Introduction of AI professors

1. For which teaching area are you appointed?
Cyberphysical systems

2. What is your background and how did you come to work with AI?

I studied information technology with a focus on high frequency engineering in Mannheim and in France. After graduation, I was active in medical device research and development for more than 15 years. At senetics healthcare group, an innovative service company in the medical device industry, I was active in European research projects that focused on the use of AI in networked mobile systems. Among other things, this involved AI-assisted evaluation of various biosignals for measuring blood glucose.

3. What lectures do you currently give and what do students learn in them?

• Cyberphysische Systems: Applications, design and modeling of cyberphysical systems using the example.
• Project management: methods for agile project management
• Artificial Intelligence: building robots and using algorithms to control them
• Webtechnologies & Technical Language: Design of web pages and relevant technical terms and their meaning
• Computer Science: building computers, designing programs, excerpts from theoretical computer science

4. What is the importance of AI in this area?
AI is a technology in cyber-physical systems. Robots can become autonomous
and also learn. It is also important to know different AI techniques, which have to be selected and used adapted to the particular application. In my teaching, students apply easy-to-understand AI methods and integrate them into a system context.

5. How do you assess the career opportunities for students in our AI degree programs?

I rate the career opportunities as very good. In industry, AI appears in all
sectors and almost all classically trained engineers, e.g., in electrical engineering or
mechanical engineering, are overtaxed here. Applying AI methods is not difficult in itself. However, designing the right methods efficiently and integrating them profitably into existing software is very complex. This is becoming increasingly clear in companies.

6. In addition to teaching, you are also active in research. In which research area(s) do you specialize?

I do research in the field of medical technology, mainly the use of robots or the networked data acquisition and analysis. I am also interested in smart city applications, e.g. the creation of a digital twin of a city and the use of OpenData to optimize the daily processes of a city.

7. What fascinates you about these topics?
The complexity and the possibilities. Since everything interlocks and has to function, it is very complex to design a system within a smart factory or a smart city alone. Technical challenges, different disciplines, legal requirements and open or defined interfaces are necessary for a smart city to function, for example. This is where many disciplines of engineering come together. However, if a team or project is successful, the possibilities at this point, especially in conjunction with AI, are fascinating.

8. How has AI impacted this area(s) of research and what role does it play?
AI plays the same role today as microcontrollers used to. They suddenly appeared in every machine and took over the control of a plant, a car, or a coffee machine. AI is influencing technology in very similar ways, but in addition, it is also influencing pure software systems. AI adds learning and predictive capabilities to the software on PCs and microcontrollers within these areas. In a smart factory, this means that vehicles can drive on their own and do not need to be controlled. In a smart city, software can predict the likelihood of gridlock or a natural disaster based on historical and current measurement data from hundreds of sensors.

9. What types of AI are you involved with?
Since I always consider AI in a system context and rarely study or interpret it on its own, finite state machines or decision tree methods are mainly used in teaching. These have the advantage that they do not depend on extensive measurement data. Thus they can be designed and tested quickly. In addition, I deal with the use of simple neural networks trained with machine learning.

10. Which specific use cases of AI do you find most impressive?
Autonomous driving and intelligent robots in various scenarios.

1. For which teaching area are you appointed?
Media Informatics.

2. What is your background and how did you come to work with AI?

I have a degree in Communication Design as well as a Master's in Computer Science and a PhD in Computer Science. For a few years I worked as a media designer, software developer and as an independent entrepreneur. Since 2008 I am professor at the Ostbayerische Technische Hochschule Amberg-Weiden and for some time now I am head of the study program for media production and media technology. In the context of industrial projects, I have worked my way into topics of artificial intelligence. I also teach in the bachelor's program in Artificial Intelligence.

3. What lectures do you currently give and what do students learn in them?

I hold the subjects information visualization for the media computer scientists, web engineering as well as media art for the masters media technology and media production. I also hold foundation courses (Programming and Web Technology) for students in the international AI bachelor's program.

4. What is the importance of AI in this area?
Programming basics are the prerequisite for understanding Artificial Intelligence methods. Information visualization is important for understanding the data used to train AI algorithms. In Computer Arts, this year's theme is Artificial Intelligence as well as Artificial Life.

5. How do you assess the career opportunities for students in our AI degree programs?

Very good, of course; AI is one of the mega future trends. Due to the mathematical skills required, the course is very demanding and the number of new students is correspondingly low, so there is no need to worry about finding a job.

