The seminar for becoming a Certified Data Scientist Specialized in Assuring Safety addresses the safety assurance of Artificial Intelligence (AI) in safety-critical areas.
We provide an overview of the state of the art for assuring AI safety. At the interface between safety and Artificial Intelligence, we address relevant standards and standardization initiatives. We create awareness for the challenges caused by using AI in safety-critical solutions by highlighting typical problems from this area of conflicting requirements.
In the seminar, participants learn about possible strategies for assuring the safety of Artificial Intelligence. Together, will test a selection of approaches to address concrete challenges and derive customized safety concepts.
The training, which is conducted online, includes a large proportion of exercises and interaction in order to convey the content in a practice-oriented manner and to enable transfer to everyday professional life.
Basic knowledge of statistics and data analytics is expected. Initial experience with Machine Learning methods is an advantage. Prior knowledge in the field of safety engineering is not required.
Safety Assurance for Artificial Intelligence at a glance:
The seminar Safety Assurance for Artificial Intelligence is held in German. Accompanying documents are available in English.
Day 1: Motivation and Fundamentals
Day 2: Standards and Measures for AI Safety
Day 3: Measures for Safe AI (cont.)
Day 4: Safety Argumentation
Day 5: Certified exam in the morning
Dr.-Ing. Rasmus Adler studied Applied Computer Science and has been employed at Fraunhofer IESE since 2006. In his PhD, he developed fail-operational solutions for active safety systems such as ESP. After that, he dedicated himself to model-based engineering of autonomous systems as a project manager and safety expert. He coordinated the development of solutions to measure at runtime the risk of planned/possible autonomous system behavior with respect to the current situation and to initiate risk-minimizing actions. In his current position as Program Manager Autonomous Systems, he is particularly dedicated to the risk management of networked cyber-physical systems. To maximize the benefit of individual systems, but also the overall benefit of systems of systems, he relies on cooperative risk management at runtime that is partly based on Artificial Intelligence. Since current safety standards do not support this innovative risk management, he is involved in standardization committees and participates in the development of normative requirements for autonomous networked cyber-physical systems.
Dr. Michael Kläs has been working in applied research and advising companies in the areas of software quality and data analytics ever since completing his studies in Computer Science. In the past decade, he has been responsible in numerous industry and research projects for the establishment of KPI systems, the evaluation of new technologies, and the development of predictive analyses. In his dissertation, he addressed the prediction of software faults using expert knowledge. Currently, his focus is on the area of potential analysis for data-driven innovation and on the analysis of data quality and uncertainty in Big Data and AI systems. As the author of numerous professional publications, he is also actively engaged as a university lecturer and as an expert in standardization (DIN/VDE).
Janek Groß studied mathematics and psychology in Eichstätt, Germany, earning a Bachelor of Science degree and the first state examination for secondary school teachers. He completed his Master’s degree in Robotics Cognition Intelligence at the Faculty of Computer Science at the Technical University of Munich. During his studies, he acquired extensive knowledge in mathematical statistics and empirical sciences. He also gained relevant experience in the development of larger neural networks and in the use of mainframe computers. In basic research, his main interests are in the areas of time series analysis and information theory.
Since the beginning of 2021, he has been working in the “Data Science” department of Fraunhofer IESE, in close collaboration with the “Safety Engineering” department. His tasks include the empirical validation and formal assurance of data-driven AI models used in autonomous vehicles and robots.
Dr. Adam Trendowicz is a senior engineer in the “Data Science” department at the Fraunhofer Institute for Experimental Software Engineering IESE in Kaiserslautern, Germany. After receiving his PhD in the area of software project effort and risk estimation models from the University of Kaiserslautern (Germany), he continues to work in data science and data-driven business innovation.
Dr. Trendowicz has more than 20 years of experience in the analysis of software projects and products in various industries. He has led various activities in the areas of software measurement, prediction, and improvement in software companies of different sizes and in different domains (including Germany, Japan, and India). In this context, he has developed and empirically validated prediction models for software cost and software quality.
