Special Lecture Series

We study two-sided matching markets under count-based hereditary constraints, which extend beyond simple capacity limits and arise in applications such as diversity requirements and refugee resettlement after appropriate aggregation. In these settings, fairness and non-wastefulness are often incompatible, and existing approaches typically address this tension by prioritizing one property at the expense of the other. We take a different approach by relaxing both properties simultaneously using a single integer parameter. Specifically, we study envy-received up to k peers (ER-k) and non-wastefulness up to k objections (NW-k). Our main theoretical result establishes a tight compatibility frontier by showing that, for every integer k, ER-k and NW-k are always compatible under count-based hereditary constraints. We provide two equivalent polynomial-time algorithms to compute such matchings: a k-admissible cutoff algorithm and a k-admissible college-proposing deferred acceptance mechanism. Finally, experimental results demonstrate that even small relaxations achieve a favorable balance between fairness and non-wastefulness.

In April 2023, Dr. Zhaohong Sun joined the Kyushu University Multi-Agent Lab as a tenure-track Associate Professor, while simultaneously serving as a Research Scientist at CyberAgent AI Lab through the cross-appointment system. He also holds a Visiting Researcher position at the University of Tokyo Market Design Center (UTMD) and actively contributes to the ERATO Kojima Market Design Project under the direction of Prof. Fuhito Kojima. Dr. Sun received his Ph.D. in Computer Science from UNSW Sydney in September 2020, where he was supervised by Scientia Prof. Haris Aziz, Prof. Serge Gaspers, and Scientia Prof. Toby Walsh. During his doctoral studies (2016–2020), he was also affiliated with the Commonwealth Scientific and Industrial Research Organisation (CSIRO). Prior to his Ph.D., he obtained his master’s degree from Kyushu University (2014–2016) under the supervision of Distinguished Prof. Makoto Yokoo. He earned his bachelor’s degree from Dalian University of Technology in 2013.

Recent advances in artificial intelligence are transforming electricity markets and renewable energy systems. This talk presents industrial applications developed in collaboration with Axpo Iberia, including wind power forecasting systems based on numerical weather prediction, wake-effect modeling, and deep learning, battery energy storage system optimization, and AI-driven trading and decision support tools for electricity markets. The lecture also discusses emerging large language model (LLM) agents for customer demand forecasting and energy trading support under uncertain and volatile market conditions.

Prof. Antonio J. Romero Barrera is a Short-Term Quantitative Analyst at Axpo Iberia, Adjunct Professor at Comillas Pontifical University, and Ph.D. candidate at the University of Alcalá (Spain). His research focuses on artificial intelligence, renewable energy forecasting, wake-effect modeling, optimization, and electricity markets. He works on industrial AI applications for wind power forecasting, battery optimization, and AI-driven decision support systems for energy trading environments. He has authored journal articles and conference papers in AI and renewable energy, serves as a reviewer for several international journals, has completed more than 200 peer reviews according to Web of Science, and has received multiple national research and academic awards related to energy transition, sustainability, and innovation.

This lecture presents a perspective that extends the use of large language models (LLMs) from prompt design to process design, and discusses the reorganization of intellectual work in research. LLMs are used today for many tasks such as document generation, information retrieval and code generation, but mostly as support for individual tasks. Through concrete examples—handling e-mail, preparing materials, information gathering and task management—the lecture shows how to integrate LLMs into the entire research workflow. In particular, taking problems such as hallucination and sycophancy into account, it explains design principles for ensuring reliability, including "self-designed prompts" and built-in verification processes. It then presents design principles for research processes that integrate these elements and offers an outlook on research in the era of AI for Science. The aim is to provide practical insights so that researchers and practitioners treat LLMs not as mere tools but as objects of process design.

Shun Okuhara is a Lecturer at the Graduate School of Engineering, Mie University, and a Visiting Researcher at the Institute for Advanced Research, Nagoya University. He received his Ph.D. in Engineering from Nagoya Institute of Technology in 2020. He has been engaged in research and education at Kyoto University and Nagoya Institute of Technology, including collaborative research with educational technology and medical informatics. He conducted research abroad as a visiting professor at Carleton University, promoting international joint research. His specialties are multiagent systems, artificial intelligence and educational technology, with a focus on decision-making and consensus building among multiple actors (automated negotiation). Recently he has been building systems that guarantee the safety, explainability and agreement quality of decision protocols based on LLMs and constraints, and pursuing structural AI systems that support decision-making and governance in society beyond generative AI.

