Keynote Speakers
Prof. Hongmei (Mary) He
University of Salford, Manchester, UK
Title: The Challenges and Opportunities of Human-Centered AI for Trustworthy Robots and Autonomous Systems
Bio:
Discover the work of Hongmei (Mary) He, a Professor specializing in Future Robotics, Engineering, and Transport Systems at the University of Salford. With advanced degrees from Loughborough University, UK, and a background as a senior embedded system engineer at Motorola, China, Dr. Hongmei is at the forefront of AI research. Her expertise spans Cognitive Robotics, Cyber Security, Data/Sensor Fusion, and the safety of autonomous systems. An accomplished researcher, she has led numerous projects funded by top-tier institutions and has an extensive publication record. Dr. Hongmei also plays a significant role in the academic and professional community through her involvement with IEEE, the EPSRC peer-review college, and as a reviewer for the EU H2020 ICT Robotics Programme.
The trustworthiness of robots and autonomous systems (RAS) is at the centre of many research agendas on AI driven autonomous systems. This research systematically investigates for the first time the key aspects of human-centred AI (HAI) for trustworthy RAS in terms of safety, security, human-machine interaction, system health and ethics by identifying the challenges in implementing trustworthy autonomous systems with respect to the five key facets and exploring the role of AI in relation to the five facets of trustworthy RAS. It also presents a new acceptance model for RAS as a framework for human-centred AI requirements, promoting machine intelligence that augments human capabilities and places humans at the centre to achieve trustworthy RAS by design.
Prof. Bruno Agard
École Polytechnique, Université de Montréal, Canada
Title: Making Demand Predictions from Noisy Datasets
Bio:
He is currently a Professor in Department of Mathematics and Industrial Engineering, Ecole Polytechnique de Montréal, Canada. He received his doctoral degree at National School of Industrial Engineering – National Polytechnic Institue of Grenoble in Grenoble, France. And then, he became Assoc. Prof. at Department of Mathematics and Industrial Engineering, Ecole Polytechnique de Montreals in Montreal, Quebec, Canada from 2018-2014.
He has nearly 190 publications including journal articles, conference papers, book chapters or technical reports. So far, he has got 10 times of Meritas Prize from 2004 to 2022 and 3 Paper Awards which are: (2ème place à la compétition du jeune chercheur Prix Laurent Villeneuve), (Best Young researcher Paper Award) and (2ème au prix Mayoux-Dauriac 2011).
In the area of Industry 4.0, most companies plan to use artificial intelligence to enhance various internal processes that IA is supposed to learn directly from the databases. The knowledge is supposed to be in the dataset, but in many cases, the data itself contains a certain amount of noisy information (missing data, errors, …). In this context, we will show some strategies that have been implemented with real industrial partners in order to handle such situations. After a certain level of data preparation, the data is clustered in groups that share certain similarities, then a model is learned for the group and the prediction of the group is formatted for each specific element in the group.
Dr. Bill Wu (Wei William Wu)
Distinguished professor of Hangzhou Dianzi University, CEO of Cynoware
Title: AIoT: AI and IoT Integration
Bio:
Professor Wei Wu earned his Ph.D. in Industrial Computing from Université de Lille, France, and his Bachelor’s and Master’s degrees in Electrical Engineering from Wuhan University. With 35 years of experience in the U.S., France, and China, he has worked across both industry and academia. Currently, he is a distinguished professor at Hangzhou Dianzi University and remains actively engaged in both academic and industry sectors.
AI and IoT Integration elves into the powerful synergy between Artificial Intelligence (AI) and the Internet of Things (IoT) to develop smarter, more efficient systems. Attendees will explore key trends, such as edge computing, and examine practical applications across various industries, including hospitality, manufacturing, and agriculture. The workshop features case studies on using SIP phones for hotel room management, AIoT in textile manufacturing for process optimization, and AI-driven fruit recognition in supermarkets. Additionally, it addresses challenges like data privacy and scalability while offering insights into future trends, providing participants with the knowledge needed to drive innovation in their respective fields.
Prof. Xianyi Zeng
ENSAIT Textile Engineer School, GEMTEX, University of Lille, France
Title: Development of intelligent garments for online monitoring of human health
Bio:
Xianyi Zeng is a full professor (exceptional class – grade 2) at ENSAIT Textile Engineer School – University of Lille, France, and Director of the GEMTEX National Laboratory.
He has been an IEEE senior member since 2011 and led the Theme on Human-Machine Systems in GRAISyHM (regional association of Researchers on Automation) since 2013. He has had the French National Knight’s title in the Order of the Academic Palms since 2019 and was the holder of the Innovation R&D Award from the France-China Committee, and the EU Key Innovation Team Award from the EU Innovation Radar, both in 2021. He was awarded Top-10 Innovation Leader of Overseas Chinese in Europe in 2022. In ENSAIT, he has been a leader of the Department of Fashion and Service Engineering since 2009.
