Tutorials

Title: Camera-Based Physiological Sensing for Fitness and Healthcare

Abstract: This tutorial will focus on the principles and techniques of camera-based contact-free physiological sensing, an area of research that has gained increasing attention due to its applications in non-intrusive health monitoring across various sectors such as fitness, healthcare, and automotive safety. The tutorial will cover methods for extracting physiological signals such as heart rate, heart rate variability, respiration rate, and blood oxygenation saturation without direct physical contact. It will address the technical challenges associated with contact-free sensing, primarily the low signal-to-noise ratio caused by subject movement. The tutorial will provide a comprehensive view of how principled and deep learning approaches can effectively manage these challenges under practical conditions to reliably extract physiological signals. The tutorial content is designed for attendees interested in the intersection of multimedia information processing and healthcare.

Biography: Chau-Wai Wong received his B.Eng. degree with first-class honors in 2008 and an M.Phil. degree in 2010, both in electronic and information engineering from The Hong Kong Polytechnic University. He completed his Ph.D. in electrical engineering at the University of Maryland, College Park, in 2017. He is currently an assistant professor at the ECE Department, Forensic Sciences Cluster, and Secure Computing Institute at NC State University, USA. He was a data scientist at Origin Wireless, Inc. His research interests include machine learning, multimedia forensics, statistical signal processing, and video coding, with a recent focus on physiological sensing, federated learning, and generative models. Dr. Wong is a recipient of the NSF CAREER award and a top-four student paper award. He is an elected member of IEEE IFS TC (2024-26), IEEE MSA TC (2022-26), and APSIPA IVM TC (2020-22). He was an area chair for ICME'21-24, a workshop chair for MIPR'22, and an area chair for MIPR'19. [Webpage] 

Title: Robust Neural Networks: Towards Explainability, Uncertainty, and Intervenability

Abstract: Neural network driven applications like ChatGPT suffer from hallucinations where they confidently provide inaccurate information. A fundamental reason for this inaccuracy is the lack of robust measures that are applied on the underlying neural network predictions. In this tutorial, we identify and expound on three human-centric robustness measures, namely explainability, uncertainty, and intervenability, that every decision made by a neural network must be equipped and evaluated with. Explainability and uncertainty research fields are accompanied by a large body of literature that analyze decisions. Intervenability, on the other hand, has gained recent prominence due its inclusion in the GDPR regulations and a surge in prompting-based neural network architectures. In this tutorial, we connect all three fields using gradient-based techniques to create robust machine learning models. Further information and materials are available at [Webpage] 

Biography: Ghassan AlRegib is currently the John and Marilu McCarty Chair Professor in the School of Electrical and Computer Engineering at the Georgia Institute of Technology. In the Omni Lab for Intelligent Visual Engineering and Science (OLIVES), he and his group work on robust and interpretable machine learning algorithms, uncertainty and trust, and human in the loop algorithms. The group has demonstrated their work on a wide range of applications such as Autonomous Systems, Medical Imaging, and Subsurface Imaging. The group is interested in advancing the fundamentals as well as the deployment of such systems in real-world scenarios. He has been issued several U.S. patents and invention disclosures. He is a Fellow of the IEEE. Prof. AlRegib is active in the IEEE. He served on the editorial board of several transactions and served as the TPC Chair for ICIP 2020, ICIP 2024, and GlobalSIP 2014.  He was area editor for the IEEE Signal Processing Magazine. In 2008, he received the ECE Outstanding Junior Faculty Member Award. In 2017, he received the 2017 Denning Faculty Award for Global Engagement. He received the 2024 ECE Distinguished Faculty Achievement Award at Georgia Tech. He and his students received the Best Paper Award in ICIP 2019 and the 2023 EURASIP Best Paper Award for Image communication Journal. 

Biography:Mohit Prabhushankar received his Ph.D. degree in electrical engineering from the Georgia Institute of Technology (Georgia Tech), Atlanta, Georgia, 30332, USA, in 2021. He is currently a Postdoctoral Research Fellow in the School of Electrical and Computer Engineering at the Georgia Institute of Technology in the Omni Lab for Intelligent Visual Engineering and Science (OLIVES). He is working in the fields of image processing, machine learning, active learning, healthcare, and robust and explainable AI. He is the recipient of the Best Paper award at ICIP 2019 and Top Viewed Special Session Paper Award at ICIP 2020. He is the recipient of the ECE Outstanding Graduate Teaching Award, the CSIP Research award, and of the Roger P Webb ECE Graduate Research Assistant Excellence award, all in 2022. He has delivered short courses and tutorials at IEEE IV'23, ICIP'23, BigData'23, WACV'24 and AAAI'24.