[et_pb_section fb_built=”1″ _builder_version=”4.16″ _module_preset=”default” custom_padding=”0px||24px|||” da_disable_devices=”off|off|off” global_colors_info=”{}” da_is_popup=”off” da_exit_intent=”off” da_has_close=”on” da_alt_close=”off” da_dark_close=”off” da_not_modal=”on” da_is_singular=”off” da_with_loader=”off” da_has_shadow=”on”][et_pb_row _builder_version=”4.16″ _module_preset=”default” use_background_color_gradient=”on” background_color_gradient_stops=”#afafaf 0%|#636363 100%” background_color_gradient_start=”#afafaf” background_color_gradient_end=”#636363″ width=”100%” max_width=”2560px” module_alignment=”center” custom_padding=”||24px|||” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.16″ _module_preset=”default” header_text_color=”#FFFFFF” global_colors_info=”{}”]<\/p>\n
[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”4.16″ _module_preset=”default” custom_padding=”4px|||||” da_disable_devices=”off|off|off” global_colors_info=”{}” da_is_popup=”off” da_exit_intent=”off” da_has_close=”on” da_alt_close=”off” da_dark_close=”off” da_not_modal=”on” da_is_singular=”off” da_with_loader=”off” da_has_shadow=”on”][et_pb_row column_structure=”1_3,2_3″ disabled_on=”off|off|off” _builder_version=”4.20.2″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”1_3″ _builder_version=”4.20.2″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/easyconferences.eu\/iisa2026\/wp-content\/uploads\/2025\/10\/Jie-Lu-AO.jpg” title_text=”Jie Lu AO” _builder_version=”4.20.2″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][et_pb_column type=”2_3″ _builder_version=”4.20.2″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.20.2″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n
IEEE Fellow, IFSA Fellow, ACS Fellow, Australian Laureate Fellow, Australian Industry Laureate Fellow
\nDirector of Australian Artificial Intelligence Institute
\nUniversity of Technology Sydney, Australia[\/et_pb_text][et_pb_text _builder_version=”4.20.2″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n
Short CV:<\/strong><\/p>\n Distinguished Professor Jie Lu is a world-renowned scientist in the field of computational intelligence, primarily known for her work in concept drift, transfer learning & fuzzy transfer learning, recommender systems, and decision support systems. She is an IEEE Fellow, IFSA Fellow, Australian Computer Society Fellow, Australian Laureate Fellow and Australian Industry Laureate Fellow. Professor Lu is the Director of the Australian Artificial Intelligence Institute (AAII) at University of Technology Sydney, Australia. She has published six research books and over 500 papers in leading journals and conferences; won 10 Australian Research Council (ARC) Discovery Projects and over 30 industry projects as leading chief investigator; and has supervised over 60 PhD students to completion. She serves as Editor-In-Chief for Knowledge-Based Systems<\/em>. She is a recognized keynote speaker, delivering over 50 keynote speeches at international conferences. She is the recipient of NeurIPS Outstanding Paper Award, three IEEE Transactions on Fuzzy Systems Outstanding Paper Awards, Australasian AI Distinguished Research Contribution Award, Australian NSW Premier’s Prize and the Officer of the Order of Australia (AO).<\/p>\n Title: Concept Drift Detection, Understanding and Adaptation<\/span><\/strong><\/p>\n Abstract:<\/strong> \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 <\/span> <\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_3,2_3″ disabled_on=”off|off|off” _builder_version=”4.20.2″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”1_3″ _builder_version=”4.20.2″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/easyconferences.eu\/iisa2026\/wp-content\/uploads\/2025\/10\/photo_trajkovic.jpg” title_text=”photo_trajkovic” _builder_version=”4.20.2″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][et_pb_column type=”2_3″ _builder_version=”4.20.2″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.20.2″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n Professor in the School of Engineering Science, Simon Fraser University, Burnaby, British Columbia, Canada<\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.20.2″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n Short CV:<\/strong><\/p>\n Ljiljana Trajkovic received the Dipl. Ing. degree from University of Pristina, Yugoslavia, the M.Sc. degrees in electrical engineering and computer engineering from Syracuse University, Syracuse, NY, and the Ph.D. degree in electrical engineering from University of California at Los Angeles. She is currently a professor in the School of Engineering Science, Simon Fraser University, Burnaby, British Columbia, Canada. Her research interests include communication networks and dynamical systems. Dr. Trajkovic served as IEEE Division X Delegate\/Director, President of the IEEE Systems, Man, and Cybernetics Society, and President of the IEEE Circuits and Systems Society. She serves as Editor-in-Chief of the IEEE Transactions on Human-Machine Systems. She is a Distinguished Lecturer of the IEEE Systems, Man, and Cybernetics Society and was a Distinguished Lecturer of the IEEE Circuits and System Society. She is a Fellow of the IEEE.