Martina Zitterbart is a Professor of Computer Science at the Karlsruhe Institute of Technology (KIT) , with a career spanning over 30 years in telematics research. She leads the Institute of Telematics , focusing on multimedia communication systems , mobile networking , and wireless sensor networks . Current C4 Professor at KIT since 2001 PhD in Computer Science (University of Karlsruhe, 1990) Visiting scientist at IBM Research (USA/Switzerland, 1989-1992) Her research now integrates artificial intelligence into network security and 6G automation . Key projects include: KIWI : Federated machine learning for cross-domain attack detection Open6GHub : 6G infrastructure development (€66.8M BMBF grant) 6G-ANNA : Network automation for next-gen mobile systems Recent publications focus on: DDoS attack detection through machine learning QUIC protocol performance in long-term use energy packet transmission for smart grids autonomic network management She has received multiple awards including: Alcatel SEL Research Prize (2002) Best Paper Awards (EI.A 2024, NoF 2023) Teaching Excellence Recognition (2003, 2008) As a member of IEEE, ACM, and the German Society for Informatics, she actively contributes to academic discourse through conferences like SIGCOMM and workshops on blockchain technology.
Markos Stamatakis is a Researcher at the German National Library of Science and Technology (TIB) within the Research and Development Department, specifically working in the Visual Analytics Research Group. His office is located at Lange Laube 28, 30159 Hannover (Room: 2.04), with postal address Welfengarten 1 B, 30167 Hannover. Dr. Stamatakis' research focuses on the automatic processing of educational videos with the purpose of generating questions related to the topic. His work integrates multiple modalities including images, audio, and speech transcripts/subtitles. Key aspects of his research include dataset creation, implementation of object detection algorithms, and application of large language models to recognize video content and enable subsequent question generation. His publication record demonstrates consistent contributions to the fields of educational technology and AI, with recent work examining vision-language models for educational video question generation, analysis of student drawings in chemistry classes, and predicting knowledge gain from MOOC video consumption. His research shows a clear trajectory toward developing AI systems that enhance educational experiences through multimodal analysis of learning materials. As part of the TIB's research infrastructure, Stamatakis collaborates with colleagues including R. Ewerth, A. Hoppe, and others across multiple projects focused on scholarly communication and educational technology.
Jun Luo is an Associate Professor in the School of Computer Science and Engineering at Nanyang Technological University (NTU), Singapore. He earned his PhD in Computer Science from EPFL under the supervision of Prof. Jean-Pierre Hubaux and completed postdoctoral research at the University of Waterloo. He joined NTU in 2008 as an Assistant Professor and was promoted to Associate Professor in 2014. He served as Deputy Director of the Centre for Multimedia and Network Technology from 2010 to 2013. Education: PhD in Computer Science, Swiss Federal Institute of Technology in Lausanne (EPFL), 2006 MS in Electrical Engineering, Tsinghua University, 2000 BS in Electrical Engineering, Tsinghua University, 1997 Research Interests: Jun Luo's research focuses on mobile and pervasive computing, wireless networking, machine learning, and applied operations research. His primary research thrusts include: Contact-free Sensing Driven by Deep Learning : Leveraging RF, acoustic, and visible light signals for human activity recognition, respiration monitoring, and localization without wearable devices. Visible Light Communication and Sensing : Exploring LED-camera systems for data transmission, occupancy inference, and indoor broadcasting. Indoor and Outdoor Localization and Tracking : Developing systems using WiFi, geomagnetism, and crowdsourced data for precise positioning. Machine Learning for Mobile Networking : Applying deep learning and optimization to improve wireless network performance, mobile crowdsensing, and resource allocation. Publication Trends: His recent publications (2021–2023) demonstrate a strong focus on deep learning-enhanced sensing using RF and acoustic signals, particularly for health monitoring (e.g., respiration, heartbeat), multi-person tracking, and privacy-preserving techniques. He frequently collaborates with researchers in signal processing, computer vision, and networking, publishing in top venues like IEEE Transactions on Mobile Computing, MobiCom, and INFOCOM. His work emphasizes practical deployment on commodity devices and integration of sensing with communication systems. Scientific Recognition: IEEE Fellow Advising and Grants: Dr. Luo has advised numerous PhD and Master's students, as evidenced by the extensive list of student co-authors across his publications. He has led significant research projects in wireless sensor networks, mobile computing, and IoT systems, likely supported by competitive grants from Singaporean and international funding agencies. His role as Deputy Director of a research center indicates leadership in managing research teams and collaborative efforts. Labs and Teams: He leads a research group focused on mobile and distributed computing, deep learning, and computer vision. His team actively publishes in top-tier conferences and journals, working on projects involving RF sensing, acoustic platforms, visible light communication, and privacy-aware systems. The group collaborates with researchers both within NTU and internationally, particularly in Canada and China.
