Invited Speaker 

Invited Speakers

 Speaker Title: Exploring the Potential of Topological Deep Learning in Advancing Medical Imaging  

Dr. Yashbir Singh (Yash)

Department of Radiology

Mayo Clinic, Rochester, USA 

Link for personal Webpage

Dr. Yashbir (Yash) Singh, Ph.D., is an Assistant Professor of Radiology at the Mayo Clinic, Rochester, MN. His accomplished career includes a role as a Medical Scientist at WVU Medicine, USA, a DAAD fellow, and he is an active member of the New York Academy of Sciences. Dr. Singh's research concentrates on deep learning-based artificial intelligence (AI) and topological data analysis within medical imaging, specifically enhancing the interpretability of AI models and discovering new disease imaging biomarkers. 

Abstract: Topological Deep Learning (TDL) has emerged as a powerful tool in medical imaging, promising a revolution in diagnostic and therapeutic strategies. This talk explores the potential of TDL in advancing medical imaging, emphasizing its role in improving human-computer interaction (HCI) in healthcare. TDL leverages the intricate geometric and topological structures underlying data, enhancing the predictive power of traditional deep learning models. The integration of TDL into healthcare presents an opportunity to optimize diagnostic accuracy, minimize human error, and offer personalized treatment plans. This talk also discusses the significance of intuitive HCI approaches in medical imaging, critical in facilitating seamless interaction between healthcare professionals and complex AI systems. Creating intelligent interfaces that enable the interpretation of TDL outputs and support clinical decision-making achieves this. The potential challenges and ethical considerations of implementing TDL in healthcare are also addressed. This talk underscores the transformative potential of TDL in medical imaging and its capability to redefine HCI, fostering a more efficient, accurate, and patient-centric approach in healthcare. 

 Speaker Title: Toward Human-Centered AI to Improve Decision Making in Healthcare 

Dr. Hyunggu Jung

Department of Computer Science and Engineering

University of Seoul, Korea 

Link for personal Webpage

Hyunggu Jung is an associate professor of Computer Science & Engineering and an associate professor of Artificial Intelligence at the University of Seoul and directs the Human-Centered Artificial Intelligence Lab (HCAIL). He received his B.S., M.Math, and M.S. from KAIST, the University of Waterloo, and Stanford University, respectively, all in Computer Science. He also received his B.S. with a minor in Business Economics from KAIST and his Ph.D. in Biomedical and Health Informatics from the University of Washington School of Medicine. Further, he worked at Microsoft Research and PARC, a Xerox company as a research intern, respectively. His research interests lie at the intersection of human-centered AI, health informatics, social computing, and accessibility & aging. His research aims to advance AI research through design and engineering to support people with special needs (e.g., older adults, streamers with visual impairments, North Korean defectors with depression) across multiple domains: health, social media, sharing economy, and education. He is a recipient of the Korean Government Scholarship and Mogam Science Scholarship. For more information on his recent research activities.

Abstract: In this talk, I will present my recent research efforts in the field of human-centered artificial intelligence (AI). Over the past few years, my research has focused on the early-stage development of innovative technologies through design and engineering to support people with special needs across multiple domains: health, social media, accessibility, and sharing economy. For the rest of the talk, I will focus on describing my approaches using design methods and mathematical models to develop human-centered AI technologies. I will conclude with my future research directions, including the development of tools that leverage data by reflecting the needs and barriers of AI stakeholders (e.g., healthcare providers, managers, and patients affected by AI-based decisions), and the development of effective human-centered AI systems for improving decision making in healthcare.

 Speaker Title: Exploratory Visual Analysis of Hospital Networks for Aiding Clinical Researchers 

Dr. Hyunjoo Song

School of Computer Science and Engineering

Soongsil University, Korea

Link for personal Webpage

Hyunjoo Song is an Assistant Professor in the School of Computer Science and Engineering at Soongsil University. He received B.S. in computer science and engineering and M.S. and Ph.D. in electrical engineering and computer science from Seoul National University. His research interests include human-computer interaction, information visualization, visual analytics, eye tracking, and health informatics.