<\/p>\n Title: Data Mining and Machine Learning for Analysis of Network Traffic<\/span><\/strong><\/p>\n Abstract:<\/strong>\u00a0 \u00a0 \u00a0\u00a0 <\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_3,2_3″ disabled_on=”off|off|off” _builder_version=”4.20.2″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”1_3″ _builder_version=”4.20.2″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/easyconferences.eu\/iisa2026\/wp-content\/uploads\/2025\/10\/PHOTO-PROFILE_MARIA-VIRVOU.jpg” title_text=”PHOTO PROFILE_MARIA VIRVOU” _builder_version=”4.20.2″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][et_pb_column type=”2_3″ _builder_version=”4.20.2″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.20.2″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n Dean, School of Informatics and Communication Technologies [\/et_pb_text][et_pb_text _builder_version=”4.20.2″ _module_preset=”default” hover_enabled=”0″ global_colors_info=”{}” sticky_enabled=”0″]<\/p>\n Short CV:<\/strong><\/p>\n Professor- Dr. Maria Virvou is the Dean of the School of Informatics and Communication Technologies at the University of Piraeus, Greece, and a Full Professor in the Department of Informatics. She is Co-Founder and Co-Editor-in-Chief of the Springer book series Learning and Analytics in Intelligent Systems and Artificial Intelligence\u2013Enhanced Software and Systems Engineering, as well as Co-Founder of the IEEE Information Intelligent Systems and Applications (IISA). Title: AI in Education: Which Technologies Matter, How Deep Is Deep, and What Functions will Transform Learning?<\/span><\/strong><\/p>\n Abstract:<\/strong> Intelligent Tutoring Systems (ITSs) have long delivered structured, adaptive, and interpretable instruction through explicit knowledge representation, learner modelling, and personalized pedagogical strategies. Large language models, in contrast, enable open-ended dialogue, generative capacity, and broad accessibility, introducing new possibilities for learner engagement and creativity. However, both paradigms present limitations: while ITSs can be constrained by domain specificity and development complexity, LLMs encounter challenges of reliability, factual coherence, and sustained learner modelling.<\/span><\/p>\n Together, these AI technologies offer significant opportunities to enhance personalization, engagement, and skill development. They promote critical thinking, creativity, collaboration, communication, and digital literacy, while depending on these same human capacities in a reciprocal process of design, governance, and pedagogical integration. The continuing evolution of these technologies requires researchers, educators, and designers to ensure that intelligent educational systems operate responsibly, remain grounded in humanistic and pedagogical values, and contribute to a future in which human and artificial intelligence co-evolve to enrich the intellectual foundations of education.<\/p>\n <\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_3,2_3″ disabled_on=”on|on|on” _builder_version=”4.20.2″ _module_preset=”default” disabled=”on” global_colors_info=”{}”][et_pb_column type=”1_3″ _builder_version=”4.20.2″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/easyconferences.eu\/iisa2026\/wp-content\/uploads\/2025\/01\/clarke.png” title_text=”clarke” _builder_version=”4.20.2″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][et_pb_column type=”2_3″ _builder_version=”4.20.2″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.20.2″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n Professor in Cyber Security and Digital Forensics