Marco Bertini is an Associate Professor at the Department of Information Engineering, University of Florence, where he teaches in the School of Engineering. He is affiliated with the Media Integration and Communication Center (MICC) and is a member of GIRPR (Group for Image Recognition and Pattern Recognition). His research focuses on computer vision, multimedia, and pattern recognition with applications in video analysis and semantic processing. Laurea Degree in Electronics Engineering from University of Florence (1999) Ph.D. (2004) Dr. Bertini's research spans automatic video analysis, annotation, semantic transcoding, and social media analysis. He has led multiple EU-funded research projects including ASSAVID, DELOS Network of Excellence, VIDI-Video, IM3I, ORUSSI, and euTV. His current work focuses on smart museums and smart cities funded by the Italian Ministry of University, Instruction and Research, along with semantic video coding applications. As an active contributor to the academic community, he serves as Associate Editor for IEEE Transactions on Multimedia and has organized major conferences including European Conference on Computer Vision 2012 and ACM Multimedia 2010. He has also guest-edited special issues for Multimedia Tools and Applications journal. Associate Editor, IEEE Transactions on Multimedia Organizer, European Conference on Computer Vision 2012 Organizer, ACM Multimedia 2010 Guest Editor, Multimedia Tools and Applications Special Issue Dr. Bertini teaches undergraduate and graduate courses including Programming (OOP, C++, design patterns), Parallel Computing, and GPU Programming. He has previously taught Unix Fundamentals, CISCO CCNA, Multimedia Databases, and Information Technologies Laboratory. His research is conducted primarily at the Media Integration and Communication Center (MICC), where he collaborates with industry partners including SELEX ES on Terrestrial Trunked Radio video communication systems.
Steven Alexander Hicks is an Associate Professor in the Department of Computer Science at Oslo Metropolitan University's Faculty of Technology, Art and Design. His research focuses on applying computer vision and machine learning techniques to medical imaging problems, particularly in gastrointestinal endoscopy and reproductive medicine. He is an active contributor to the medical AI community through his involvement in organizing challenges and workshops such as ImageCLEFmedical and MediaEval. Hicks' research interests center on medical image analysis, with particular emphasis on gastrointestinal endoscopy and reproductive health applications. His work spans medical image segmentation, polyp detection, sperm tracking systems, and explainable AI for medical applications. He has developed innovative approaches for medical image analysis including diffusion models for synthetic data generation, visual question answering systems for gastrointestinal tract analysis, and frameworks for evaluating explanation methods in neural networks. His research bridges the gap between computer science and clinical medicine, aiming to develop practical AI tools that can be integrated into medical workflows. Analysis of Hicks' recent publication record reveals a strong focus on medical image analysis challenges, particularly in gastrointestinal endoscopy and reproductive medicine. He has been instrumental in organizing and contributing to the ImageCLEFmedical challenges, which have become important benchmarks in the field. His work shows a progression from basic image analysis techniques to more sophisticated approaches incorporating explainability, multimodal learning, and generative models. The consistent publication output across top venues demonstrates his active role in advancing medical AI research. Hicks has made significant contributions through his leadership in organizing evaluation campaigns and challenges that have shaped research directions in medical image analysis. His work with the MediaEval and ImageCLEF communities has provided valuable benchmarks and datasets for researchers worldwide. He has been involved in several large-scale collaborative projects addressing important problems in medical imaging, including polyp segmentation, instrument detection in endoscopy, and sperm analysis. As part of the broader research ecosystem at Oslo Metropolitan University, Hicks contributes to a vibrant research environment focused on applying computing technologies to healthcare challenges. His work often involves interdisciplinary collaboration between computer scientists, medical professionals, and domain experts to ensure that technical solutions address real clinical needs. His research group appears to focus on developing practical AI solutions that can be translated into clinical practice, with particular attention to validation methodologies and explainability requirements in medical contexts.