6. In addition to teaching, you are also active in research. In which research area(s) do you specialize?

Information visualization, data science, machine learning, human-machine interaction, usability and accessibility, interaction design, web technologies, and programming languages for media and computer art.

7. What fascinates you about these topics?
I am interested in the application and possibilities of AI in the media field.

8. How has AI impacted this area(s) of research and what role does it play?
In terms of data analytics, a lot has become possible. Personalized applications with voice interaction and image recognition are not possible without AI.

9. What types of AI are you involved with?
With machine learning in different variations.

10. Which specific use cases of AI do you find most impressive?
Generative algorithms.

11. Is there anything else you'd like to say that you haven't had a chance to do yet?
It is also important to use AI responsibly. Personal assistants should not dictate what you have to do. People need to use AI as a meaningful tool and not let AI patronize them.

1. For which teaching area are you appointed?
Media Informatics and Mobile Computing

2. What is your background and how did you come to work with AI?

I studied computer science at Saarland University, and already in my 2nd semester I started working as a research assistant at the then freshly founded German Research Center for Artificial Intelligence (DFKI). So I have been "doing" AI for more than 32 years. After my diploma, I worked for Dokumenta S.A. (Luxembourg) as an application developer and consultant for almost five years. Afterwards, I worked for 14 years at DFKI Saarbrücken as a senior software engineer and researcher in the research area of language technology. In 2007, I received my PhD on hybrid architectures for speech technology components and was subsequently project manager of BMBF-funded research projects, among others, for many more years. Since the winter semester of 2014, I have been a professor at OTH Amberg-Weiden, and since 2021 I have been setting up the Innovation and Competence Center for Artificial Intelligence at OTH Amberg-Weiden.

3. What lectures do you currently give and what do students learn in them?

Because of my role as dean, I am currently focusing on my core lectures Mobile & Ubiquitous Computing, App Programming, Natural Language Processing and about every 2nd semester also Physical Computing (together with colleagues Martin Frey and Gerald Pirkl). Besides language technology, my topics are the programming of mobile applications on microcontrollers, wearables and smartphones/tablets, often in combination with sensor technology, displays, and of course the various communication technologies like WLAN, Bluetooth, NFC and GNSS ("GPS") play a major role. Because I am not limited to smartphones, the buzzword "Internet of Things" (IoT) with protocols like MQTT often fits as well.

4. What is the importance of AI in this area?
On the one hand, the sensor technology of mobile devices and wearables provides data for machine learning (either on the mobile devices themselves or in the "edge" or "cloud"); on the other hand, mobile devices such as smartphones are also becoming intelligent themselves through AI processes. Speech dialog systems, automatic question answering, chatbots or text summarization and extraction procedures are another focus. Interaction with spoken language in particular is gaining in importance in connection with mobile applications.

5. How do you assess the career opportunities for students in our AI degree programs?

Excellent, especially because they are excellently supervised and receive practical training on the latest hardware equipment.

6. In addition to teaching, you are also active in research. In which research area(s) do you specialize?

Natural Language Processing, i.e. the (intelligent) processing of spoken and written language.

7. What fascinates you about these topics?
Always new approaches and methods as well as the possibility to combine them in so-called hybrid approaches to more and more powerful applications. Ultimately, this is how symbolic and neural network-based methods can be combined to get the best out of both core worlds of AI - logic and learning. What is equally fascinating to me is that modern methods learn for themselves from large amounts of data, namely digitally available texts, without humans still having to annotate. However, building meaningful applications with them requires deep engineering and machine learning expertise.

8. How has AI impacted this area(s) of research and what role does it play?
The ability to understand and produce human language is commonly understood as the mark of intelligence, and accordingly Natural Language Processing is on the one hand an important area within AI, and on the other hand NLP itself is one of the earliest applications of AI and has in turn fertilized AI as a whole with formal foundations and methods of logic and knowledge representation. Even some of the most complex deep learning approaches, the transformers, belong to the field of Natural Language Processing, but sometimes already achieve general intelligence features that go beyond purely linguistic capabilities.

9. What types of AI are you involved with?
Both classical approaches in (semantic) knowledge representation, parsing (syntax analysis) and information and ontology extraction from texts, as well as Deep Learning methods.

10. Which specific use cases of AI do you find most impressive?
Neural Style Transfer, Deep Fake (on images, audio, video), and of course Transformer-based approaches to automatic question answering and text generation.