Abstract: This talk delves into the role of visual analytics in healthcare, elucidating its potential to improve patient care and research methodologies. This talk introduces a visual analytic study on hospital networks. Inter-hospital coordination, especially with the advent of endovascular thrombectomy (EVT), is vital for optimal stroke treatment outcomes. While many studies have focused on quantitative analyses of hospital networks, there's a gap in topological examinations. This talk introduces a framework that constructs and analyzes networks from stroke patient transfer data. The tool visualizes national hospital network structures, delves into detailed structures via dynamic queries, and highlights hub-and-spoke configurations within clusters. It will also discuss the future direction of the research regarding the evolution of network structures over time and explore simulations of networks based on the changing roles (e.g., hub and spoke) of hospitals. 

 Speaker Title: Enriching Cultural Heritage through HCI-Infused AI: ASI-Protected Temples and Monuments 

Dr. Gaurav Tripathi

Bharath Electronics Limited, India 

Link for personal Webpage

Dr. Gaurav Tripathi is a renowned expert in AI, deep learning, and computer vision with a profound passion for cultural preservation. With a Ph.D. in Deep Learning and extensive experience in researching and implementing these technologies, Dr. Tripathi brings a unique blend of technical expertise and a deep understanding of Indian cultural heritage. Currently Dr. Tripathi is working as an AI Technical expert to Ministry of Culture, India. Their engaging presentation skills, combined with a multidisciplinary background, make them an ideal candidate to deliver an inspiring and informative session at IHCI 2023.

Abstract: This talk delves into the symbiotic relationship between AI, deep learning, and computer vision in revitalizing India's cultural heritage, with a specific focus on ASI-protected temples and monuments. By conquering challenges in documentation, restoration, maintenance, and visitor engagement, these cutting-edge technologies are driving a resurgence in cultural preservation. The talk showcases pioneering implementations that exploit AI, deep learning, and computer vision to safeguard and propagate India's cultural legacy. It encompasses:

·       Efficient documentation and image analysis automation.

·       Facilitating artifact restoration and data-driven conservation decision-making.

·       Utilizing computer vision for structural monitoring, damage detection, and predictive maintenance.

·       Enriching visitor encounters through AR/VR immersion, interactive guidance, and personalized suggestions.

The discourse addresses ethical considerations, cultural sensitivity, and data privacy while embracing the socio-cultural facets of this revival. In alignment with HCI's vision, this presentation underscores innovation, pragmatism, and diverse viewpoints, underscoring the potential of AI, deep learning, and computer vision to amplify user involvement in Indian architectural marvels. These technologies offer a transformative conduit for fostering a deeper admiration of India's rich cultural tapestry.

 Speaker Title: How can healthcare services benefit from artificial intelligence?

Dr. Jan-Willem van 't Klooster 

University of Twente, Netherlands 

Link for personal Webpage

Dr. Jan-Willem van 't Klooster is a head of department of the BMS faculty innovation lab, He is responsible for the strategy, technical management, research, operations, finances and team of 15FTE at BMS LAB. His lab consists of about 20 states of the art lab spaces; a world class mobile lab; research software, data science, VR and web development capacity; 1600 lendables; advanced VR, neuroscience and physiological measurement capacities; and a portfolio of instruments for hightech business & entrepreneurship teaching and research. He received his PhD in health informatics from the University of Twente in 2013. His research interests include innovative healthcare service models, digital health, BCI, remote monitoring and physiological sensing.  

Abstract: In this talk, we will untangle the main concepts and AI, their purposes, usage and expectations in healthcare, and study main approaches, architectures, and principles. Special focus will be given to generative AI, personalization, and social robotics in healthcare. Enriched with many current research case studies and examples, this tutorial will provide an excellent learning opportunity for computer scientists, health psychologists, health scientists and interaction designers interested in this exciting multidisciplinary field and aid in developing an articulated opinion on whether AI in healthcare a sword of Damocles is indeed.