Ee-Peng Lim is a Professor at the School of Information Systems, Singapore Management University. His research spans artificial intelligence, data mining, natural language processing, and computer vision, with applications in healthcare, education, finance, and food computing. He leads projects developing AI systems for behavioral counseling, educational analytics, and multimodal food recognition. Research Interests: Dr. Lim's work focuses on conversational AI for mental health interventions, educational data mining for student performance prediction, food computing for nutrition analysis, and multimodal learning frameworks. His recent projects leverage large language models for complex reasoning tasks and develop robust computer vision systems for real-world applications. Publication Trends: Recent articles (2024-2025) show strong emphasis on multimodal AI systems, large language model applications in behavioral science and healthcare, educational analytics, and advanced food computing techniques. His work increasingly integrates cognitive science principles with deep learning architectures. Leadership: Dr. Lim collaborates extensively with international researchers and has co-organized academic workshops including the International Workshop on Talent and Management Computing (TMC) at KDD.
Andrea Bernardini is an Associate Professor in the Department of Computer Science at the University of Udine, Italy. With a research career spanning over 15 years, Bernardini has established himself as a prominent researcher in cybersecurity, machine learning, and medical applications of AI. His work bridges theoretical computer science with practical applications in healthcare, IoT security, and wireless sensing technologies. Dr. Bernardini's research focuses on applying artificial intelligence techniques to solve complex problems in cybersecurity and medical diagnostics. His work spans several key areas including IoT security, where he develops methods for analyzing vulnerable internet-connected devices; medical AI, where he applies deep learning to diagnose conditions like sleep apnea and Parkinson's disease; and wireless sensing technologies that use Wi-Fi signals for person identification and monitoring. His approach often combines multiple technical domains to create innovative solutions for real-world problems. His recent publications demonstrate a strong trend toward interdisciplinary research that combines cybersecurity with healthcare applications. Bernardini's work on 5G network security, Wi-Fi based person identification, and EEG analysis for Parkinson's disease diagnosis shows his ability to bridge multiple technical domains. His research often involves collaboration with medical professionals and industry partners to ensure practical applicability of theoretical advances. Dr. Bernardini has been actively involved in several significant research projects focused on cybersecurity frameworks for emerging technologies. His work with the ITASEC and SERICS conferences indicates strong engagement with the European cybersecurity research community. Recent projects include developing meta-search engines for IoT device posture analysis and ontological approaches to 5G service cybersecurity, addressing critical infrastructure protection needs.
Sadiq Ahmad is a researcher with significant contributions to energy systems, wireless networks, and machine learning applications. Active in top venues like IEEE Access and IEEE Communications Surveys & Tutorials , his work spans from 2015 to 2025. Research Interests: Focus on Radio resource allocation in cognitive radio sensor networks (2015) Smart energy management in microgrids (2020–2025) Visual saliency in video quality assessment (2025) Optimization algorithms for power systems (2021) Collaborations: Frequently co-authors with Ayaz Ahmad Muhammad Naeem Mubashir Husain Rehmani Shahid Iqbal on interdisciplinary projects involving electrical engineering and AI.
Anastasios Tefas is a Professor at the Department of Informatics, Aristotle University of Thessaloniki. He received his B.Sc. (1997) and Ph.D. (2002) in Informatics from the same university. His academic career includes progressive roles from Postdoctoral Researcher (2002-2006) to Lecturer, Assistant Professor, Associate Professor, and ultimately Professor (2022-present). Education Ph.D. in Informatics, Aristotle University of Thessaloniki (2002) B.Sc. in Informatics, Aristotle University of Thessaloniki (1997) Professional Experience Professor (2022-present) Associate Professor (2017-2022) Assistant Professor (2013-2017) Lecturer (2008-2013) Assistant Professor at TEI of Kavala (2006-2008) His research focuses on computational intelligence, deep learning, and pattern recognition, with applications in multimedia data analysis, computer vision, and robotics. He has contributed to over 170 journal papers, 300 conference papers, and 17 book chapters. Currently coordinating the Energy Efficient and Trustworthy Deep Learning (DeepLET) project, he has participated in 25 national/European-funded research initiatives. Prof. Tefas serves as Area Editor for Signal Processing: Image Communications and has co-organized 15+ workshops. His work has garnered >12,000 citations with an H-index of 53 (Google Scholar).
