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Keynote Lectures

Simulation: What Made us Intelligent Will Make our Robots Intelligent
Antonio Loquercio, University of Pennsylvania, United States

Available Soon
Foteini Liwicki, Lulea University of Technology, Sweden

Available Soon
Massimo Tistarelli, Università degli Studi di Sassari, Italy

Seeing the Heartbeat: Remote Physiological Signal Measurement and Applications Using Facial Videos
Guoying Zhao, University of Oulu, Finland

 

Simulation: What Made us Intelligent Will Make our Robots Intelligent

Antonio Loquercio
University of Pennsylvania
United States
 

Brief Bio
Antonio Loquercio (https://antonilo.github.io/) is an assistant professor at the University of Pennsylvania. He received his PhD and M.Sc. from UZH and  ETH Zurich in 2021 and 2017. His research interests include learning-based robotics, computer vision, and machine learning. His work includes seminal results on vision-based agile flight via learning sensorimotor policies and continual learning in legged locomotion. He is the recipient of the 2017 ETH Medal for Outstanding Master Thesis, the Best System Paper Award at the Conference on Robot Learning (CORL) 2018, the RSS’20 Best Paper Award Honorable Mention, and the T-RO’20 Best Paper Award Honorable Mention. His article on superhuman drone racing was featured on Nature’s cover. He received the Georges Giralt PhD Award, the most prestigious award for PhD dissertations in robotics in Europe.


Abstract
Antonio Loquercio (https://antonilo.github.io/) is an assistant professor at the University of Pennsylvania. He received his PhD and M.Sc. from UZH and ETH Zurich in 2021 and 2017. His research interests include learning-based robotics, computer vision, and machine learning. His work includes seminal results on vision-based agile flight via learning sensorimotor policies and continual learning in legged locomotion. He is the recipient of the 2017 ETH Medal for Outstanding Master Thesis, the Best System Paper Award at the Conference on Robot Learning (CORL) 2018, the RSS’20 Best Paper Award Honorable Mention, and the T-RO’20 Best Paper Award Honorable Mention. His article on superhuman drone racing was featured on Nature’s cover. He received the Georges Giralt PhD Award, the most prestigious award for PhD dissertations in robotics in Europe.



 

 

Available Soon

Foteini Liwicki
Lulea University of Technology
Sweden
 

Brief Bio
Foteini Simistira Liwicki received her Ph.D. diploma from the School of Electrical and Computer Engineering, NTUA, Greece, in the field of Pattern Recognition in 2015 with the title “Recognition of online handwritten mathematical expressions”. From 1997 till 2015, she worked as Research Associate in the Institute of Language and Speech Processing, ATHENA R.C., where she was mainly responsible for research programs in the field of Pattern Recognition, Machine Learning and Natural Language Processing. She was also highly involved in the design and development of innovative educational platforms (targeting mainly high school education in Greece but also in other European countries). From 2015 till June 2019 she worked as a PostDoc fellow in the University of Fribourg, in the field of Document Image Analysis and Database generation. From June 2018 till June 2019 she worked as a PostDoc fellow with the Machine Learning group at the Luleå University of Technology, Sweden. From May 2022, she is working as Associate Professor at the Luleå University, in the area of Machine Learning with focus on combining Artificial Intelligence and Neuroscience.


Abstract
Available Soon



 

 

Available Soon

Massimo Tistarelli
Università degli Studi di Sassari
Italy
 

Brief Bio
Available Soon


Abstract
Available Soon



 

 

Seeing the Heartbeat: Remote Physiological Signal Measurement and Applications Using Facial Videos

Guoying Zhao
University of Oulu
Finland
 

Brief Bio
Guoying Zhao received the Ph.D. degree in computer science from the Chinese Academy of Sciences, Beijing, China, in 2005. She is currently an Academy Professor and full Professor (tenured in 2017) with University of Oulu. She is also a visiting professor with Aalto University. She is a member of Academia Europaea, a member of Finnish Academy of Sciences and Letters, Fellow of IEEE, IAPR, ELLIS and AAIA. She has authored or co-authored more than 360 papers in journals and conferences with 35060+ citations in Google Scholar and h-index 90. She is the recipient of 2024 IAPR Maria Petrou Prize. She has been associate Editor-in-Chief for Computer Vision and Image Understanding (CVIU), was/is associate editor for IEEE Trans. on Multimedia, Pattern Recognition, IEEE Trans. on Circuits and Systems for Video Technology, Image and Vision Computing and Frontiers in Psychology Journals. She is general co-chair for CVIP2025 and ACII 2025, was program co-chair for ACM International Conference on Multimodal Interaction (ICMI 2021), tutorial chair for ICPR 2024, panel chair for FG 2023, publicity chair of 22nd Scandinavian Conference on Image Analysis (SCIA 2023) and FG2018, and has served as area chairs for many conferences. Her current research interests include image and video representation, facial-expression and micro-expression recognition, emotional gesture analysis, affective computing, and biometrics. Her research has been reported by Finnish TV programs, newspapers and MIT Technology Review.


Abstract
Physiological signals such as heart rate (HR), heart rate variability (HRV), and respiratory frequency (RF) are vital indicators of human health and are typically assessed during clinical examinations. Traditional methods for measuring these signals rely on contact sensors, which can be inconvenient and may cause discomfort during long-term monitoring. This talk presents research on remote heart rate measurement from facial videos, covering the evolution from early hand-crafted approaches to advanced deep learning-based methods. In addition, new benchmark datasets - OBF and ORPDAD datasets — are introduced to support this line of research. Applications of remote heart rate measurement are also discussed, including atrial fibrillation (AF) screening and face anti-spoofing, leveraging heart rate-related features extracted from videos.



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