Lennart Svensson is a Professor of Signal Processing at Chalmers University of Technology, where he earned both his M.S. (1999) and Ph.D. (2004) in electrical engineering. His research focuses on nonlinear filtering, multi-target tracking, deep learning, Bayesian inference, and reinforcement learning. A prolific contributor to multi-target tracking literature, he has received multiple awards for his publications. As an educator, Lennart has developed five courses in the past five years, promoting active learning through flipped classrooms and online lectures. His teaching excellence has earned him both Chalmers' annual Pedagogical Award and a best lecturer recognition.

Dr. Yuxuan Xia, a Postdoctoral researcher in the Department of Electrical Engineering at Chalmers University of Technology, specializes in multi-object tracking and sensor fusion, with a particular focus on extended objects. Having earned both his M.Sc. in communication engineering (2017) and Ph.D. in signal processing (2022) from Chalmers, Xia has quickly established himself as a rising expert in his field. His research bridges advanced signal processing techniques with practical applications, especially in automotive systems, contributing to the development of sophisticated perception systems for autonomous vehicles and complex environments. Xia has co-organized tutorials on multi-object tracking at the Fusion conferences from 2020 to 2022, demonstrating his commitment to sharing knowledge in the field. As an instructor for the "Multi-Object Tracking for Automotive Systems" course and a contributor to the "Sensor Fusion and Multi-Object Tracking" professional certificate program, Dr. Xia plays a crucial role in educating the next generation of engineers and researchers in this rapidly evolving area of technology.

Dr. Karl Granström, currently a perception specialist at Embark Trucks, is a distinguished expert in autonomous vehicle technology, focusing on developing safer and more efficient transportation systems. With a PhD in Automatic Control and an MSc in Applied Physics and Electrical Engineering from Linköping University, Granström has built an impressive career spanning academia and industry. His research interests in sensor fusion, robotics, and machine learning have led to significant contributions in the field of tracking, particularly in extended object tracking. Prior to his current role, he held positions as a researcher at Chalmers University of Technology and as a postdoc at the University of Connecticut. Granström's expertise has been widely recognized, earning him multiple conference paper awards and the prestigious 2018 ISIF Young Investigator Award. He continues to contribute to academia by teaching courses on multi-object tracking for automotive systems at Chalmers, bridging the gap between cutting-edge research and practical applications in autonomous vehicle technology