JCRAI 2022 Keynote Speakers
Introduction to Prof. Jan Treur:
Jan Treur has been a full professsor of AI since 1990 and is a well-recognized expert on the area of multidisciplinary human-like AI-modeling. He has published over 700 well-cited papers about cognitive, affective, and social modeling and AI systems making use of such models. He has also supervised more than 40 Ph.D. students in these areas and from 2016 on written and edited three books on (adaptive) network-oriented AI-modeling and its application in various other disciplines. Current research addresses modeling of higher-order adaptive processes by self-modeling network models with a specific focus on mental processes based on internal mental models and their use by internal simulation, their learning or formation (including organisational learning), and the control over them. An application focus is on the development and use of shared mental models supporting the road toward a just safety culture in organisations such as hospitals. A joint Springer Nature book about computational modeling of multilevel organisational learning is in preparation and will come out by the end of 2022 or beginning of 2023.
Introduction to Gülay Canbaloğlu:
Gülay Canbaloğlu combines her studies in Computer Engineering, Sociology and AI at the prestigious private Koç University in Istanbul with her work as Ph.D. candidate for the Safety and Security Science group at Delft University of Technology under supervision of prof.dr. Jan Treur and dr. Peter Roelofsma. Her research focus is on computational modelling of organisational learning with special application to learning of just safety culture in health care organisations. Part of her research is conducted in collaboration with dr. Anna Wiewiora from the School of Management of Queensland University of Technology in Brisbane. In her recent work she reached an important achievement by successfully using self-modeling networks to obtain suitable computational models of complex multilevel organisational learning processes. A joint Springer Nature book about this is in preparation and will come out by the end of 2022 or beginning of 2023.
Computational Modeling of Multilevel Organisational Learning Using Self-Modeling Network Models
Processes of multilevel organisational learning emerge as a cyclic interplay of various mechanisms at different levels. To analyse and simulate them computationally, the self-modeling network modelling approach from AI provides a powerful means to address the complexity of the interaction of different adaptation mechanisms and the control over them. In this keynote speech, recent developments are presented showing how this approach can be used to analyse and simulate complex processes of multilevel organisational learning. This covers both feed-forward learning to learn shared team or organisation mental models out of individually learned personal mental models and feedback learning to let individuals learn personal mental models from shared mental models. It is shown how by a self-modeling network, the different types of learning can be modeled using a first-order self-model for the learning and a second-order self-model level for the control over the learning. It will be discussed how this may be applied in the context of improving safety in health-related organisations such as hospitals.
JCRAI 2022 Invited Speakers
Introduction to Dr. Sandeep Singh Sengar:
Dr. Sandeep Singh Sengar is a Lecturer in Computer Science at Cardiff Metropolitan University, United Kingdom. He also holds the position of cluster leader for Computer Vision/Image Processing at this place. Before joining this position, he worked as a Postdoctoral Research Fellow at the Machine Learning Section of Computer Science Department, University of Copenhagen, Denmark (a rank #1 university of Denmark). He completed his Ph.D. degree in Computer Vision at Department of Computer Science and Engineering from Indian Institute of Technology (ISM), Dhanbad, India and an M. Tech. degree from Motilal Nehru National Institute of Technology, Allahabad, India. He has more than seven years of research and teaching experience. Dr. Sengar’s broader research interests include Machine/Deep Learning, Computer Vision, Image/Video Processing and its applications. He has published several research articles in reputed international journals and conferences. He is an Editorial Board Member of International Journal of Imaging Systems and Technology. He is a Reviewer of several reputed International Transactions, Journals, and conferences. He has also served as a Technical Program Committee member in many reputed International Conferences. He has organized several special sessions and given keynote presentations at International Conferences. In addition to these, he has also given many expert talks in reputed organizations. He always believes in collaborative opportunities.
Deep Learning Brings a New Dimension to Medical Imaging
Medical image segmentation is the part of computer vision and its target is to label each pixel of an object of interest in medical images. An end to end deep learning approach, Convolutional Neural Networks have shown state-of-the-art performance for automated medical image segmentation. However, it doesn’t perform well in case of complex environments. U-Net is another popular deep learning architecture especially for biomedical imaging. In this talk, a concise overview of the modern deep learning models applied in computer vision specifically in medical image analysis are provided and the key tasks performed by deep learning models, i.e., classification, segmentation, and detection are shown. Furthermore, we will discuss the thoroughly evaluated deep learning framework for segmentation of arbitrary medical image volumes. Our one of the discussed frameworks requires no human interaction, no task-specific information, and is based on a fixed model topology and a fixed hyper parameter set.
Keynote speech Ⅱ
Introduction to Dr. Shoujin Wang:
Shoujin Wang is a Lecturer in Data Science at University of Technology Sydney. He was a Research Fellow in Data Science at RMIT University from 2021 to 2022. Prior to joining RMIT, Shoujin was a postdoc at Macquarie University from 2018 to 2021. Shoujin obtained his PhD in data science from University of Technology Sydney in 2018. His main research interests include data mining, machine learning and recommender systems. He has published more than 30 research papers in these areas, most of which were published at premier data science and AI conferences or journals, like The ACM Web Conference, AAAI, IJCAI and ACM Computing Surveys (CSUR). Shoujin has generally served as a PC member or a senior PC member at over 10 premier international data science conferences including KDD, AAAI, IJCAI, WSDM, CIKM and a reviewer for more than 10 prestigious journals including Machine Learning, IEEE Transactions on Knowledge and Data Engineering, ACM Transactions on Information Systems, etc. Shoujin is the recipient of various awards, including 2021 DAAD AINet Fellowships, 2022 Club Melbourne Fellowships.
Recommender Systems: from Accuracy to Trustworthiness
This talk will provide an introduction to recommender systems (RSs), one of the most widely used AI techniques in the real world. Particularly, it will emphasize trustworthy (e.g., robust, fair, transparent, secure) RSs, the next-generation recommender systems which go beyond the traditional accuracy-oriented recommender systems. First, I will briefly introduce the background of recommender systems by answering three key questions, namely, What is an RS, Why we need RSs and How to build an RS? Then, I will introduce the classic methodologies for building RSs including collaborative filtering and content-based filtering, as well as the evaluation methods for RSs. Finally, I will illustrate the key concepts, main challenges together with methodologies in trustworthy RSs with some new insights. I will also share some future directions in this vibrant area.
JCRAI Past Speakers
Prof. Bruno SICILIANO
University of Naples Federico
University of Zurich