 Speaker Title: Building Inclusive Interfaces to Assist Persons with Disabilities for Effective Human Computer Interaction  

Dr. K S Kuppusamy 

Department of Computer Science

Pondicherry University, India 

Link for personal Webpage

Dr. Kuppusamy is an Assistant Professor in the Department of Computer Science, School of Engineering and Technology, Pondicherry University, Pondicherry. His research interest includes Accessible Computing, Human Computer Interaction, Machine Learning. He has published 85+ papers in Indexed International Journals, International Conferences and technical magazines. His articles are published by reputed publishers such as Oxford University Press, Elsevier, Springer, Taylor and Francis, World Scientific, Emerald and EFY. He is the recipient of Best Teacher award from Pondicherry University for Six times. He is serving as the Counsellor for Persons with Disabilities at HEPSN Enabling Unit, Pondicherry University.

Abstract: The term “Human” in the Human Computer Interaction refers to a heterogenous group of users. Inclusion is the key to effective Human Computer Interaction. In this talk, I would like to focus on a special group of users, Persons with Disabilities (PwD). The PwDs are often a marginalized group of users who face significant barriers interacting with the digital systems. In this talk, I would like to explore various interaction level barriers faced by persons with disabilities. The potential solutions to some of these barriers can be through thoughtful interface design. An interface need to be intelligent and at the same time it has to be inclusive too. Some of the barriers in the interfaces make the persons with disabilities a soft target in various cyberattacks. This talk will focus on adapting the latest developments in the field of Artificial Intelligence for the design of Intelligent and Inclusive Interfaces that aims to provide a barrier-free digital experience for persons with disabilities.

 Speaker Title: Wi-Fi Sensing: Principle, Implementation and Applications for Human Activity and Gesture Recognition 

Prof. Siba K Udgata

School of Computer and Information Sciences

University of Hyderabad, India 

Link for personal Webpage

Dr. Siba K Udgata is currently a Professor of Computer and Information Sciences at the University of Hyderabad, India, where he directs a research group focusing on sensor networks, IoT, wireless communications, and intelligent algorithms. He worked as a Research Fellow at the United Nations University/International Institute of Software Technology (UNU/IIST), Macau. He has published more than 100 research papers in peer-reviewed journals and at international conferences. He has edited ten international conference proceedings for Springer LNAI, AISC and SIST. He is a recipient of the IBM SUR (Shared University Research) award for the project "Mobile Sensor network based rescue management system". He has successfully completed seven Government of India sponsored research projects in the domain of sensor network, IoT, and cognitive radio network. 

Abstract: This talk is an attempt to demonstrate a device free general purpose Wi-Fi sensing system to track events and recognize activities even through the wall and other materials using the Channel State Information (CSI) values extracted from the received Wi-Fi signals at the receiver end. The received signal characteristics change with the presence of the human beings, and their activities affect the signal propagation, resulting from reflection and scattering. The activities can be recognized by analyzing the CSI values corresponding to different sub-carriers of the received signal. CSI values contain fine grain information such as amplitude and phase to achieve better sensing accuracy with a unique pattern that can be observed corresponding to each activity and material. We will present our experience of developing the transmitter and receiver hardware modules together with the necessary software for capturing the CSI from Wi-Fi signals and conducted multiple experiments using the low power, low cost ESP-32 Wi-Fi module and Intel 5300 NIC module, for human presence, activity detection, material detection, ambient condition in indoor environments.

 Speaker Title: IoT-Based Unobtrusive Physical Activity Monitoring System for Predicting Dementia  

Dr. Jungyoon Kim 

Department of Computer Science

Kent State University, Ohio, USA 

Link for personal Webpage

Jung-Yoon Kim received a B.S. degree in electronics and an M.S. degree in electrical and computer engineering from the University of Ulsan, Korea, in 2004 and 2006, respectively, and a Ph.D. degree in information sciences and technology from Pennsylvania State University, University Park, PA, USA, in 2014. He is currently an Assistant Professor of Computer Science with Kent State University, where he is also the Founding Director of the Smart Communities and IoT Laboratory His areas of experience and expertise are in smart health and well-being, especially in real-time cardiovascular disease and stress monitoring, physiological sensor design, and intelligent analytics for decision supports; environmental monitoring and assessment―especially in air quality monitoring and cut slope movement monitoring; and ubiquitous computing—especially in embedded system design, energy efficient processing, and programming model for networking performance.   

