Dr. Yifei Dong is a Research Fellow at the Data Science Institute , University of Technology Sydney, with expertise in LLM-assisted agent systems , explainable AI , and multimodal artificial intelligence . Holding a PhD in Computer Science from UNSW Sydney and over a decade of fintech industry experience including CTO roles, he has secured $2.4 million in competitive funding for AI solutions bridging academia and real-world applications in healthcare, education, and finance. Education : PhD in Computer Science from UNSW Sydney Current Role : Research Fellow at UTS Data Science Institute (2023–present) Past Academic Appointments : Lecturer at Southern Cross University and Western Sydney University Dr. Dong’s research focuses on making AI systems transparent and socially beneficial , with contributions to adversarial AI, trustworthy digital societies, and wireless sensor networks. His recent work includes: Developing AICAttack (2025) for adversarial image captioning attacks Creating the QMAD fairness metric (2025) for dynamic environments Advancing explainable ECG diagnosis systems (2025) via multimodal LLMs As a scientific awardee (2025 RegTech Social Impact of the Year), he has pioneered AI solutions for vulnerable populations, such as NDSI participants through the "My Complaint Assistant" tool. His supervision of PhD and Honours students emphasizes technical rigor and ethical responsibility.
Olivier Verscheure serves as the Executive Director of the Swiss Data Science Center (SDSC), a national R&D center organizationally hosted by both École Polytechnique Fédérale de Lausanne (EPFL) and ETH Zurich. He also holds multiple Adjunct Professor appointments at EPFL, specifically within the School of Computer and Communication Sciences (SIN and SSC) and the School of Engineering (SEL). His educational background includes: Ph.D. in Computer Science from École Polytechnique Fédérale de Lausanne (EPFL), June 1999 Verscheure's research focuses on the intersection of data science and real-world applications. His work centers on stream and big data mining, geospatial analysis, and large-scale data management. These technical capabilities are applied across diverse domains including personalized health and medicine, Intelligent Transportation Systems, telecommunications, smart building technologies, Smart Grid infrastructure, healthcare analytics, and waste water management systems. His approach emphasizes creating practical data science solutions that address complex challenges in these sectors while considering the constraints of real-world deployment. An analysis of his recent publication record reveals a strong focus on real-time data processing and analytics, particularly for transportation and urban systems. His work frequently addresses challenges in handling massive time series data, developing efficient architectures for low-latency analytics, and creating practical applications for smart city infrastructure. There's a clear progression from theoretical data science contributions to production-ready systems that can process billions of data points daily, demonstrating his ability to bridge research and practical implementation. His notable achievements include: Two IBM Outstanding Technical Achievement Awards Best Paper Award for his research Student Best Paper Award Verscheure has substantial experience in research leadership and mentoring. During his tenure at IBM, he managed the Exploratory Stream Analytics research group and led a technical and management team of approximately 40 people at the IBM Research lab in Ireland. He has served on PhD committees at major universities and published nearly 100 research papers that have garnered over 2,400 citations. His work has resulted in more than 40 US and international patents, demonstrating both academic and practical impact. As Executive Director of the Swiss Data Science Center, Verscheure oversees a distributed multi-disciplinary team working across domains including personalized health, transportation, earth and environmental science, social science and digital humanities, and economics. The center aims to federate data providers, data and computer scientists, and subject-matter experts around a cutting-edge analytics platform while addressing security and privacy issues. Under his leadership, the SDSC develops embedded data science support, offers end-to-end data science services, and fosters a community to share tools and knowledge in data science.
Professor Harald Kosch serves as Vice President for Academic Infrastructure and IT at the University of Passau. Since 2006, he holds the Chair of Distributed Information Systems within the Faculty of Computer Science and Mathematics. He co-leads the tri-national IRIXYS research center (University of Passau, INSA Lyon, Università di Milano) and directs the German-French DFH/UFA Doctoral College. Current academic leadership roles Specialization in distributed systems and big data International research networks in digital innovation Recipient of French academic distinction His team develops tools for distributed information processing in multimedia and data-intensive applications, with applications in eHumanities and emergency logistics. Research emphasizes cross-border collaboration and technology transfer between academia and industry. Scientific distinctions include: Chevalier de l'Ordre des Palmes Académiques 2022 DFH Dissertation Prize (for Dr. Benjamin Planche) Active in EU digital strategy, Bavarian sustainability initiatives, and Franco-Bavarian AI competitions.