Xianyi Zeng has published more than 160 papers in peer-reviewed international journals, presented more than 260 papers at international conferences, and supervised more than 40 PhD students. In addition, as a principal investigator, he has led three European projects (Asia-Link: 2004-2008), SMDTex – European Joint Doctorate Program on Textile Sustainable Design and Management (Erasmus Mundus Program: 2013 – 2021), FBD_BModel – Fashion big data and business model (H2020 Program: 2017-2021) and several national and regional research projects such as IOTFetMov (ANR Program: 2015 – 2019), Camille 3D (FUI Program: 2012 – 2015), SUCRE (ARCIR Program: 2013 – 2017) and industrial projects in France and Europe.
In this presentation, we propose a series of principles for designing intelligent and connected garments for online human health monitoring, including textile/garment design, electronic devices integration, local decision support system development. These principles will permit to enhance product autonomy and intelligence level and fully integrate devices into textiles. The proposed garment design process can be more adapted to customized body shapes of the target population and is capable of selecting the most relevant fabrics and garment patterns for minimizing signal attenuation and improving wearer’s comfort. Also, the integrated physiological sensors are connected to a centralized microcontroller, on which an intelligent algorithm is implemented for filtering noises, extracting relevant features from measured signals and intelligently interacting with the cloud platform. Two specific applications (i.e. fetal movement monitoring and COVID-19 symptom evaluation) have been proposed to show how intelligent garments are integrated into the patient’s lifestyle for long-term continuous health care.
Prof. Victor Chang
Aston University, United Kingdom
Title: Digitalization in omnichannel healthcare supply chain businesses: The role of smart wearable devices
Bio:
Prof. Victor Chang is a Professor of Business Analytics at Operations and Information Management, Aston Business School, Aston University UK, since mid-May 2022. He was previously a Professor of Data Science and Information Systems at the School of Computing, Engineering and Digital Technologies, Teesside University, UK. He has deep knowledge and extensive experience in AI-oriented Data Science and has significant contributions in multiple disciplines. Within 4 years, Prof Chang completed Ph.D. (CS, Southampton) and PGCert (Higher Education, Fellow, Greenwich) while working for several projects simultaneously. Before becoming an academic, he has achieved 97% on average in 27 IT certifications. He won 2001 full Scholarship, a European Award on Cloud Migration in 2011, IEEE Outstanding Service Award in 2015, best papers in 2012, 2015 and 2018, the 2016 European award: Best Project in Research, 2016-2018 SEID Excellent Scholar, Suzhou, China, IEEE Outstanding Young Scientist award in 2017, IEEE 2017 special award on Data Science, 2017-2023 INSTICC Service Awards, Talent Award Suzhou 2019, Top 2% Scientist between 2019 and 2024, top Business Research Scholar, the most productive AI-based Data Analytics Scientist between 2010 and 2019, Highly Cited Researcher 2021, Top 125 British Computing Scientists 2022-2024 and numerous awards mainly since 2011. Prof Chang was involved in different projects worth more than £14 million in Europe and Asia. He has led more than 4 major projects worth more than £3 million. He has published 3 books as sole authors and the editor of 2 books on Cloud Computing and related technologies. He published 1 book on web development, 1 book on mobile app and 1 book on Neo4j. He gave 51 keynotes at international conferences. He is widely regarded as one of the most active and influential young scientists and experts in IoT/Data Science/Cloud/security/AI/IS, as he has the experience to develop 10 different services for multiple disciplines. He is the founding conference chair for IoTBDS, COMPLEXIS and FEMIB to build up and foster active research communities globally with positive impacts and has recently stepped down.
The advancement in technology has fostered the prevalence of the Internet of Things (IoT), which enhances healthcare business quality, offers a seamless customer experience, and maximizes turnovers and profits. Consequently, omnichannel services have emerged by integrating online and offline channels and providing customers with more real-time information and services to increase their engagement. Healthcare wearable devices appear as a salient tool to connect healthcare providers and patients and thus become an essential part of the omnichannel environment. Along with this trend, the ethical concerns while using these devices have increasingly intensified and are significant barriers to market expansion. Nevertheless, there is a lack of studies discussing the role of wearables in omnichannel hospital supply chain management and examining the influence of those above concerns on healthcare wearables adoption. Therefore, this study explores these gaps through an integrated approach. Furthermore, we proposed a framework integrating the traditional statistical and machine learning-based approach to analyze a large amount of data; and thereby facilitate a data-driven analytic model to manage omnichannel healthcare supply chain businesses.