Stuart Perry is a Professor at the University of Technology Sydney (UTS), serving as Head of Discipline for Signal Processing and Analytics in the School of Electrical and Data Engineering. He holds affiliations with UTS' Faculty of Engineering and Information Technology, the Global Big Data Technologies Centre, and the Visualization Institute. With over 20 years of experience, his career spans roles at DSTO and Canon Information Systems Research Australia (CiSRA), focusing on image processing, signal processing, and perceptual quality measurement. Perry co-directs the Perceptual Imaging Laboratory (PILab), researching 3D environments, light field technologies, and human perception in immersive realities. He actively contributes to international standards committees like ISO/TC42 and ISO/SC29/WG7, leading JPEG's point cloud coding efforts. His research emphasizes adaptive image processing, machine learning-driven object detection, and medical imaging applications. Perry has authored 60+ publications, two books, and 20 patents. Current projects include VR empathy case studies, point cloud compression, and disaster management digital transformation. Education: PhD in Engineering, University of Sydney (1999) Research Interests: His work bridges computational imaging and human perception, addressing challenges in augmented/virtual reality (AR/VR), 3D scanning, and immersive media. Key areas include light field and point cloud coding, psychophysics of visual perception, and medical diagnostics via machine learning. He explores how perceptual principles can optimize interactive technologies for education, healthcare, and entertainment. Recent Research Trends: Recent articles focus on 3D Gaussian splatting, glaucoma detection via deep learning, and JPEG Pleno standards for plenoptic imaging. His work balances technical innovation (e.g., efficient point cloud compression) with human-centric design (e.g., reducing VR motion sickness through display lag analysis). Awards & Recognition: Member of IEEE and founding SPINet participant. Over 60 refereed publications and 20 patents highlight his industry-academic impact. Grants & Leadership: Leads SmartSat CRC projects on yield estimation and Aus4innovation-funded disaster response tech. Manages grants totaling millions AUD. Editorial roles include Associate Editor of SPIE/IS&T Journal of Electronic Imaging. Labs & Collaborations: PILab collaborates internationally on perceptual imaging standards. Active in ISO committees shaping future media technologies.
Dr. Biswajeet Pradhan is a Distinguished Professor at the University of Technology Sydney (UTS), holding positions in the School of Civil and Environmental Engineering and the Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS). His primary affiliation is within the Faculty of Engineering & IT. He holds a habilitation in Remote Sensing from Dresden University of Technology, Germany (2011), and has served as an 'Ambassador Scientist' for the Alexander von Humboldt Foundation (2015–2022). His research focuses on Geospatial Information Systems (GIS), remote sensing, machine learning, environmental modeling, and disaster risk assessment. He has published over 832 articles (H-index 147) and secured $14M in research funding. Key research trends in his work include AI-driven climate hazard modeling, geospatial applications for disaster management, and machine learning in environmental monitoring. His recent publications span topics like flood risk assessment, landslide susceptibility, and hurricane damage prediction. Awards: 55+ awards, including Alexander von Humboldt Fellowship and Clarivate's Highly Cited Researcher (2006–2020). Grants & Advising: 23 completed projects totaling $14M; supervised 55 PhD students and 42 MSc students. Labs/Teams: Leads the CAMGIS Research Centre, collaborating with institutions globally (Malaysia, Germany, Norway, etc.).
Zhicheng Liu is an Assistant Professor in the Department of Computer Science at the University of Maryland. His research focuses on human-computer interaction, machine learning, and data visualization, emphasizing democratizing visualization design through computational tools and human-centered systems. He received the NSF CAREER Award (2023) for his work on manipulable semantic components in data visualization. Education details are not explicitly stated in the provided texts, but his professional trajectory indicates advanced academic training in computer science. Research interests include interactive visualization authoring, collaborative systems, AI-enhanced design tools, and semantic analysis of visual content. His work bridges computer science with cognitive science, psychology, and graphic design to create impactful interdisciplinary solutions. Liu's recent publications (2023–2025) explore topics like semantic chart decomposition, AI-assisted documentation, and cross-linguistic collaboration tools. His NSF-funded project aims to develop modular visualization components for easier creation of interactive charts. Awards include the NSF CAREER Award, recognizing his potential to advance visualization research. He advises four PhD candidates focusing on visualization and HCI topics. His grants include a $600K NSF award supporting his CAREER project. Collaborative tools developed (e.g., DocDancer, VisAnatomy) demonstrate his focus on practical, user-centric systems. Liu’s lab focuses on responsive documents, AI-driven tutorial systems, and visualization corpus development. He emphasizes real-world impact through tools that simplify data analysis and communication for non-experts.
