Women in Data Science (WiDS) Conference, FAU Erlangen-Nürnberg, 2025
About the Conference
The WiDS Erlangen Conference is an integral part of the annual WiDS Worldwide Conferences, organized by Stanford University, with over 200 events held worldwide. At FAU, the conference will spotlight cutting-edge advancements in data science and artificial intelligence, emphasizing their applications across diverse fields such as science, medicine, and engineering, as well as addressing industry challenges. The event will feature a distinguished lineup of both emerging and established women data scientists from academia and industry, sharing insights from their latest research.
Everyone is cordially invited to the WiDS Erlangen Conference, which will be held in person on Tuesday, June 10, 2025.
Venue: Room H13, Felix-Klein Building, Cauerstrasse 11, 91058 Erlangen
How to participate?
Participation in the WiDS Erlangen Conference is free. However, registration is necessary. It would be great to register online before June 8, 2025.
Registration Closed
Thank you for your interest! Registration for the conference is now closed. We look forward to welcoming all registered attendees and appreciate your enthusiasm and support.
Confirmed Speakers
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: Across Limbs and Interfaces: Advancing sEMG-Based Control for Intuitive Prosthetics
Abstract: Many myoelectric prostheses are rejected due to complexity, calibration demands, or lack of intuitive control. This keynote presents work across two domains — lower- and upper-limb prosthesis control — using surface EMG (sEMG) signals as a user-intent interface. In the lower-limb system, we explore real-time ankle prosthesis control via ridge regression with a 9-second calibration and evaluate performance using the Target Achievement Control (TAC) test. In the upper limb, we examine the signal quality and movement classification potential of an Agonist-Antagonist Myoneural Interface (AMI) using high-density sEMG and Linear Discriminant Analysis (LDA). These insights highlight how signal separability, training time, and control strategy can shape the usability of prostheses.
Abstract: Many myoelectric prostheses are rejected due to complexity, calibration demands, or lack of intuitive control. This keynote presents work across two domains — lower- and upper-limb prosthesis control — using surface EMG (sEMG) signals as a user-intent interface. In the lower-limb system, we explore real-time ankle prosthesis control via ridge regression with a 9-second calibration and evaluate performance using the Target Achievement Control (TAC) test. In the upper limb, we examine the signal quality and movement classification potential of an Agonist-Antagonist Myoneural Interface (AMI) using high-density sEMG and Linear Discriminant Analysis (LDA). These insights highlight how signal separability, training time, and control strategy can shape the usability of prostheses.
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: Dr.-Ing. Siming Bayer, Research Group Leader, Chair of Computer Science 5 (Pattern Recognition), FAU
Title: Multimodal Data Analysis with Machine Learning – a journey between departments and across borders
Abstract: This presentation illustrates interdisciplinary and international approaches to applying advanced machine learning methodologies across healthcare and energy sectors. It outlines the development and application of digital twins using multimodal data and knowledge graphs for utility management. Further, the focus shifts to healthcare, highlighting AI-based frameworks for enhanced patient-centered care, specifically in medical screening, diagnostic support, and surgical planning. A detailed probabilistic registration framework is introduced for multimodal image alignment, primarily targeting vascular structures. Clinical validations of these methodologies, including intraoperative brain shift compensation and longitudinal pulmonary studies, demonstrate substantial improvements in accuracy and robustness. Further outlooks for multimodal data analysis are given considering the current development of advanced AI techniques.
Abstract: This presentation illustrates interdisciplinary and international approaches to applying advanced machine learning methodologies across healthcare and energy sectors. It outlines the development and application of digital twins using multimodal data and knowledge graphs for utility management. Further, the focus shifts to healthcare, highlighting AI-based frameworks for enhanced patient-centered care, specifically in medical screening, diagnostic support, and surgical planning. A detailed probabilistic registration framework is introduced for multimodal image alignment, primarily targeting vascular structures. Clinical validations of these methodologies, including intraoperative brain shift compensation and longitudinal pulmonary studies, demonstrate substantial improvements in accuracy and robustness. Further outlooks for multimodal data analysis are given considering the current development of advanced AI techniques.