Recent advancements in artificial intelligence (AI) and sensing technologies are transforming healthcare through non-invasive, scalable, and interpretable diagnostic systems. Two emerging approaches—LLM-driven Alzheimer’s detection and radar-based fall detection—demonstrate how intelligent systems can improve early diagnosis and safety monitoring for aging populations. The LLM-driven Alzheimer’s detection framework leverages spontaneous speech as a low-cost, non-invasive biomarker for cognitive decline. Traditional diagnostic methods like neuroimaging and cerebrospinal fluid analysis are invasive and costly, whereas linguistic and paralinguistic patterns can reveal subtle signs of early Alzheimer’s disease (AD). The proposed Symptom- Informed Sequence Enhancement (SISE) framework enhances weak pathological cues by detecting linguistic anomalies and integrating them into large language model embeddings. Using the DementiaNet_Text dataset, this approach improves accuracy and interpretability, bridging linguistic biomarkers with clinical insights for preclinical AD detection. Complementing this cognitive assessment approach, radar-based fall detection focuses on physical safety and monitoring. Falls represent a major health risk for older adults, and radar sensing offers a privacy-preserving, contact-free solution. Utilizing 24 GHz millimeter-wave radar, human activity patterns—including falls—can be reliably distinguished through speed and acceleration analysis. To address coverage limitations, metasurface-based reflective surfaces have been developed, enabling beam splitting and reducing blind spots. This innovation improves spatial coverage and enhances radar-assisted sensing within 5G and 6G communication frameworks. Together, these technologies represent a unified vision for intelligent healthcare: integrating language-based cognitive diagnostics with radar-assisted physical monitoring. The combination of LLM-driven analysis and advanced sensing systems supports continuous, non-invasive observation of neurological and physical health, paving the way for AI-empowered, privacy- conscious digital healthcare solutions that enhance both diagnostic precision and patient safety.

Dr. George Xiao is a Principal Research Officer at the National Research Council Canada and an adjunct professor at Carleton University and the University of British Columbia Okanagan. An IEEE Fellow (2015), Fellow of the Engineering Institute of Canada (2023), and the Canadian Academy of Engineering (2024), he has over 40 years of experience in instrumentation and measurement technologies. His innovations span fiber optic sensors, radar systems, ePassport technology, and stretchable human–machine interfaces. Dr. Xiao has received numerous awards, including the 2018 Career Excellence Award and 2014 Technical Award from IEEE Instrumentation and Measurement Society, a 2022 Gold Edison Award, a 2022 R&D 10 Award, and NRC Technology to Market Awards. He also served as the Editor-in-Chief of the IEEE Journal of RFID.

Ling Bai is currently a Lecturer and Research Fellow at the University of British Columbia. She received her PhD in Computer Science and Technology in 2022. Her research interests include AI-driven models for Intelligent Sensing, Diagnostics, and Prognostics; Intelligent Cyber- Physical Systems; Digital Twins; Human–Computer Interaction; and Cognitive Science, with applications in autonomous driving, infrastructure monitoring, industrial manufacturing, smart cities, and healthcare．

In the course of the talk we will first introduce the concept of Knowledge Graphs (KGs) and it’s evolution and (current) applications, and then provide some mixed outlook on own research and trends where KGs are usable in Hybrid (Neuro-Symbolic, Agentic) AI applications and systems to power Data Understanding and Data management  As an application case, I will mainly refer to open, collaboratively edited Knowledge Graphs such as Wikidata.