Soumyabrata Dev is an Assistant Professor in the School of Computer Science at University College Dublin (UCD), where he leads the THEIA lab focusing on interdisciplinary research in computer vision, machine learning, and remote sensing. His work addresses challenges in climate science, environmental monitoring, solar forecasting, and healthcare. He holds a PhD from Nanyang Technological University (Singapore) and has held postdoctoral roles at Trinity College Dublin and ADAPT SFI Research Centre. His education includes a B.Tech. (summa cum laude) from National Institute of Technology Silchar, India, and a visiting doctoral experience at École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. Prior to academia, he worked as a network engineer at Ericsson India (2010–2012). Research interests span image processing, environmental data analytics (e.g., air/water quality), solar energy forecasting, and AI-driven solutions for sustainable development. He collaborates globally with institutions in academia, industry, and government, aligning with UCD’s strategic goals for impactful innovation. He is an SFI Funded Investigator at ADAPT SFI and a UCD Climate Fellow (2024–2026). His scientific awards include the 2024 Stanford/Elsevier Top 2% Scientists List. He advises numerous PhD/MSc students on topics like coastal monitoring, air quality modeling, and AI for sustainability. THEIA Lab’s projects include solar irradiance forecasting, knowledge graph-based climate data platforms, and blockchain-enhanced healthcare systems. He teaches modules on Operating Systems, Wireless Sensor Networks, and Augmented/Virtual Reality. His work bridges theory and application, emphasizing real-world impact in climate action and renewable energy.
Andrew T. Duchowski is a Professor and Chair of the Visual Computing Division at Clemson University's School of Computing (part of the College of Engineering and Science). He holds a Ph.D. in Computer Science from Texas A&M University (1997) and a B.Sc. in Computer Science from Simon Fraser University (1990). His academic roles include service as Focus Area Chair at SIGGRAPH 2019 and Co-organizer of multiple workshops such as MobileHCI 2018 and ETRA 2018. His research focuses on eye tracking, visual perception, human-computer interaction, and computer graphics. Key areas include gaze analytics, virtual environments, and applications in healthcare, education, and cultural heritage. He has pioneered methodologies like the 'Gaze Analytics Pipeline' and authored Eye Tracking Methodology: Theory & Practice (Springer, 3rd ed., 2017). R&D interests span cognitive load measurement via pupil dynamics, ambient/focal attention modeling, and gaze-based interaction design. His work bridges computer science with fields like spatial cognition and neuroergonomics. Recent projects include studies on attention in VR, neurocognitive biomarkers, and sustainable cultural landscape management. Awarded multiple honors including the NSF CAREER Award (2000-2003) and Clemson's Faculty Excellence Award (2001), Duchowski has authored over 150 peer-reviewed papers. He led educational initiatives such as the 'Groovy Graphics Assignments' for SIGGRAPH and co-organized global conferences like ETRA and EuroGraphics. His service includes editorial roles in journals like Transactions on Applied Perception and reviewing for NIH, NSF, and international universities.
Prof. Maura Mengoni is an Associate Professor at the Department of Industrial Engineering and Mathematical Sciences of the University of Macerata (UNIVPM). Her work focuses on human-centered design methodologies integrating advanced technologies like AI-driven systems, XR applications, and affective computing. She leads research in smart manufacturing systems, ergonomic assessment tools, and inclusive technology development for diverse user groups including people with disabilities. Key projects include SOPHIA's IoT architecture for predictive maintenance and emotion-aware interfaces for automotive safety. Research interests span industrial automation, virtual/augmented reality applications, and multimodal interaction design. Recent studies investigate cognitive workload in driving simulators, emotion-based museum experiences, and facial expression analysis for educational evaluation. Her work bridges engineering with humanities through projects like Civitas, enhancing cultural heritage accessibility via spatial augmented reality at Urbino’s Ducal Palace. Publications emphasize interdisciplinary approaches, including collaborative designs with end-users for accessible technologies and data fusion techniques in manufacturing. She has pioneered systems linking customer emotions to shopping experiences and developed tools for ergonomic risk assessment using RGB camera networks.
