Women in Data Science (WiDS) Conference, FAU Erlangen-Nürnberg, 2025

Registration

Speaker: Prof. Dr. Andrea Bréard, Vice President Education, FAU

Speaker: Prof. Dr. Franziska Mathis-Ullrich, Department of Artificial Intelligence in Biomedical Engineering, FAU

Title: Cognitive Robotics for Surgery: Elevating Efficiency in the OR

Abstract: The integration of robotics into surgery has revolutionized the field, enabling greater precision and less invasiveness. As we move beyond rigid robotic systems like the Da Vinci and Senhance, the next frontier lies in flexible, sensorized continuum robots capable of navigating complex anatomical environments with minimal tissue damage. This talk explores how the future of surgical robotics hinges on cognitive capabilities—robots that perceive, learn from expert demonstrations, and adapt to dynamic surgical contexts. Leveraging machine learning, especially reinforcement and imitation learning, we develop systems that not only assist but also collaborate naturally with surgeons. Additionally, we address the challenges of controlling flexible instruments through a hybrid approach combining classical modeling, data-driven techniques, and real-time sensor feedback. Together, these advances lay the groundwork for context-aware, semi-autonomous surgical robots, fundamentally advancing the human-robot partnership in the operating room.

Speaker: Dr. Carolin Kaiser, Head of Artificial Intelligence

Title: The Human Touch in AI: Exploring Its Influence on Consumer Choices

Abstract: As AI technology continues to evolve, shopping interfaces are becoming not only smarter but also increasingly human-like. Chatbots now engage in sales conversations, voice assistants process verbal orders, and avatars and robots can even communicate non-verbally with customers through smiles or eye contact. This growing human-like quality of machines is changing how people perceive and interact with them. It raises important questions about the psychological impact of such relationships, including the potential to influence consumer decisions. This talk presents various studies on different shopping interfaces to show how interactions with artificial intelligence affect consumer perception, buying attitudes, and purchasing behavior. It also discusses the opportunities and risks for businesses, consumers, and society.

Speaker: Hannah Braun, Ph.D. Candidate, The Assistive Intelligent Robotics (AIROB) Laboratory, FAU

Title: TBA

Abstract: TBA

Speaker: Dr. Tanja Kaiser, Research Group Leader, Artificial Intelligence and Robotics Lab

Title: Multi-Robot Learning: Towards Intelligent Robot Groups

Abstract: No longer confined to structured environments such as factory floors, robots are becoming more ubiquitous in our world, including where we live and work, and their numbers will continue to grow. Robots will inevitably need to interact with each other, whether in cooperation or competition. In many applications, multi-robot systems (MRSs) offer even greater efficiency and robustness than single-robot systems. However, coordinating multiple robots is challenging due to our ever-changing world. In this talk, I will use examples from our research to show how machine learning and generative AI techniques can help us overcome remaining challenges in multi-robot systems to make them ready for our everyday environments.

Speaker: Prof. Dr. Seung Hee Yang, Department of Artificial Intelligence in Biomedical Engineering, FAU

Title: TBA

Abstract: TBA

Speaker: Dr.-Ing. Siming Bayer, Research Group Leader, Chair of Computer Science 5 (Pattern Recognition), FAU

Title: TBA

Abstract: TBA

Speaker: Melanie B. Sigl, Managing Consultant at PRODATO and Ph.D. Candidate at Chair of Computer Science 6 (Data Management), FAU

Title: Scalable Optimization of Large-Scale Systems Using MLOps

Abstract: Optimizing large systems is essential for long-term cost reduction and improved product efficiency across supply chains. However, implementing plant-specific Advanced Process Controls can be expensive, labor-intensive, and time-consuming. Machine learning techniques can significantly reduce this effort. The resulting model serves as a digital twin of the plant, enabling simulation and optimization of various plant settings. The goal, following a successful proof of concept, is to fully automate and scale the operationalization of the entire infrastructure and data pipeline. This talk explores the migration of the machine learning project from on-premise to the Cloud. It discusses the challenges of implementing a scalable MLOps process and how these challenges were addressed in the project.

Speaker: Dr. Emmanuelle Salin, Group Leader, Department of Artificial Intelligence in Biomedical Engineering, FAU

Title: TBA

Abstract: TBA