Axel Polleres heads the Department of Information Systems and Operations Management, and leads the Data Management group at the Institute for Data, Process and Knowledge Management of Vienna University of Economics and Business (WU Wien). He joined WU Wien in Sept 2013 as a full professor in the area of "Data and Knowledge Engineering". Since January 2017 he is also a member of the Complexity Science Hub Vienna faculty. Between January and June 2018, he has been appointed as visitng professor at Stanford University under the Distinguished Visiting Austrian Chair Professors program hosted by The Europe Center in the Freeman Spogli Institute for International Studies at Stanford. He obtained his Ph.D. and habilitation from Vienna University of Technology and worked at University of Innsbruck, Austria, Universidad Rey Juan Carlos, Madrid, Spain, the Digital Enterprise Research Institute (DERI) at the National University of Ireland, Galway, and for Siemens AG's Corporate Technology Research division before joining WU Wien. His research focuses on querying and reasoning about ontologies, rules languages, logic programming, configuration technologies, Semantic Web technologies, Web services, Knowledge Management, Linked Open Data, Knowledge Graphs and their applications. He has worked in several European and national research projects in these areas. Axel has published more than 200 articles in journals, books, and conference and workshop contributions and co-organised several international conferences and workshops in the areas of logic programming, Semantic Web, data management, Web services and related topics and acts/acted as editorial board member for several journals. Moreover, he actively contributed to international standardisation efforts within the World Wide Web Consortium (W3C) where he co-chaired the W3C SPARQL working group.

Recommender systems and social networks are often faulted to be the cause for creating Echo Chambers – environments where people mostly encounter news that match their previous choices or those that are popular among similar users, resulting in their isolation inside familiar but insulated information silos. Echo chambers, in turn, have been attributed to be one cause for the polarization of society, which leads to the increased difficulty to promote tolerance, build consensus, and forge compromises. To escape these echo chambers, we propose to change the focus of recommender systems from optimizing prediction accuracy only to considering measures for social cohesion.

This proposition raises questions in three spheres: In the technical sphere, we need to investigate how to build “socially considerate” recommender systems. To that end, we develop a novel recommendation framework with the goal of improving information diversity using a modified random walk exploration of the user-item graph. In the social sphere, we need to investigate if the adapted recommender systems have the desired effect. To that end, we present an empirical pilot study that exposed users to various sets (some diverse) of news with surprising results. Finally, in the normative sphere, these studies raise the question what kind of diversity is desirable for the functioning of democracy.

Reflecting the consequences of these findings for our discipline, this talk highlights that computer science needs to increasingly engage with both the social and normative challenges of our work, possibly producing a new understanding of our discipline. It proposes similar consequences for other disciplines in that they increasingly need to embrace all three spheres.

Abraham Bernstein is a Full Professor of Informatics at the University of Zurich (UZH), Switzerland. His current research focuses on various aspects of the semantic web, recommender systems, data mining/machine learning, crowd computing, and collective intelligence. His work is based on both social science (organizational psychology/sociology/economics) and technical (computer science, artificial intelligence) foundations.

Mr. Bernstein is also a founding Director of the University of Zurich’s Digital Society Initiative (DSI) — a university-wide initiative with more than 180 faculty members investigating all aspects of the interplay between society and the digitalization.

Prior to joining the University of Zurich, Mr. Bernstein was on the faculty at New York University and also worked in industry. Mr. Bernstein is a Ph.D. from MIT and holds a Diploma in Computer Science from the Swiss Federal Institute in Zurich (ETH).

This talk will introduce the main challenges facing widespread adoption of automated negotiation in society with a specific focus on supply chain management as a main application domain. We will describe the SCM competition, its design, goals and current status. Some of the most recent advances related to this area including application of RL and MARL technology, interplay with LLM-based agentic systems and our recent efforts in bringing this technology to the industry will also be discussed.

Yasser is a Research Scientist at the Data Science Laboratories, NEC based in Tokyo, Japan. He is also a Professor of intelligent systems at Assiut University, Egypt and a visiting researcher at the National Institute for Advanced Industrial Science and Technology, Japan. He received his PhD in 2009 from Kyoto University. Since then, he worked in the areas of robotics, human-robot interaction, action recognition, emotion recognition and more recently in multiagent systems with a focus on automated negotiation and agreement technologies.