Cristina Emma Margherita Rottondi is an Associate Professor at the Department of Electronics and Telecommunications (DET) of Politecnico di Torino . She is a member of the Photonext Interdepartmental Center and contributes to research in telecommunications, computer music, and network optimization. Her work spans privacy-preserving protocols, smart grid communication, and low-latency audio streaming. Research Interests : Networked Music Performance, Optical Networks, Smart Grid Privacy, Machine Learning. Education : Not explicitly listed. Research Areas include: Smart Grid Privacy : Developing secure protocols for data aggregation and distributed energy optimization. Optical Network Design : Investigating machine learning-driven solutions and spatial division multiplexing. Networked Music Performance : Addressing latency and inclusivity in remote musical collaboration. Publication Trends highlight interdisciplinary work at the intersection of telecommunications , machine learning , and music technology . Recent articles focus on privacy-preserving smart grids , 5G-enabled musical IoT , and UDP packet trace datasets . Scientific Awards : 2020 Charles Kao Award Best Paper Awards at IEEE Online Greencomm (2014), DRCN (2017), and others N2Women Rising Star (2020) Advising includes PhD candidates working on networked music performance , accessible musical education , and medical wearable devices . She has contributed to national patents for inclusive audio hardware.
Maria Wirzberger is a tenure-track professor at the University of Stuttgart, where she leads the Department of Teaching and Learning with Intelligent Systems and serves as spokesperson for the Stuttgart Research Focus "Interchange Forum for Reflecting on Intelligent Systems" (SRF IRIS). She is also co-director of the Artificial Intelligence Software Academy (AISA) and part of the Cyber Valley ecosystem, Europe’s largest AI research consortium. Her interdisciplinary research bridges cognitive psychology, human-computer interaction, instructional design, and artificial intelligence. Research Interests: Modeling and simulation of human cognition using cognitive and connectionist approaches Advanced statistical modeling (multilevel models, time series, structural equation models) Distraction, interruption, and attention control in applied settings Multimodal cognitive load assessment via performance, behavior, and physiology Design of adaptive, assistive, and responsible intelligent systems Individual differences in HCI (age, neurodiversity, technology affinity) Trust, bias, and critical reflection in human-AI interaction Her recent publications focus on cognitive load in learning, system delays in virtual training, interruption modeling using ACT-R, and adaptive feedback mechanisms. The work spans experimental, computational, and applied domains, increasingly extending to digital health, industrial robotics, and single-pilot operations. She applies lab, field, and online studies combined with user-centered software development. Scientific Awards and Grants: Erasmus+ teaching mobility grant (2018) InProTUC travel grant (2016) Research stay grant at University of Groningen "Deutschlandstipendium" scholarship (2013/14) Maria Wirzberger has supervised several theses and has been involved in grant-funded research such as the DFG Research Training Group "CrossWorlds". She actively reviews for top journals and conferences and holds leadership roles in academic service. She has organized symposia and workshops and is a member of numerous professional societies including the Cognitive Science Society, ACM, and APA. Labs and Teams: She leads a research team at the University of Stuttgart focused on teaching and learning with intelligent systems, embedded within the interdisciplinary Cyber Valley network. Her group develops and evaluates user-adaptive technologies using predictive analytics and cognitive science principles.
