    

My focus is on developing perception systems for complex environments, especially when handling high-dimensional or unreliable data sources.

I believe that AI and machine learning represent the next step in our ongoing efforts to automate repetitive tasks. With carefully designed trainable models built on a deep understanding of individual subproblems, we can automate even higher-level functions that are not sufficiently represented by a small set of equations.

Working at AUMOVIO’s AI Lab has been a great opportunity to explore this fascinating field and make a significant impact with my work. I am excited to continue pushing the boundaries of perception systems and delivering innovations that will make a meaningful difference in a wide range of industries.

I am currently working on spatio-temporal models. Prior to joining the research team in Berlin, I was part of the team working on AUMOVIO's Autonomous Mobile Robot Corriere.

My academic background includes a master's degree in Artificial Intelligence from Radboud University in Nijmegen, Netherlands, as well as a research master focusing on Computational Neuroscience at the Donders Institute. My research revolves around building temporally and spatially consistent 3D and 4D models using recent advancements in representation learning, 3D deep learning and generative models.

At present, I am focused on addressing the following questions to drive innovation in the automotive industry:

• How do we lift limited sensory inputs into a rich 3D representation effectively?
• What is the best way to predict the temporal evolution of a scene over time?
• How can we improve data availability for autonomous driving applications?

My research focuses on developing plausibility methods for Deep Neural Network(DNN)-based automotive perception systems that operate in real-time.

The opportunity to apply cutting-edge AI research to real-world automotive challenges motivates me. AUMOVIO’s innovative environment allows me to contribute to meaningful solutions while pursuing my academic interests in AI plausibility.

My research enhances the reliability of AI systems in AUMOVIO’s products by developing methods to assess and improve AI model outputs, making our systems more robust and explainable for safety-critical automotive applications.

AI has improved driver assistance systems, predictive maintenance and manufacturing efficiency. Future potentials include fully autonomous driving and advanced traffic management.  

Building on my Engineering background from the Karlsruhe Institute of Technology (KIT) , Germany and through diverse roles at international research facilities and companies, I gained valuable practical experience in AI and autonomous driving. I am convinced that this field will have an even bigger impact in the near future.

At AUMOVIO, my research focuses on motion planning and control for autonomous vehicles under complex road traffic conditions. My work explores how autonomous systems can anticipate and adapt to the uncertain behavior of other traffic participants. By integrating learning-based methods with model-based approaches, I develop uncertainty-aware and interaction-aware motion planning techniques that enhance both safety and comfort in autonomous driving.

The AUMOVIO AI Lab is a key hub for AI innovation, where I collaborate with talented researchers on projects that drive advancements in intelligent mobility and contribute to safer, more efficient automotive systems.    

My research focuses on designing safe compressed neural networks specifically tailored for memory-constrained devices, with a particular emphasis on safety-critical applications such as autonomous vehicles.

Through my work as part of the PPP project CeCaS (Central Car Server), I am actively contributing to advancing Safe AI functionality. This involves the development of both hardware and software architectures as well as validated software solutions for future high-performance computing (HPC) products at AUMOVIO.

AUMOVIO's AI Lab in Berlin is at the forefront of AI innovation, driving cutting-edge algorithms and solutions that seamlessly integrate into AUMOVIO’s products. I believe that the true potential of AI lies in making it more transparent, human-centric and safe, ensuring integrity and trust in the rapidly-evolving AI landscape.  

I completed my master's degree in Mechanical Engineering with a specialization in Automotive Engineering at Karlsruhe Institute of Technology (KIT) in Karlsruhe, Germany. Following that, I worked as an algorithm developerfor L4 autonomous driving functions.

Now, I am pursuing my PhD at the Freie Universität Berlin's Dahlem Center for Machine Learning and Robotics in Berlin. My research interests center around the crucial aspect of predicting future trajectories of traffic participants, which are an essential input to the motion planning systems of autonomous agents. My primary focus is on developing approaches for combining learning-based and model-based methods to build more robust and efficient long-term traffic scene prediction models.

I am excited to contribute to AUMOVIO's AI-empowered products and services day by day, using my research and development experience garnered over the years.