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: Deep Learning Approaches for Clinical Data
Abstract: The increasing digitalization of clinical data offers significant opportunities for the development of deep learning approaches to support clinical decision-making. However, several challenges hinder their development and implementation in clinical practice, including the heterogeneity of clinical data, the complexity of biomedical interactions, and the scarcity of annotated datasets. Combining machine learning with clinical expertise can help better understand and address these challenges. The Erlangen Center for AI in Medicine, established in 2024, aims to foster interdisciplinary research collaboration between clinicians and machine learning researchers. In this talk, I will present examples of ongoing research projects in various medical fields.
Abstract: The increasing digitalization of clinical data offers significant opportunities for the development of deep learning approaches to support clinical decision-making. However, several challenges hinder their development and implementation in clinical practice, including the heterogeneity of clinical data, the complexity of biomedical interactions, and the scarcity of annotated datasets. Combining machine learning with clinical expertise can help better understand and address these challenges. The Erlangen Center for AI in Medicine, established in 2024, aims to foster interdisciplinary research collaboration between clinicians and machine learning researchers. In this talk, I will present examples of ongoing research projects in various medical fields.
Program Overview
Tuesday, June 10, 2025
| 08:30 – 09:30 | On-site registration and collection of conference badge |
| 09:30 – 09:45 | Opening & Welcome Talk: Prof. Dr. Andrea Bréard, Vice President Education, FAU |
| 09:45 – 10:10 | Prof. Dr. Franziska Mathis-Ullrich, Department of Artificial Intelligence in Biomedical Engineering, FAU Cognitive Robotics for Surgery: Elevating Efficiency in the OR |
| 10:10 – 10:35 | Dr. Carolin Kaiser, Nuremberg Institute for Market Decisions The Human Touch in AI: Exploring Its Influence on Consumer Choices |
| 10:35 – 11:00 | Hannah Braun, Ph.D. Candidate, The Assistive Intelligent Robotics (AIROB) Laboratory, FAU Across Limbs and Interfaces: Advancing sEMG-Based Control for Intuitive Prosthetics |
| 11:00 – 11:20 | Coffee Break |
| 11:20 – 11:40 | Dr. Tanja Kaiser, Research Group Leader, Artificial Intelligence and Robotics Lab, UTN Nürnberg Multi-Robot Learning: Towards Intelligent Robot Groups |
| 11:40 – 13:10 | Lunch Break |
| 13:10 – 13:35 | Panel Discussion: Career perspectives and challenges as a female data Scientist in academia and industry |
| 13:35 – 14:00 | Dr.-Ing. Siming Bayer, Research Group Leader, Chair of Computer Science 5 (Pattern Recognition), FAU Title: TBA |
| 14:00 – 14:20 | Coffee Break |
| 14:20 – 14:45 | Melanie B. Sigl, Managing Consultant at PRODATO Scalable Optimization of Large-Scale Systems Using MLOps |
| 14:45 – 15:10 | Dr. Emmanuelle Salin, Group Leader, Department of Artificial Intelligence in Biomedical Engineering, FAU Deep Learning Approaches for Clinical Data |
| 15:10 – 16:10 | Networking and Closing |
Organizers
Melanie B. Sigl (Ambassador WiDS Erlangen FAU), melanie.sigl[at]fau.de
Bathsheba Darko (Ambassador WiDS Erlangen FAU), bathsheba.darko[at]fau.de
Marius Yamakou (Department of Data Science, FAU), marius.yamakou[at]fau.de
More Information
Email us at konferenz-wids[at]fau.de
Previous Editions
Support and Sponsors
WiDS Erlangen 2025 expresses special thanks to Andrea Hoppe and Prof. Dr. Jens Habermann for their exceptional support throughout the organization of the conference, and extends profound gratitude to the esteemed sponsors for their generous contributions:
Code of Conduct: Our community values considerate, respectful, and collaborative behavior from all participants. Please read the entire code of conduct.
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