The Cyberspace Congress

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Dr. Elena Rodriguez
IEEE Fellow
Senior Member of ACM
Professor of Intelligent Systems and Robotics
Director, Human-AI Collaboration Lab
Technical University of Munich, Germany

Topic: The Future of Human-AI Collaboration: Building Trust in Intelligent Cyber-Physical Systems

Abstract: As artificial intelligence systems become increasingly integrated into critical cyber-physical infrastructures—from autonomous vehicles to smart manufacturing and healthcare robotics—the question of human-AI collaboration and trust becomes paramount. This keynote addresses the fundamental challenges and emerging solutions in creating transparent, reliable, and ethically-aligned intelligent systems that augment rather than replace human capabilities.

The presentation will explore the latest advances in explainable AI (XAI), covering techniques for making deep learning models interpretable to human operators, especially in safety-critical applications. We will examine frameworks for human-in-the-loop learning, adaptive automation that respects human autonomy, and cognitive models of trust calibration in human-AI teams.

Special focus will be given to emerging cyber-physical systems including collaborative robots (cobots) in Industry 5.0, AI-assisted surgical systems, and autonomous driving technologies. The talk will present novel approaches to designing AI systems that can explain their decisions in real-time, adapt to human preferences and working styles, and gracefully handle uncertainty and edge cases.

We will also discuss critical societal implications including workforce transformation, skill development for the AI age, regulatory frameworks for autonomous systems, and the ethical design principles necessary to ensure AI benefits all of humanity. Case studies from our Human-AI Collaboration Lab will demonstrate successful deployments in manufacturing, healthcare, and disaster response scenarios.

Key research directions include: multimodal human-robot communication, affective computing for empathetic AI, federated learning for privacy-preserving personalization, and formal verification methods for AI safety.

Biography: Dr. Elena Rodriguez is a Professor of Intelligent Systems and Robotics at the Technical University of Munich (TUM), where she directs the Human-AI Collaboration Lab. She received her Ph.D. in Robotics and AI from ETH Zurich in 2010, following her M.Sc. in Computer Science from Universidad Politécnica de Madrid.

Dr. Rodriguez is internationally recognized for her pioneering work in explainable AI, human-robot interaction, and trustworthy autonomous systems. She has published over 180 papers in top-tier conferences and journals, including NeurIPS, ICML, IJCAI, and Science Robotics. Her research on transparent decision-making in autonomous vehicles has influenced EU policy on AI regulation.

She is an IEEE Fellow (2019) and Senior Member of ACM, having received the IEEE Robotics and Automation Society Early Career Award (2016) and the European Association for Artificial Intelligence Distinguished Service Award (2020). Dr. Rodriguez was named one of MIT Technology Review's 35 Innovators Under 35 (2015) and received the Helmholtz Prize for Outstanding Research in AI (2021).

Dr. Rodriguez serves on numerous advisory boards including the European Commission's High-Level Expert Group on AI, the IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems, and the Partnership on AI. She is Associate Editor for IEEE Transactions on Robotics and the International Journal of Social Robotics.

Before joining TUM, Dr. Rodriguez was a Research Scientist at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and held visiting positions at Stanford University and the Max Planck Institute for Intelligent Systems. Her lab collaborates extensively with industry partners including BMW, Siemens, and Bosch on next-generation intelligent manufacturing systems.

Her current research focuses on developing provably safe AI systems, creating intuitive human-robot communication interfaces, and addressing the societal impacts of automation and AI deployment in critical infrastructure.