Embedded Files
Below you can find the list of invited speakers in alphabetical order.
Maria Kyriani
Maria Kyriani
University of Santa Clara
USA
Dr. Maria Kyrarini is an Assistant Professor of Electrical and Computer Engineering and a David Packard Jr. Faculty Fellow at Santa Clara University, where she leads the Human-Machine Interaction & Innovation (HMI²) research group. Previously, she was a postdoctoral research fellow at the Heracleia Human-Centered Computing Lab at the University of Texas at Arlington. She earned her Ph.D. in Electrical Engineering from the University of Bremen in 2019, focusing on robot learning from human demonstrations for human-robot synergy. Her research interests include robot learning from demonstrations, human-robot interaction, and human-centered robotics, with an emphasis on enhancing human performance.
Dr. Jiachen Li is an Assistant Professor in the Department of Electrical and Computer Engineering and, by courtesy, the Department of Computer Science and Engineering at the University of California, Riverside. He leads the Trustworthy Autonomous Systems Laboratory. Before joining UCR, he was a Postdoctoral Scholar at Stanford University and received his Ph.D. from the University of California, Berkeley. Dr. Li was named an RSS Robotics Pioneer and an ASME DSCD Rising Star. He serves as a Co-Chair of the IEEE RAS Technical Committee on Robot Learning and an Associate Editor for more than ten leading journals and conferences.
Jiachen Li
Jiachen Li
University of California - Riverside
USA
Bill Smart
Bill Smart
Oregon State University
USA
Dr. Bill Smart is an Associate Professor of Mechanical Engineering and an Adjunct Associate Professor of Computer Science at Oregon State University. He received his Ph.D. in Computer Science from Brown University in 2002. His research spans robotics and machine learning, with a focus on building intelligent systems that operate effectively in real-world, human-centered environments.
In robotics, he studies human–robot interaction, including how to improve collaboration between people and robots, how to enable robots to function autonomously for weeks or months at a time, and how they can serve as personal assistants for individuals with severe motor disabilities. In machine learning, his work focuses on developing methods that allow robots to learn effective or optimal behaviors from long-term interaction with the world, even when feedback is sparse, delayed, or occasionally incorrect.
Dr. Mike Hagenow is an Assistant Professor in the Department of Computer Sciences at the University of Wisconsin–Madison. Prior to this role, he was e a postdoctoral fellow in the Department of Aeronautics and Astronautics and CSAIL at MIT. His research focuses on human–robot teaming, with an emphasis on contact-rich, physically demanding tasks, shared autonomy, and robot learning. He aims to enable more effective collaboration between humans and robots by improving how robots learn from human demonstrations and how real-time interaction can be supported in complex tasks. Previously, he worked as a manager at Epic Systems on patient portal development. He received a B.S. in Mechanical Engineering from Tufts University in 2014, followed by an M.S. and Ph.D. in Mechanical Engineering from the University of Wisconsin–Madison in 2019 and 2023, respectively.
Mike Hagenow
Mike Hagenow
University of Wisconsin - Madison
USA
Zlatan Ajanovic
Zlatan Ajanovic
Intellectus Agens
Dr. Zlatan Ajanovic is a Postdoctoral Researcher at the Chair for Machine Learning and Reasoning of RWTH Aachen University, with Prof. Hector Geffner. Before, he was a postdoctoral researcher at TU Delft with Prof. Jens Kober. He earned his PhD degree from the Graz University of Technology. His main research interest lies in the Decision-making and Control for Autonomous Robots based on Planning, Learning, and Control Theory. He is the recipient of the IFAC Young Author Award, Hans List Award, and DAAD AInet PostDoc Fellowship. He has co-organized several workshops and conferences, the most notably AAAI25 and ICAPS25 Bridging the Gap Between AI Planning and Reinforcement Learning (PRL), ICAPS25 Workshop PlanRob: Planning and Robotics, ICRA 2023 Workshop on Life-Long Learning with Human Help (L3H2), 2023 BeNeLuX AI Conference, and ICAPS 2024 Conference.
Page updated
Report abuse