Concept drift is known as an unforeseeable change in underlying streaming<\/span> data distribution over time. The phenomenon of concept drift has been recognized as the root cause of decreased effectiveness in many decision-related applications. A promising solution<\/span> for coping with persistent environmental<\/span> change and avoiding system performance degradation is to build <\/span>a detection, understanding and adaptive system. This talk will present a set of methods and algorithms<\/span> that can<\/span> effectively and accurately detect, understand, and adapt<\/span> concept drift. The main contents include (1) concept drift detection: competence models to indirectly measure variations in data distribution through changes in competence. By detecting changes in competence, differences in data distribution can be accurately detected and quantified, then further described in unstructured data streams; (2) concept drift understanding: algorithms for determining a drift region to identify when and where a concept drift takes place in a data stream, and a local drift degree measurement that can continuously monitor regional density changes; (3) concept drift adaptation: methods and algorithms for model adaptation as well as solutions for redundancy removal. These techniques can be applied to data-driven real-time prediction and decision support in complex data stream environments.<\/span><\/p>\nProfessor Ljiljana Trajkovic<\/h2>\n
<\/span>Collection and analysis of data from deployed networks is essential for understanding communication networks. Hence, data mining and statistical analysis of network data have been employed to determine traffic loads, analyze patterns of users’ behavior, \u00a0predict future network traffic, and detect traffic anomalies. The Internet has historically been prone to failures and attacks that significantly degrade its performance, affect the Internet connectivity, and cause routing disconnections. Frequent cases of various cyber threats have been encountered over the years and, hence, detection of anomalous behavior is a topic of great interest in cybersecurity. In described case studies, traffic traces collected by various collection sites are used to classify network anomalies. Various anomaly and intrusion detection approaches based on machine learning have been employed to analyze collected data. Deep learning, broad learning, gradient boosted decision trees, and reservoir computing algorithms were used to develop models based on collected datasets that contain Internet worms, viruses, power outages, ransomware events, router misconfigurations, Internet Protocol hijacks, and infrastructure failures in times of conflict. The reported results indicate that while performance of machine learning models greatly depends on the used datasets, they are viable tools for detecting the Internet anomalies.<\/p>\nProfessor Dr. Maria Virvou<\/h2>\n
University of Piraeus, Greece<\/p>\n
She previously served as Editor-in-Chief of SpringerPlus and currently serves as Associate Editor of Knowledge-Based Systems (Elsevier). Professor Virvou holds a Ph.D. in Artificial Intelligence from the University of Sussex, funded by a competitive scholarship in Artificial Intelligence from the Greek State Scholarships Foundation (IKY) following national examinations. She also holds an M.Sc. in Computer Science from University College London (UCL) and a Degree in Mathematics from the National and Kapodistrian University of Athens.
Her scholarly record includes over 400 publications and 8 monographs indexed in Scopus. She ranks 1st worldwide in Intelligent Software and Educational Software and 2nd in User Modeling, according to Scopus and Microsoft Academic Search. Professor Virvou has supervised 15 Ph.D. theses and 10 postdoctoral researchers, and is currently supervising 5 Ph.D. candidates. She has coordinated numerous national and international research projects and has received multiple distinctions, including a UNESCO award for her contributions to computer science. In recognition of her impact, Springer published Advances in Intelligent Healthcare Delivery and Management: Research Papers in Honour of Professor Maria Virvou. She is consistently listed among the top 2% of researchers worldwide in Artificial Intelligence, according to the global academic ranking by Stanford University.<\/p>\n
<\/span>Artificial Intelligence (AI) in education is undergoing a profound transformation, evolving from traditional knowledge-based reasoning toward advanced computational paradigms, including machine learning, deep learning, affective computing, adaptive hypermedia, and large language models (LLMs). As these technologies redefine teaching and learning, a central inquiry emerges: which technologies hold enduring pedagogical significance, how deep are their representational capabilities, and which functional mechanisms are most likely to transform the foundations of education itself?<\/span><\/p>\nNathan Clarke<\/strong><\/h3>\n
School of Engineering, Computing and Mathematics (Faculty of Science and Engineering), University of Plymouth<\/p>\n