11. Is there anything else you'd like to say that you haven't had a chance to do yet?
It remains exciting. What worries me a bit is the digitalization lameness and AI fear in Germany, which we simply cannot afford if we want to remain a leading industrial nation. More AI and automation are the only way to secure prosperity in the face of a declining population and to be able to hold our own against competition from the US and China. To do this, we need more people who can not only apply artificial intelligence, but also develop it further.

1. For which teaching area are you appointed?
Electrical engineering, electrical measurement, communications engineering

2. What is your background and how did you come to work with AI?

Through national and European research projects. We have been working with AI methods here for at least 5 years.

3. What lectures do you currently give and what do students learn in them?

El. measurement technology, high frequency technology, applied systems engineering. AI is mentioned here, but is not part of the lectures. Statistics is only touched upon in the context of el. measurement technology for accuracy/resolution/error consideration.

4. What is the importance of AI in this area?
There is a big impact in research projects, but no impact in my lectures.

5. How do you assess the career opportunities for students in our AI degree programs?

I cannot say, since I do not teach in the AI program.

6. In addition to teaching, you are also active in research. In which research area(s) do you specialize?

Automotive, electromobility (energy-optimal route calculation, charging stops, etc.). Automated driving (radar, camera, lidar sensor technology, fusion ...). Intelligent infrastructure units to support autonomous vehicles, focusing on aforementioned sensor technology, data abstraction and secure communication via mobile communications (including Quality of Service assessment).

7. What fascinates you about these topics?
It is the logical continuation of what I did in my industry days. Making cars safer (reducing accidents), more efficient, more environmentally friendly and more comfortable has always been my goal.

8. How has AI impacted this area(s) of research and what role does it play?
Automated driving works without AI at best on the highway, i.e. in very well structured territory. And even here, systems work better with AI.

9. What types of AI are you involved with?

Decision trees (decisions comprehensible, that's why approved in vehicles), neural networks (not approved for e.g. longitudinal/transversal driving because the decisions are not comprehensible), spiking neural networks / neuromorphic signal processing as a hopefully suitable means to reduce power requirements.

10. Which specific use cases of AI do you find most impressive?
Real-time camera processing.

1. For which teaching area are you appointed?
Big Data and Cloud Computing for AI

2. What is your background and how did you come to work with AI?

Already in my diploma thesis in computer science, 17 years ago, I dealt with AI-related issues by designing a system for heuristic optimization of the distribution of hardware and software components in vehicles for AUDI AG. Machine learning and genetic programming were used for this.

My curriculum vitae can be read in more detail on my homepage. There are a number of references to all three technical terms in my multi-layered field of teaching from my career. AI accompanied my dissertation as well as my further activities in the industry.

3. What lectures do you currently give and what do students learn in them?

In addition to various basic subjects such as Database Systems, Algorithms and Data Structures and Programming 3: Java, I hold advanced bachelor modules such as Big Data, Cloud & NoSQL or Web Application Development as well as master modules such as Semantic Web Technologies or Big Data and Cloud-based Computing. Students learn about software building blocks for Big Data processing such as Hadoop/Spark, MongoDB, Neo4j, Exasol and Snowflake as well as modern web application development and the practical use of cloud-based infrastructures.

4. What is the importance of AI in this area?
Bulk data serves as a starting point for training modern artificial intelligence, so students need techniques for data management and analysis. In addition, it is possible to run massively parallel programming of AI procedures on Big Data computing networks. Integrated machine learning in database systems is also exciting. AI plays several roles in cloud computing, including that the cloud offers quite a few platforms for AI application development.

5. How do you assess the career opportunities for students in our AI degree programs?

They have excellent career opportunities. Our AI students who apply for internships at companies for their practical semester are already experiencing real enthusiasm from the companies about the fact that we have already taught them the skills on AI but also Big Data and Cloud Computing at this point.

6. In addition to teaching, you are also active in research. In which research area(s) do you specialize?

My primary research area is Big Data Curation. This involves AI-powered methods to ensure that relevant data is reliably available in the future, for research and reuse. After all, "Deletion is easy. Preserving is hard!"

In addition, I have research interests in Green Big Processing, methods optimized by cost, to ensure relevance and economics for bulk data processing in the cloud. I am also pursuing the following research areas currently popular in the international DB community: massively parallel programming of AI procedures; integrated machine learning in database systems; intelligent algorithms and agile data integration with NewSQL and NoSQL systems; scalable data architectures in the cloud on modern and evolving hardware infrastructures.