Dr. Yangyang Shu serves as an Associate Lecturer in the School of Systems and Computing at the University of New South Wales (UNSW) Canberra campus. Previously, he held a Research Fellow position at the Australian Institute for Machine Learning (AIML) at the University of Adelaide, where he contributed to the Centre for Augmented Reasoning (CAR) project. His academic foundation includes a Ph.D. in Computer Science from the University of Technology Sydney. His research spans cutting-edge areas in artificial intelligence, with core expertise in Machine Learning and Computer Vision . Key focus areas include: Low-supervised paradigms (Weakly/Semi-/Self-Supervised Learning) Rationale-Guided Machine Learning systems Generative AI applications Machine Learning in Music and affective computing Fine-grained visual recognition in data-scarce environments Dr. Shu's publication record demonstrates consistent contributions to top-tier venues including CVPR, ECCV, IEEE Transactions on Multimedia, and Pattern Recognition. His work bridges theoretical advances in learning with practical applications in photo aesthetic assessment, semantic segmentation, and emotion recognition, showing particular strength in developing methods for low-data regimes and leveraging privileged information. He maintains active research collaborations through the School of Systems and Computing at UNSW Canberra, building on prior affiliations with AIML and the Centre for Augmented Reasoning. His current teaching responsibilities include ZEIT 2103 (Data Structures and Representation) for Semester 1, 2025.
Marte Blikstad-Balas is a Professor at the Department of Teacher Education and School Research (ILS) within the Faculty of Educational Sciences at the University of Oslo. Her research focuses on literacy, reading, writing, digital media, and classroom practices, with particular attention to Norwegian didactics and video-based methodologies. PhD in Educational Sciences (University of Oslo): Redefining School Literacy. Prominent literacy practices across subjects in upper secondary school Master's in Language and Cultural Studies Didactics (Hedmark University College) General Teacher Education (Hedmark University College) Her work explores the intersection of technology and education, examining digital competencies, screen vs. paper reading, and critical literacy in classrooms. She leads major projects like QUINT Theme 3 (video-supported teacher learning) and the Nordic Center of Excellence - Quality in Nordic Teaching as Deputy Head. She serves as Editor-in-Chief for the Nordic Journal of Literacy Research and leads the European Educational Research Association Network 27 Didactics . Recent publications highlight her expertise in video-based teacher development (2024), digital literacy challenges (2025), and Norwegian classroom practices (2024). As a member of the Academy for Young Researchers (2017-2021), she contributes to advancing educational research in Scandinavia. Key research areas: Literacy, Digital Media in Education, Classroom Research, Teaching Methodology, Norwegian Didactics Projects: QUINT, VIST, LISA, SISCO
Jussara M. Almeida is an established computer science researcher specializing in social network analysis, misinformation detection, and human mobility modeling. Her extensive publication record (1996–2025) demonstrates active research in web science, political communication on messaging platforms (WhatsApp/Telegram), and cloud systems. She frequently collaborates with Brazilian institutions and international partners on large-scale data projects. Research Focus: Her core interests include: Modeling information diffusion in encrypted messaging apps (WhatsApp/Telegram) Predicting human mobility patterns and privacy implications Analyzing political discourse and election-related coordination online Developing computational methods for misinformation detection Optimizing cloud/edge computing performance Publication Trends: Recent work (2021-2025) shows intensified focus on: Telegram's role in political mobilization and information dissemination Advanced techniques for identifying fake news websites and image-based misinformation Privacy-preserving mobility analysis and edge computing Child safety in live-streaming platforms Collaborations & Impact: Key collaborators include Marcos André Gonçalves, Fabrício Benevenuto, and Marco Mellia. Her research provides critical insights into real-world problems like election integrity, platform governance, and user privacy.
Michael O'Donoghue is a Lecturer in Education at the Manchester Institute of Education, University of Manchester. He serves as the Humanities New Academics Programme (HNAP) coordinator and Programme Director for the PG Certificate in Higher Education. His work focuses on innovation in teaching and learning, particularly through video-based educational tools. Research interests include pedagogical strategies in higher education, digital training methodologies, and the integration of multimedia resources into curriculum design. His 2011 conference paper on Designing Video for Teaching and Learning highlights his expertise in educational video production and learning management systems. Michael contributes to the United Nations Sustainable Development Goals (SDGs) through advancements in higher education (SDG 4). He is part of a research network exploring topics like educational video, case studies, and learning processes.