I introduce a decade-long research of online / offline citizen dialogue in Japan or between Japanese and non-Japanese societies on the themes of nuclear waste management or Sustainable Development Goals (SDGs). We organized seven offline / online citizen dialogue in a large city, a nuclear power plant (NPP)-hosting city, and a Fukushima Daiichi nuclear power plant accident-affected city in Japan, or by connecting the NPP-hosting city and the large city, or the Fukushima-affected city and the large city, to learn and discuss nuclear waste management, through random sampling invitation. The attitude for dialogue was measured empirically before and after dialogue, and the attitudinal change was examined. As a global extension of this domestic research and development, we also conducted an international online citizen dialogue between Finland and Japan, as well as between Taiwan and Japan, on the themes of SDGs, including gender equality, biodiversity conservation, and citizen participation for sustainability transformation. The method of reflecting, originally developed through mental health and social welfare activities in Norway and Finland, was applied to facilitate dialogue and to stimulate self-awareness of embedded cultural context. The indicators of boundary of collaboration (trust) and of awareness of own cultural inheritance, were developed and measured before and after dialogue. Attitudinal changes (effect size) of these indicators, in addition to that of attitude for dialogue, were examined. By combining citizen dialogue with digital technology and communication platforms, we could facilitate building dialogic relationships across and within cultural boundaries, contributing to capacity building toward sustainability transformation.

Hidenori Nakamura is an associate professor of environmental policy, Faculty of Engineering, Toyama Prefectural University, Japan. He holds a doctoral degree from Department of Social Engineering, Graduate School of Decision Science and Technology, Tokyo Institute of Technology, Japan. After graduating from the University of Tokyo Graduate School of Science with a master’s degree in Earth and Planetary Physics in 1997, he joined Japan International Cooperation Agency (JICA) and engaged in international development work.  After working in management consulting, he completed a master program in Economic and Political Development at Columbia University’s Graduate School of International and Public Affairs in 2005. In the same year, he interned at the United Nations Children's Fund Uganda Office. He has worked at the Institute for Global Environmental Strategies and Nagoya University's Graduate School of Environmental Studies before assuming his current position.

In recent years, the Web3 economy, built upon blockchain technology, has been rapidly expanding, attracting attention as a new foundation for economic activity across diverse fields such as finance, gaming, and digital art. At the same time, serious challenges have emerged, including massive financial losses due to cryptocurrency hacks, the abuse of anonymous social media platforms for scams such as phishing, investment fraud, and romance scams, as well as rampant market manipulation through pump-and-dump schemes and wash trading. Although cryptocurrency users are aware of the risks of hacks and fraud, there remains a lack of well-developed platforms for sharing knowledge and support systems for risk mitigation. As a result, isolated trading environments have become prime targets for malicious actors. To address this situation, it is necessary to explore mechanisms that reduce vulnerabilities to such attacks and enable safe and secure economic activity. Specifically, we discuss an approach based on three key pillars: (1) anomaly detection in cryptocurrency price movements and transactions, (2) suppression of fraudulent or abnormal transactions through AI agent-assisted transaction submissions and democratic consensus among users, and (3) control of transaction approval queues.

Dr. Akihiro Fujihara received a Doctor of Science degree from Yokohama City University in 2006. He is currently a Full Professor in the Department and Graduate School of Information and Communication Systems Engineering, Chiba Institute of Technology. He is a member of the IEEE and IEICE. He has authored or co-authored over 70 publications of research papers on stochastic processes, human mobility and communication behavior patterns, IoT, and blockchain. His recent research interests include blockchain interoperability and applications of blockchain technology to smart city services. He was a recipient of the IEEE COMPSAC Best Paper Award of 2014. He served as a guest associate editor for Frontiers in Blockchain. He also serves a technical committee on Information Network (IN) and Communication Quality (CQ) study groups of IEICE Communications Society.

It is critical to transform the aged-care ecosystem into senior-centric community-based care.

However, solutions that monitor activities of daily living (ADLs), to facilitate community-based aging in place, are limited. In this presentation, motivations for aging in place for elders in Canada will be briefly summarized. Furthermore, deep learning based non-intrusive load monitoring (NILM) will be introduced and the most recent progress towards NILM assisted residents-centric energy management for smart homes will be discussed. Lastly, NILM integrated daily activity monitoring method for elderly people will be discussed in this seminar.

Dr. Shichao Liu is now an Associate Professor in the Department of Electronics at Carleton

University, Ottawa, Ontario, Canada. Dr. Shichao Liu received his Ph.D. degree from the Carleton University, Ottawa, ON, Canada, in 2014. He was the winner for the 2010 Carleton President’s Doctoral Fellowship. Dr. Liu is the primary investigator of several NSERC and Mitacs Accelerate Grants. He won the John R. Evans Leaders Fund from Canada Foundation of Innovation and Research Achievement Award at Carleton University, Faculty of Engineering and Design. He also won the Best Paper Runner-Up Award in IEEE EPEC 2022 and Best Conference Paper Finalist in IEEE ICMA 2024.