7. What fascinates you about these topics?
Big Data Curation requires a whole bouquet of skills to answer the data relevance question with high confidence. What is exciting to me is how we marry these disciplines and how we can lower the hurdles for use in practical applications. For all my research interests and teaching responsibilities, I am driven by a deep love and passion for computer science.

8. How has AI impacted this area(s) of research and what role does it play?
Data management and artificial intelligence have formed a symbiotic relationship for decades. Modern AI has grown into the Big Data world and cloud computing. Driven by the increasing importance of AI for applications in all technical areas, a consolidated interdisciplinary partnership between Big Data, cloud computing and AI exists today.

9. What types of AI are you involved with?
In the area of Data Science, I deal with machine learning, i.e., subsymbolic AI. In the area of web application development, also with semantic knowledge representation and symbolic AI.

10. Which specific use cases of AI do you find most impressive?

I just find every AI-in-everyday-life impressive. Starting with email spam filters, which have already become so commonplace that probably no one thinks of AI in that context anymore. Beyond that, voice assistants like Alexa and Siri. Voice translators like DeepL. The recommendation systems with which feeds like Flipboard and TikTok keep me happy. And already assisted driving, and hopefully autonomous driving someday.

11. Is there anything else you'd like to say that you haven't had a chance to do yet?
In our time, innovative techniques for mass data storage and analysis are newly emerging and this data serves as raw material for artificial intelligence. I am looking forward to actively shaping this development.

1. For which teaching area are you appointed?
Intelligent mobile systems

2. What is your background and how did you come to work with AI?

Longer story, I am actually a "roboticist" with an electrical engineering background and worked at Bosch for several years after my PhD, first in the area of fully automated parking systems and most recently for global employee training in machine learning. Parallel to my job or between my two stations at Bosch, I also studied mathematics and computer science. AI has always fascinated me, luckily in robotics you are quite close to it, which is why it was a smooth transition.

3. What lectures do you currently give and what do students learn in them?

•     Programming I (Python): Basics of programming
•     Internet technologies: what happens in the background when I go to a page in my browser?
•     Real-time operating systems: Everything about microcontroller programming when real time matters.
•     Mobile robotics with ROS: How do driving/flying robots find their way from A to B?

4. What is the importance of AI in this area?

Depends, in robotics AI is becoming more and more important, in real-time operating systems probably less so.

5. How do you assess the career opportunities for students in our AI degree programs?

Very good, since the students are strong in programming while also being taught theory.

6. In addition to teaching, you are also active in research. In which research area(s) do you specialize?

My most recent publication was in the area of reinforcement learning.

7. What fascinates you about these topics?

Reinforcement learning is in fact the only technique where robots learn on their own, i.e. start from scratch and improve through trial and error. I personally find this much more fascinating than classical supervised learning where the data is "hard" given.

8. How has AI impacted this area(s) of research and what role does it play?

Reinforcement learning has received a massive boost from Deep Learning, especially in the last 10 years, which has allowed us to solve much more complex problems.

9. What types of AI are you involved with?
Most recently, a lot with explainable AI. This refers to all types of AI where humans want to understand why the AI arrived at its result. The whole thing is becoming more and more important, especially in the context of autonomous driving and medicine: Imagine an AI tells you after analyzing an MRI scan that you have a tumor. Then you already want to know based on which information the AI came to the conclusion.

10. Which specific use cases of AI do you find most impressive?

It's two years old, but protein folding by AlphaFold still fascinates me. For decades, biologists tinker with solutions and make no progress. And suddenly (to exaggerate) a few AI experts with no prior knowledge of protein folding take on the problem and solve it in a few years.

11. Is there anything else you'd like to say that you haven't had a chance to do yet?
Amberg is beautiful! :-)

1. For which teaching area are you appointed?
Data Analytics.

2. What is your background and how did you come to work with AI?

After completing my undergraduate degree and subsequent PhD in applied mathematics, I wanted to work in a field where mathematics and computer science intertwined to generate real-world benefits. At that point, more and more companies were recognizing the potential in leveraging their existing data assets, so I decided to join a large German insurance company as a Data Scientist. Since the business model of insurance companies has always been based on data, there was already a high level of "data expertise" in this environment. In addition to the use of classic statistical methods, for example for rate design, more and more new and exciting use cases and projects were added over time, for which AI methods could be profitably used. In addition to the technical aspects, I enjoyed helping to build a data science team and supporting it as a scrum master. What appealed to me most about a university professorship was the opportunity to experience and accompany the use of AI methods in various application areas and companies in projects and theses. In addition, I already enjoyed teaching a lot during my time at the university, which is why I did not hesitate when the opportunity arose to switch to the professorship for "Data Analytics" at OTH Amberg-Weiden in the summer semester of 2019.