Mohammad Swash is a Lecturer in Digital Media at the Brunel Design School within the College of Engineering, Design and Physical Sciences at Brunel University London. He joined the university in 2013 after completing his Ph.D. in Holoscopic 3D Imaging Systems at the same institution, establishing himself as a leading researcher in advanced imaging technologies. Dr Swash's extensive research portfolio spans multiple disciplines with a primary focus on 3D imaging and display systems. His work encompasses holoscopic and multiview 3D technologies, including cameras, processing techniques, and display systems. He has made significant contributions to 3D virtual reality, augmented reality, computer vision, and medical image processing. His research in human-computer interaction design and serious gaming demonstrates practical applications of theoretical concepts across various industries. Analysis of his recent publications (2021-2024) reveals a clear trend toward applying 3D imaging technologies to solve real-world problems in autonomous vehicles, medical applications, and construction. His work increasingly integrates AI and machine learning techniques with 3D imaging systems, particularly in gesture recognition and depth estimation. This interdisciplinary approach bridges theoretical computer vision research with practical implementations across multiple sectors. Dr Swash has earned significant recognition for his scholarly contributions: Prestigious Vice-Chancellor's Doctoral Research Prize, Brunel University London IEEE Broadcast Technology Society 2013 Best Paper Award First BEST poster presentation prize, Brunel Research Students Conference, ReSCon09, 2009 Chancellor prize and the Medal for excellence achievement, Brunel University London, 2008 Graham Hawkes prize for the best final year project, Brunel University London, 2008 Best UG Project Award in Electronic and Computer Engineering, Brunel University London, 2009 His research funding includes major projects such as the Internet of Radio-Light (funded by the European Commission, 2017-2020), CEPROQHA (funded by Qatar National Research Funds, 2016-2019), and initiatives focused on cultural heritage preservation through 3D imaging. These projects demonstrate the practical impact of his work across healthcare, transportation, and construction industries. Dr Swash actively collaborates with researchers globally and is a member of the IMM research group at Brunel University, contributing to cutting-edge advancements in digital media and 3D technologies.
David Durand is a Lecturer in Computer Science at the Computer Science Department of the IUT of Amiens (Institute of Technology) within the University of Picardie Jules Verne . He serves as a Deputy Vice-President for Communication, Culture & Scientific Mediation and is a teacher-researcher affiliated with the MIS Laboratory and its SDMA Team . Research Focus: Distributed and communicating objects, middleware, IoT, and data processing in eHealth. His work addresses IoT heterogeneity, communication protocols, and AI-driven data processing architectures for medical applications. Recent projects explore intrusion detection in Medical IoT (MIOTs), AI threat management in healthcare, and movement analysis for Parkinson's disease treatments. His publications span cybersecurity, robotics, and AI in healthcare, with a focus on federated learning and anomaly detection. He actively supervises students in IoT-related topics, including smart homes, logistics, and medical robotics.
Arminda Guerra Lopes is a Professor at the Polytechnic Institute of Castelo Branco, Portugal, with 25 years of academic service. She holds a PhD in Human-Computer Interaction from Leeds Metropolitan University (UK) and maintains a research fellowship at the Interactive Technologies Institute (ITI/LARSyS) in Portugal. Her institutional leadership includes roles as School Vice Director and President of scientific/pedagogical boards. Her research focuses on human-centered technology design, with expertise in: Social informatics and collaborative systems Human-Work Interaction Design (HWID) Creativity support tools Quality-of-life technologies AI-human interaction paradigms She maintains active international collaborations across Europe and Asia. Recent publications (2017-2023) demonstrate strong focus on AI interactions, workplace systems, and accessibility solutions, with recurring themes of pilot implementation studies, artistic interfaces, and wellbeing technologies. Her work frequently employs mixed-methods approaches bridging technical development with human behavioral analysis. Dr. Guerra leads research teams exploring affective computing, augmented reality navigation, and game-based learning, with projects involving multisensory installations, gesture recognition systems, and community information platforms.