Dr. Liu is currently an Associate Editor of IEEE Transactions on Network and Science Engineering, IEEE Transactions on Industrial Cyber-Physical Systems, an Associate Editor of IEEE Access, an Associate Editor of International Journal of Robotics and Automation, an Associate Editor of Frontiers in Control Engineering and in the Editorial Board of Smart Cities. Dr. Liu is a guest editor of IEEE Transactions on Industrial Applications. He is the Vice Chair of IEEE Industrial Electronics Society Technical Committee on Resilience and Security in Industrial Applications. He is the Tutorial Chair for IEEE IES ICIT 2026 and he was the Final Chair of IEEE EPEC 2022.

Recent research shows that, if given the choice, nearly all older Canadians would prefer to age in place within their own homes and communities. The National Research Council of Canada’s Aging in Place Challenge program aims to support this choice through technology and innovation. Launched in April 2021 with a 7-year mandate, the program's objectives focus on improving the quality of life of older adults and their personal caregivers through innovation for safe and healthy aging in support of a sustainable model for long-term care that shifts the focus toward preventive home and community-based care.

Since its launch in 2021, the program has supported several funded collaborative R&D projects and initiatives, and continues to do so. The presentation will feature how the program is structured and works with Canadian and international partners from communities, industry, academia, health and social care organizations, government and other interested parties to conduct research with the greatest impact and aligned with the program's areas of focus:

Preventing transitions in care

Enabling people to continue living well

Creating age-friendly communities

Examples of projects and generated knowledge will be given.

With over 25 years of experience in research, development, and innovation, Patricia

Debergue has dedicated her career to advancing technology for the public good. The

program she currently leads is part of NRC’s Life Sciences division and supports the

choice of older Canadians to age in place within their homes and communities, through

technology and innovation. In that role, she is focusing on enabling researchers and the

Canadian industries to develop and implement solutions and technologies that prevent

transitions in care, enable people to continue living well with frailty and ill-health and

create age-friendly environments. Before becoming program director in April 2022, she

was leading a research section in digital health at the NRC’s Medical Devices Research

Centre where she had been conducting research in the field of digital health, virtual care

and real-time surgical training simulation for over 20 years done in collaboration with

clinicians, industry and other government departments. She has expertise in virtual

care, cognitive care, digital health and therapeutics, human-computer interaction, real-

time surgical simulation and finite element modelling.

Her latest research work has included leading a project focusing on the development of

a cognitive care software platform combining AR/VR capabilities as well as bio-/neuro-

sensing and targeting assessment and remediation of a variety of cognitive dysfunctions

common to mental health disorders.

Patricia Debergue obtained her Bachelor degree from the Université de Technologie de

Compiègne in France and her master’s degree from Sherbrooke University in Canada,

both in mechanical engineering.

Digital governance tools are proliferating on all levels of government. They are promoted not only by UN organisations as a means to reach their sustainable development goals (SDG) but also as an efficient way to increase the general well-being, the sense of belonging and to receive feedback on their policies from the wisdom of the crowd. Among such tools of co-creation as an exchange between public administrations, parliaments and the general public, online petitions are becoming more popular lately, in particular on the local level in large urban areas. The design of online petition platforms can vary greatly regarding their consequentiality. However, as they become more popular, their operation tends to create an increased workload for the operators. In this talk, I will showcase how machine learning and natural language processing can be used to increase convenience for users of such systems and making them more efficient for the public administrators on the other hand as well

Prof. Serdült has held a dual appointment since April 2017, serving as a professor at Ritsumeikan University, College of Information Science and Engineering, while continuing as Principal Investigator at the Centre for Democracy Studies Aarau (ZDA), University of Zurich. As of April 2024, he also leads the Digital Governance Systems Lab at Ritsumeikan.

His interdisciplinary research focuses on e-participation (digital platforms for citizen engagement) and e-government (tools for enhancing administrative transparency and deliberation). He has held teaching and research positions at ETH Zurich, University of Zurich, and University of Geneva, with visiting roles in Hungary, Austria, Poland, the USA, and Japan (Ritsumeikan and Waseda). In 2024–2025, he will serve as a Guest Professor at the University for Continuing Education Krems, Austria.