3. What lectures do you currently give and what do students learn in them?

In my modules "Data Analytics", "Machine Learning" and "Deep Learning", students learn how to use machine learning methods correctly and in a goal-oriented way in practice. On the one hand, this requires manual or programming skills, e.g. in the preparation and processing of data, and on the other hand, a sound knowledge of the algorithms used and the methodical approach to model building. Since it is unfortunately possible to get things wrong when using AI methods, it is also important to me as a mathematician - in addition to teaching practical skills - to convey the mathematical correlations and fundamentals of the methods so that students develop a holistic picture and a sound understanding of how they work.

4. How do you assess the career opportunities for students in our AI degree programs?

Our AI graduates have excellent career entry and development prospects. Since AI methods are universally applicable, they are in demand in many industries and can easily change industries during their careers. During my work as a Data Scientist, I was able to experience for myself how varied and versatile a job in the AI environment is. I particularly enjoyed the interdisciplinary work on future-oriented topics. So studying AI is worthwhile!

5. In addition to teaching, you are also active in research. In which research area(s) do you specialize?

My primary interest is in the area of "advanced analytics" using predictive algorithms from the fields of "machine learning" or "deep learning". In general, I am interested in the question of how - i.e. with which methods and procedures - relevant insights can be gained from data in order to use them in a targeted manner. With the help of "predictive analytics" or "prescriptive analytics", complex patterns and relationships in data can be modeled in order to make predictions about future behavior and to optimize or control processes on this basis. One example that everyone is familiar with from everyday life is personalized advertising. Providers collect large amounts of customer data, build models for customers' affinity for certain products depending on customer-specific characteristics, and then use these to play out targeted or personalized advertising.

While the approaches are often similar, the concrete objectives of "advanced analytics" can be manifold, e.g. optimization and control of (production) processes, increase of product quality, safety, sustainability, customer satisfaction and many more.

6. What fascinates you about these topics?

"Machine Learning" and "Deep Learning" excite me on the one hand because of the diverse applications and the resulting practical benefits, which are also visible in our everyday lives, and on the other hand because of the high dynamics as a field of research. Hardly a year goes by without a new milestone or impressive success to report. As a mathematician, I am also fascinated by how different mathematical disciplines such as analysis, optimization, linear algebra and statistics are intertwined in Deep Learning, and how this interlocking produces great results.

7. How has AI impacted this area(s) of research and what role does it play?

The use of AI-based models complements methods from the field of classical statistics to gain insights from data. This is only possible in the right interplay between technology and algorithms, or between computer science and mathematics, which cross-fertilize each other. Accordingly, new interdisciplinary job profiles, such as that of the data scientist, have emerged at this interface.

8. What types of AI are you involved with?
I am primarily concerned with data-driven approaches in the area of supervised and unsupervised learning.

9. Which specific use cases of AI do you find most impressive?

I find generative AI models that generate content such as images, texts, music or videos that are hardly distinguishable from real or human-generated content for us humans particularly impressive. Also, voice-based services like virtual assistants and chatbots. There has been impressive progress here again just recently with the release of ChatGPT, not least in terms of processing German language.

10. Is there anything else you'd like to say that you haven't had a chance to do yet?
I hope to be able to follow and help shape the further development for a few more years and I am curious to see where it will lead us. Despite all the euphoria - as with any technology - the risks should not be ignored. That's why responsible handling is important, and I hope that in a few years' time we will be able to say that we have succeeded.

1. For which teaching area are you appointed?

Artificial visual intelligence

2. What is your background and how did you come to work with AI?

Already during my bachelor and master studies (Applied Mathematics and Computer Science, Karazin University, Kharkov, Ukraine) I was fascinated by AI algorithms. I started with the
classical methods of symbolic AI, and it was already really interesting. In my PhD studies (Jacobs University Bremen, Germany), I worked mainly with visual data, and still learned the other methods of AI, which I applied to the automated analysis of biomedical images.