Abstract: Mental health-related disorders are common in elderly populations. Among the various mental health disorders, one most significant threat is dementia, and prediction of dementia has become an important issue related to well-being in old age, because the disease progression of dementia can be slowed by early diagnosis and disease control. In this paper, we propose an unobtrusive dementia-prediction system for monitoring physical activities of elderly persons either living alone or as a couple in different house structures, achieved through passive infrared (PIR) motion sensors combined with data processing. The proposed feature extraction algorithm extracts feature values related to physical activities from simple passive infrared sensors located in each room space. We then apply a variety of common popular classification models, including Deep Neural Networks (DNNs), to predict the risk of dementia in a sensor-enabled home. We implemented and validated algorithms on data collected for over a month from 18 participants who were engaged with a variety of living conditions. The proposed system was effective in predicting dementia risk, with up to a 0.99 area under the curve (AUC) using DNN with principal component analysis (PCA) and a quantile transformer scaler. In terms of the result based on leave-one-subject-out (LOSO) analysis, an accuracy of 63.38% was achieved using DNN with PCA and a standard scaler. The proposed methodology is non-invasive and cost-effective, and can be used for a variety of long-term monitoring and early symptom detection systems, helping caregivers provide optimal interventions to elderly individuals at risk for dementia. 

 Speaker Title: TBA

Dr. Anshuman Shastri 

Director, Centre for Artificial Intelligence, Banasthali Vidyapith, India

Anshuman Shastri is the Director of the Centre for Artificial Intelligence, Banasthali Vidyapith, India. He received the B.E. degree (Hons.) in electronics and communications engineering from the Birla Institute of Technology and Science, Pilani, India, in 2014, and the M.Sc. and Ph.D. degrees in electronics engineering from the University of Kent, Canterbury, U.K., in 2016 and 2020, respectively. His research interests include microwave antennas, smart energy harvesting, additive manufacturing, frequency-selective surfaces, millimeter-wave devices, and modeling of frequency reconfigurable structures. Dr. Shastri received the International Young Scientist Award in 2020, along with the International Young Researcher and Research Excellence Awards in 2021. He is a senior member of IEEE and a professional member of ACM.

Abstract: TBA

 Speaker Title: Exploring Quantum AI for Human-Computer Interaction

Dr. Madhusudan Singh 

Oregon Institute of Technology (OregonTech), USA

Dr. Madhusudan Singh is an Assistant Professor at the Oregon Institute of Technology (OregonTech), USA. Before joining OregonTech, he served as an Assistant Professor and Director of the Research Center for AI/Data Science & Blockchain Technology at Woosong University, South Korea. Throughout his career, he held positions as a Research Professor at Yonsei University and as a Senior Research Engineer at Samsung Mobile Display, South Korea. An avid researcher, Dr. Singh has delivered over 30 invited and keynote talks as an IEEE Senior Member and ACM distinguished speaker and contributions as an IEEE Subject Matter Expert for IEEE e-learning Online courses. He is also a member of Intelligent Human Computer Interaction (IHCI) Society, The International Association for Cryptologic Research (IACR), and Metaverse World Council. He currently serves as a Series Editor of Blockchain Technologies at Springer Nature. Dr. Singh's research interests encompass Applied AI, Blockchain Technology, Cybersecurity, Data Sciences and Quantum Computing. With this focus, he has authored over 80 peer-reviewed articles, patents, books, book chapters, Journals, and conference proceedings. Notably, Dr. Singh's work has earned him a position in the top 2% of scientists, as recognized in studies conducted by researchers at ICSR Lab, Elsevier BV, and Stanford University from 2020 to 2023. 

Abstract: This talk delves into the transformative potential of Quantum Artificial Intelligence (AI) in reshaping human-computer interaction. By harnessing the principles of quantum computing, including quantum algorithms and entanglement, we investigate how Quantum AI can overcome classical computing limitations. Through a comprehensive analysis, we envision a future where Quantum AI systems revolutionize user experiences, unlocking new frontiers in computation and problem-solving. This talk sheds light on the exciting frontier of Quantum AI, offering insights into its potential applications and implications for the future of human-computer interaction.