In his current work, Dr. Trendowicz focuses on data quality and preparation in the context of Machine Learning and on lean deployment of data-driven innovations based on Machine Learning and Artificial Intelligence solutions.
Dr. Trendowicz has co-created the “Data Scientist” continuing education and certification program offered by the Fraunhofer Big Data and Artificial Intelligence Alliance. Furthermore, he has held several tutorials on business-IT alignment, data preparation and analysis, software quality measurement, and cost estimation. Finally, he has co-authored several books and numerous international journal and conference publications.
Pascal Gerber studied Computer Science at the Technical University of Kaiserslautern, Germany. In his theses as well as during his work as a student research assistant at Fraunhofer IESE, he focused on topics such as reinforcement learning and quality influence models for evaluating uncertainties in the decisions of data-driven models.
After graduating, he started working in the “Safety Engineering” department of Fraunhofer IESE in 2021 and acquired fundamental competencies in safety engineering. Since 2023, he has been working in the “Data Science” department and is currently focusing on quality influence models.
Marc Wellstein received his Master’s degree in computer science (M.Sc.) from TU Kaiserslautern, Germany, in July 2021. Since then, he is full-time researcher in the “Safety Engineering” department at the Fraunhofer Institute for Experimental Software Engineering (IESE) in Kaiserslautern. His main research topic is dynamic risk management, complementing established design time assurance methods with runtime assurance aspects, and assurable, human-like driving behaviors in simulation-based validation for autonomous driving systems. Generally, his focus is mainly on the automotive domain.
Dr.-Ing. Patrick Wolf studied applied computer science at the Technical University of Kaiserslautern with a focus on embedded systems and robotics. His master's thesis dealt with the assessment of sensor data quality and the effect of uncertainties in perception systems of autonomous vehicles. From 2016 to 2023, he was a research assistant at the Chair of Robotic Systems at RPTU Kaiserslautern and developed safe and reliable autonomy solutions for commercial vehicles in off-road environments. Dr. Wolf completed his doctorate “Cognitive Processing in Behavior-Based Perception of Autonomous Off-Road Vehicles” with distinction in 2022.
Since 2023, Dr. Wolf has been working as a “Senior Safety Engineer” in the department for “Safety Engineering” at Fraunhofer IESE and develops new solutions in the field of safe autonomous driving. In addition to his research and industry activities, he is a lecturer in the Fraunhofer certificate program “Certified Data Scientist Specialized in Assuring Safety”. Since the winter semester 2023, he has been a lecturer in the Department of Computer Science at RPTU and teaches the lecture “Offroad Robotics” at the Kaiserslautern campus.
Daniel Hillen received his Master's degree in Computer Science from the Technical University in Kaiserslautern, Germany. Since 2020, he has been working full-time as a research associate and safety engineer at the Fraunhofer Institute for Experimental Software Engineering (IESE). His research focuses on the development of methods for safeguarding autonomous vehicles. His focus is on modeling the vehicle environment and model-based safety engineering.
Please fill out the registration form below. The maximum number of participants is 15. Registrations will be considered in the order they are received.
If the seminar is already fully booked on your preferred date, you have the option of being placed on the waiting list. The waiting list is non-binding and free of charge for you. As soon as a place becomes available, we will take steps to fill it as soon as possible and would then contact you.
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We do, of course, treat your data confidentially and do not pass it on to third parties. You can object to the processing of your data at any time.
The participation fee is tax-free according to Sec 4 No. 22a of the German Value Added Tax Act (UStG).
It includes accompanying documents, the examination fee, and catering during on-site events. We do, of course, treat your data confidentially and do not pass it on to third parties. You can object to the processing of your data at any time.
Following the training, our accounting department will send an official invoice to the address provided by you.
The certificate for the seminar Safety Assurance for Artificial Intelligence is issued by the Fraunhofer Personnel Certification Authority.
Admission requirements are a university degree or equivalent qualifications proven by an individual attestation.