3. What lectures do you currently give and what do students learn in them?

• Computer science: basics, algorithms, programming
• Artificial Intelligence 2: classical AI methods
• Selected topics in AI: in-depth study of classical AI methods
• Computer Vision: the basics of image processing
• Deep Vision: advanced deep learning algorithms for analysis of image data
• Research seminar: how to read the wiss. Papers and explain the methods to the others
• Introduction to the wiss. Work in medical image analysis: what is the medical image data, what are the problems and tasks, what is the state-of-the-art, how to read a paper, and how to actually write and submit your own paper

4. How do you assess the career opportunities for students in our AI degree programs?

AI methods are a main tool in the field of "Medical Image Analysis".

5. In addition to teaching, you are also active in research. In which research area(s) do you specialize?

I think the career opportunities are very good. You have the possibility to go either into industry or into research.

6. What fascinates you about these topics?

Medical image analysis, automated segmentation of MR and CT image data.

7. How has AI impacted this area(s) of research and what role does it play?

The many applications and how they can improve people's lives.

8. What types of AI are you involved with?

Actually AI plays the central role here. :) Much has become possible that could only be dreamed of 20 years ago.

9. Which specific use cases of AI do you find most impressive?

AI-guided surgery and AI assistants in radiology.

10. Is there anything else you'd like to say that you haven't had a chance to do yet?

"With great power comes great responsibility" © (Spiderman)

1. For which teaching area are you appointed?

Embedded Intelligence

2. What is your background and how did you come to work with AI?

I studied computer science at the University of Passau, and wrote my diploma thesis in the area of real-time data processing in sensor networks. Pattern recognition was an important component here to classify and remove environmental influences, which later led me to the Chair of Embedded Systems as a research assistant. There I worked in the area of gesture recognition, i.e. how AI can be applied to person-related real-time data in order to provide assistance to people. In 2012, I moved to the DFKI in Kaiserslautern, where I did my PhD in localization at the TU Kaiserslautern. Work in the Smart Factory there led to an increase in efficiency in production.

3. What lectures do you currently give and what do students learn in them?

In the Bachelor's program I am involved in basic education (programming and web client technologies), in higher semesters I am responsible for Industry 4.0 projects. In the Master Artificial Intelligence I read Selected Topics of Augmented / Virtual Reality and Embedded Intelligence. In the Industry 4.0 projects, I give out day-to-day topics from industry - e.g. evaluation of communication technologies or manageable AI projects - but also e.g. the construction of sensor prototypes. The master lectures usually focus on problems concerning real-time data streams, their preparation and processing.

4. How do you assess the career opportunities for students in our AI degree programs?

In the basic subjects, the topic of AI is only a marginal topic, I refer to possible implementation details, in higher semesters or in the master studies I discuss with the students the functionalities of the algorithms on real examples of trade and industry and show procedures in real-time data processing with respect to AI. In particular, the entire processing chain from data acquisition to AI classification / regression should be illustrated. It is important to me that students learn how to process an AI problem - from analysis of the problem, selection of sensors, recording of data, processing and classification using AI algorithms.

5. In addition to teaching, you are also active in research. In which research area(s) do you specialize?

I see excellent opportunities for our AI students. There are a large number of companies in the university's area of activity that use AI. Many companies are active here, particularly in quality assurance or in the more efficient use of equipment or materials, and they are looking for graduates in the AI field. Especially the close relationship to local companies enables our students to work on interesting topics from the companies in lectures or theses at an early stage in their education. In this way, our graduates can gain insights into their later working environment - and the companies can gain new employees.

6. What fascinates you about these topics?

I do research in the field of sensor technology, wearable sensor systems, localization and efficiency enhancement in the working environment. I also find indoor localization a very exciting topic.

7. How has AI impacted this area(s) of research and what role does it play?

I find the wide spread of the fields to be worked on very interesting. From hardware design, firmware programming, data transmission, data preparation and data evaluation, many topics are dealt with - so it never gets boring. The result, which is used in reality, is also important for me.

8. What types of AI are you involved with?

Besides the usual methods of signal processing (e.g. Kalman filtering, particle filter) I use e.g. regression algorithms to determine parameters for physical mathematical models. I also use AI algorithms to increase the accuracy of sensor data and to remove environmental influences from sensor data.

9. Which specific use cases of AI do you find most impressive?

I use machine learning algorithms, regressions, and classifiers.

10. Is there anything else you'd like to say that you haven't had a chance to do yet?

Of particular interest to me is the application of Deep Learning networks to images - object recognition, generative algorithms, and of course the language models - with all the pros and cons that their impact will have on our lives.