This guest lecture will provide a theoretical framework for understanding trust and distrust in technology, exploring its roots and interactions. It will further exemplify this complex interaction by providing examples related to the use of internet voting, a critical technology for its political dimension, extracted from some selected cases approached in the Electrust project.

David Duenas-Cid is an Associate Professor and Director of the Public Sector Data-Driven Technologies (Pub-Tech) Research Center at Kozminski University (Poland). His research is connected with Trust/Distrust and Digital Democracy. He is currently researching the processes of creating Trust and Distrust in Internet Voting and his research has been showcased at the European Commission scientific podcast CORDIScovery.　He is the president of the Thematic Group on Digital Sociology at the International Sociological Association, General Chair at the E-Vote-ID Conference, Program Chair at the Annual International Conference on Digital Government Research, Track Chair at Hawaii International Conference on System Sciences and Academic Editor at Internet Policy Review.

Many companies expand their business into adjacent spaces next to their core business area. Entering a white space—satisfying the needs of existing or new customers in fundamentally different ways, in an area far from the adjacent spaces—is considered high-risk. This lecture outlines the four elements of a business model—customer value proposition, profit formula, key resources and key processes—and their characteristics. Through exercises, participants learn methods for evaluating differentiation of products and services, of access and of payment schemes, and methods for discovering new businesses. Case studies of companies that achieved business-model innovation are also introduced.

Tokuro Matsuo received his Ph.D. in Engineering from Nagoya Institute of Technology in 2006. He was Associate Professor at Yamagata University (2006–2012) and has been Professor at the Advanced Institute of Industrial Technology since 2012. He has held positions including Visiting Researcher at the University of California, Irvine (2010–2011), SEITI Research Fellow at Central Michigan University (2010–2018), Project Professor at Nagoya Institute of Technology (2011–2014, 2015–2021), Visiting Researcher at Shanghai University (2011–2013), Visiting Professor at Bina Nusantara University (2015–), Visiting Professor at the University of Nevada, Las Vegas (2016–2017), Invited Professor at City University of Macau (2018–2020), Professor at Asia University, Taiwan (2020–2022), Visiting Professor at Nagoya Institute of Technology (2020–2022), Vice President of the Software Engineering Research Foundation, USA (2013–2018), Director of the International Association for Computer and Information Science (2015–2020, Vice President 2016–2017), President of the International Institute of Applied Informatics (2012–), Visiting Professor at Sam Houston State University (2024–), JNTO International MICE Ambassador (2016–), Kumamoto City MICE Ambassador (lifetime) and Regional Informatization Advisor of the Ministry of Internal Affairs and Communications (2008–2017). He has received more than 20 awards, including the JNTO International Conference Attraction and Contribution Award (FY2011). His specialties include applied informatics, intelligent informatics, materials informatics, event tourism, convention and event business practice and MICE management. He is a professional event planner and producer, with more than 200 keynote and invited talks, more than 10 books (Springer and others), more than 100 journal papers and more than 200 conference papers.

This talk explores subjective experience within technical environments offering sensorially and cognitively enhanced human agency. Inspired by Merleau-Ponty’s concept of ‘flesh’, it seeks to understand how neuro-somatic devices, location-aware tracking systems, machine learning and predictive algorithms reshape the nature of consciousness and phenomenal experience. Traditionally passive sites of human activity such as stores, automobiles, public transport, workplaces, and museums (to name a few) are transforming into data-intensive environments of actuation that create new forms of awareness, cognition, and agency. Some examples include neuromarketing strategies that deploy digital signage in order to influence individual with personalized recommendations based on a customer’s purchase history (Curran, 2023), in-cabin systems that advise drivers on safety suggestions based on their cognitive/affective state (Mishra et al., 2022), and museums offering customized generative AI art experiences to visitors based on their neurological and physiological responses (Kung, 2024). My talk asks to what degree are experiential qualities in these environments now pre-determined by bioinformational technologies, rather than being shaped by external experiences? How will human consciousness and subjective experience be altered by bioinformational technologies with which they are entangling ever deeper? In elucidating the phenomenology of subjective experience in the age of bioinformational environments, I consider the peripheral, micro-temporal, and pre-perceptual modality in which these technical systems cooperate with the cognitive nonconscious (Hansen 2015; Hayles 2017) before they channel forward insights into the high order consciousness of knowing subjects. Thus, as part of a longer sociotechnical continuum in the technical exteriorization of cognition and affect, the rise of Bioinformational technological spaces move us closer towards what Floridi (1999) calls ‘inforgs’.