Nicholas Bambos is the R. Weiland Professor in the School of Engineering at Stanford University, holding a joint appointment in the Department of Electrical Engineering and the Department of Management Science & Engineering. He served as the Fortinet Founders Department Chair of the Management Science & Engineering Department from 2016 to 2020. His academic career spans over three decades, with previous positions as an assistant professor (1989-1995) and tenured associate professor (1995-1996) at UCLA before joining Stanford in 1996. Prof. Bambos's primary research interests focus on the architecture and high-performance engineering of computer systems and networks, along with data analytics emphasizing medical and health-care applications. His work spans multiple domains including networking and the Internet, cloud computing, multimedia streaming, computer security, and digital health. Methodologically, his contributions extend to network control, online task scheduling, routing and distributed processing, and machine learning and artificial intelligence. His research has resulted in over 300 peer-reviewed publications that demonstrate a strong interdisciplinary approach, bridging theoretical computer science with practical healthcare applications. The trajectory of Prof. Bambos's recent publications reveals a strategic expansion from traditional networking and systems research into healthcare analytics, particularly opioid use prediction and digital health monitoring. His work increasingly integrates machine learning techniques with domain-specific medical knowledge, showing a clear evolution toward solving complex societal challenges through technological innovation. Many publications demonstrate collaborative work across engineering, medical, and data science disciplines, reflecting the growing importance of interdisciplinary research in addressing modern healthcare challenges. His significant scientific achievements have been recognized through numerous prestigious awards: R. Weiland Professorship in Engineering (2016-present) Eugene L. Grant Teaching Award (2014) IBM Faculty Award (2002) Cisco Systems Faculty Scholar (1999-2003) National Young Investigator Award from NSF (1992-1997) Prof. Bambos has graduated over 40 doctoral students who have gone on to leadership positions in academia, Silicon Valley industries, technology startups, finance, and venture capital. His research has been supported by significant funding, including a $30 million Stanford Networking Research Center which he directed from 1999 to 2005. Beyond traditional academic roles, he has served on various editorial boards, scientific committees, and as a consultant and co-founder of technology startups, demonstrating his commitment to translating academic research into real-world impact. He leads the Computer Systems Performance Engineering Lab (Perf-Lab) at Stanford, which comprises doctoral students and industry visitors engaged in various research projects. His lab serves as an interdisciplinary hub connecting theoretical computer science with practical applications in healthcare, energy, and networking domains. The lab's collaborative environment fosters innovation across traditional academic boundaries, reflecting Prof. Bambos's broader research philosophy of addressing complex problems through integrated, multi-disciplinary approaches.
Professor Henry Duh is Associate Dean (Global & Industry Engagement) and Full Professor at the School of Design, The Hong Kong Polytechnic University. He also serves as Director of the PolyU-NVIDIA Joint Research Centre and Research Centre for Art and Culture Technology. PhD in Psychology, Industrial Design, and Engineering Postdoctoral Training: NASA-Johnson Space Centre His research focuses on human-centric augmented/virtual reality systems, interaction design, and immersive technologies. With over 150 publications and 6,100 citations, he explores cognitive behaviors in digital environments and develops applications for training, education, and cultural preservation. Recent publications demonstrate expertise in adaptive testing frameworks, VR locomotion mechanics, and AI-driven design tools. His work bridges psychology, computer science, and digital art through interdisciplinary approaches. Editor-in-Chief: Journal of Visual Languages and Computing (Elsevier) FAA certified Advanced Ground Instructor and Private Pilot As principal investigator, he secured millions in grants from institutions like the National Research Foundation Singapore, Australian Research Council, and Microsoft. He collaborates with CISCO, Oracle, and Microsoft to align curricula with industry needs. He has led academic units at University of Tasmania and La Trobe University, and held visiting positions at Tsinghua University and National Taiwan University.
Martina Pastorino is a Researcher in the Department of Naval, Electrical, Electronic, and Telecommunications Engineering (DITEN) at the University of Genoa. Her work focuses on integrating machine learning with probabilistic graphical models for advanced remote sensing image analysis , particularly in multiresolution classification using satellite and UAV data. She teaches courses on Machine Learning for Pattern Recognition and Remote Sensing in master’s programs related to Internet and Multimedia Engineering and Energy Engineering . Research Interests : Remote Sensing, Machine Learning, Image Segmentation, Data Fusion, Hyperspectral Imaging, UAV Applications. Key Techniques : CNN-MRF Hybrids, CRFNet, Probabilistic Graphical Modeling, Multiresolution Analysis. Her recent publications explore applications in wildfire mapping , urban land-use analysis , and hyperspectral-panchromatic fusion , with a focus on improving semantic segmentation accuracy through hybrid deep learning frameworks. She is available for office hours on request via email at martina.pastorino@unige.it .