Dr. Peter Mantello is a Professor of Media Studies at Ritsumeikan Asia Pacific University in Japan. For the past seventeen years his research has examined the complex and multi-faceted intersection between technology, media and society. He has translated his previous career as an award-winning independent filmmaker, photographer and interactive media producer into his ongoing intellectual exploration of the various confluences between emerging technology and modern-day society.  With funding from the Japan Society for the Promotion of Science (JSPS) and the Japan Science and Technology (JST) he has studied, (i) the militarization of play through an empirical study of US military-sponsored video games and virtual reality exhibitions in the US; (ii) synergies and feedback loops between hyper-consumerism, popular culture and post 9/11 security practices; (iii) the aestheticization of extremist violence through social media artifacts such as memes, hashtags, and selfies; (iv) the temporal shifts in law enforcement due to the rise of algorithmic policing; and, (v) the cross-cultural implications of emotional AI in the UK and Japan. Currently, he is involved in an international collaboration with RWTH Aachen University, Kate Hamburger Kollge that examines comparatively emotional AI in the German and Japanese workplace.  He has authored dozens of articles in leading journals on the impact of emotional AI in various sectors of society, including the workplace. Over the past 15 years he has supervised  14 Masters students and 4 PhD students.

Social media plays an important role in disseminating information and can influence real-world outcomes such as elections. Measuring the impact of certain events on social media presents challenges for social scientists.

First, social media data needs to be more relaxed since social events often involve complex and multifaceted issues, making it difficult to isolate and measure the impact of specific interventions or communications. In addition, various social, economic, and environmental factors can obscure the direct effects of social media communications on particular events.

Second, there needs to be a universal set of metrics for measuring the impact of social media communications.

Third, even though some AI and machine learning methods can capture some potential impact, demonstrating the validity of these results remains a challenge. Despite technological advancements, questions persist about whether they contribute significant knowledge or generate incremental insights.

Based on current research, I aim to address these challenging questions.

Dr. Loni Hagen is an Associate Professor of Data Science at the University of South Florida's School of Information. She earned her Ph.D. in Information Science from the University at Albany, SUNY, and has over a decade of experience in various law enforcement roles in Korea, including as an e-government specialist at the Korean National Police Agency.

Her research explores the application of artificial intelligence and big data in social science and government decision-making, with interests in human-centered data science, social media, cybersecurity, public health crises, and data science education. Her work has been supported by the National Research Foundation of Korea and the Florida Center for Cybersecurity. Dr. Hagen has also served in leadership roles for international conferences and is a member of the Digital Government Society and the American Society for Information Science and Technology. She was awarded a Fulbright U.S. Scholar grant for 2024-2025 to research human supervision of artificial intelligence at the University of Tsukuba in Japan. She received her Master's degree from the University of Tokyo, Japan, in 2004.

In this seminar professor Andersen will present the thinking and rationales behind the thesis of decision structure and information processing as the essential elements of effective adaptive social systems including organizations and societies. The presentation will be framed in the context of mounting requirements for adaptive capabilities in modern societies as we face increasingly turbulent and abrupt environmental conditions with potentially extreme outcomes.

There will be room for discussion and exchange of views and ideas.

Torben Juul Andersen is professor of Strategy and International Management and director of the Global Strategic Responsiveness Initiative at the Copenhagen Business (CBS) in Denmark. Before joining CBS, he taught strategy and financial economics at George Mason University and Johns Hopkins University in the United States. Torben previously held executive positions at Citibank, N.A., Citicorp Investment Bank Ltd., SDS Securities a/s, Unibank A/S, and PHB Hagler Bailly. He is an Honorary Fellow of the Institute of Risk Management (IRM) in London and holds an MSc. Economics (cand. polit.) degree from the University of Copenhagen, an MBA from McGill University, and a PhD from the University of North Carolina at Chapel Hill. His primary focus includes effective strategic response capabilities and resilient adaptation in the face of emergent risks, uncertain environmental conditions, and abrupt unpredictable events with potentially extreme outcomes that characterize the major societal challenges of our time.