Michael Multerer is an Associate Professor at the Faculty of Informatics, Università della Svizzera italiana (USI). His research focuses on multiresolution methods, scattered data analysis, and numerical analysis with applications in computational mathematics and engineering. He leads projects such as the SNSF Starting Grant on multiresolution methods for unstructured data, emphasizing nonlinear approximation and kernel-based techniques. Research Interests: Development of fully discrete multiresolution methods for unstructured data Wavelet theory and kernel matrix algebra Uncertainty quantification in partial differential equations Scattered data compression and approximation Key Software Contributions: FMCA: Fast multiresolution covariance analysis for scattered data Bembel: Boundary element library for solving Laplace and Helmholtz equations SPQR: Anisotropic sparse grid quadrature in MATLAB Funding: Holder of the SNSF Starting Grant (2025) for advancing multiresolution techniques in unstructured data processing. Labs/Teams: Active in the research group at USI’s Faculty of Informatics, collaborating with institutions like TU Darmstadt and University of Basel on numerical methods and engineering applications.
Aristidou Andreas is an Associate Professor at the Department of Informatics, University of Cyprus. His academic journey includes a BSc from National and Kapodistrian University of Athens (2005), MSc from King's College London (2006), and a PhD from the University of Cambridge (2011). He has held postdoctoral positions at multiple institutions and served as a visiting professor at the University of Nicosia. His research focuses on human motion analysis, computer graphics, cultural heritage digitization, and geometric algebra applications. He is a senior member of IEEE, ACM, and Eurographics, and serves on editorial boards for journals like The Visual Computer. Research highlights include the FABRIK algorithm (widely used in game engines) and motion capture innovations for cultural heritage preservation. Awards include the DIDAKTOR scholarship, Erasmus Mundus Grant, and NVIDIA GPU grant. He leads projects funded by EU initiatives (e.g., ITN-DCH, SCHEDAR) and collaborates internationally on digital twins, virtual museums, and AI-driven motion synthesis. Key contributions span 3D motion analysis, motion reconstruction from sparse data, and emotion recognition in theater performances. His work bridges computational methods with cultural preservation, biomedical applications, and interactive VR systems.
Dr. Chulsoon Hwang is an Associate Professor of Electrical Engineering at Missouri University of Science and Technology (Missouri S&T) , part of the College of Engineering . He leads the EMC Laboratory and focuses on RF desensitization, signal/power integrity, machine learning in hardware design, and electromagnetic interference (EMI). His work bridges theoretical electromagnetics with practical applications in high-speed digital systems and hardware security. Education: Ph.D., M.S., and B.S. in Electrical Engineering from KAIST (Korea Advanced Institute of Science and Technology), Daejeon, South Korea. Prior to joining Missouri S&T in 2015, he worked as a Senior Engineer at Samsung Electronics (2012–2015). Research Highlights: Developed machine learning-driven simulators for signal integrity (e.g., High-speed Channel Transformer). Pioneered physics-assisted genetic algorithms for PDN decoupling optimization. Explored intentional EMI attacks (e.g., inaudible voice command injection into smart speakers). Advanced understanding of power supply-induced jitter (PSIJ) and EMI mitigation strategies. Recent Trends in Publications: Focus on AI-driven hardware optimization (deep learning/RL for PDN design), EMI/RFI modeling, and security vulnerabilities in IoT devices. Over 150 IEEE publications and multiple patents (e.g., inaudible voice command injection, laser foil printing for resonators). Awards and Recognition: Best Paper Awards (7 papers, 4 student papers). Dean’s Scholar Award (2022–2023) and IEEE EMC Society Technical Achievement Award (2023). Google Faculty Research Award (2020) and APEMC Young Scientist Award (2018). Grants and Labs: Active in securing research grants for AI-driven hardware design and EMI mitigation. The EMC Lab collaborates on industry-relevant projects, such as acoustic noise analysis in power distribution networks. Labs/Teams: Director of the EMC Laboratory at Missouri S&T, fostering interdisciplinary research in electromagnetics and hardware security.
Won-Jae Yi is an Associate Teaching Professor of Electrical and Computer Engineering at the Illinois Institute of Technology (Illinois Tech), part of the Armour College of Engineering. He holds a B.S., M.S., and Ph.D. in Computer Engineering from Illinois Tech, completed in 2010, 2012, and 2017, respectively. In addition to his teaching role, Yi serves as the Undergraduate Laboratory and Computing Facilities Supervisor in the Department of Electrical and Computer Engineering. He has over 13 years of experience in research focusing on IoT, wireless sensor networks, wearable devices for health monitoring, FPGAs, cyber-physical systems, and edge computing. Education: B.S. in Computer Engineering (cum laude, 2010), Illinois Tech M.S. in Computer Engineering (2012), Illinois Tech Ph.D. in Computer Engineering (2017), Illinois Tech Research Interests: Yi’s work spans IoT-enabled systems, AI-driven smart devices, cybersecurity for embedded systems, and assistive technologies for visually impaired individuals. His research emphasizes practical applications such as smart home automation, wearable health monitoring, and real-time data fusion for IoT systems. He consistently integrates his expertise in hardware-software co-design, including FPGAs and SoCs, to create robust, low-power solutions. Teaching Contributions: Yi teaches undergraduate and graduate courses in Software Engineering, Internet of Things, Cybersecurity, and Artificial Intelligence. His teaching excellence was recognized with the 2023 Bauer Family Excellence in Undergraduate Teaching Award. Labs and Facilities: As Laboratory Supervisor, Yi manages computing facilities and oversees the development of experimental setups for student projects in IoT, embedded systems, and AI. His lab focuses on prototyping smart systems that bridge theoretical concepts with real-world applications.
Dr. Andrew Melbourne is an Associate Professor (Reader) in Healthcare Technologies at King's College London, affiliated with the School of Biomedical Engineering & Imaging Sciences and the Department of Surgical & Interventional Engineering. He leads the MSc/MRes Healthcare Technologies program and focuses on imaging sciences, computational modeling, and placental physiology. His work includes cross-disciplinary collaborations with clinicians to improve understanding of fetal interventions, placental function in conditions like fetal growth restriction, and twin pregnancies. Key projects include MRI advancements for placental and fetal health, funded by organizations such as the Wellcome Trust and NIH. Research interests span medical imaging techniques (MRI, CT), fetal-neonatal brain development, and AI-driven image analysis. Notable contributions include fetal noise exposure modeling, placental perfusion studies, and super-resolution MRI applications for surgical planning. Dr. Melbourne co-leads initiatives like the MIBIRTH study, aiming to optimize pregnancy management through imaging. He has organized workshops on perinatal image analysis and contributed to student prizes in Healthcare Technologies. Publications span over 100 peer-reviewed articles in journals like Nature Communications and NeuroImage, emphasizing placental biology, fetal surgery outcomes, and AI in medical imaging. His work bridges clinical needs with engineering solutions to address maternal-fetal health challenges.
Antonio Servetti is an Assistant Professor at the Department of Control and Computer Engineering (DAUIN) at Politecnico di Torino, Italy, where he has been a faculty member since 2007. He is affiliated with the Internet Media Group (IMG) and the Interdepartmental Center PIC4SeR for Service Robotics. His work bridges multimedia processing, network communications, and web technologies. MS in Computer Engineering, Politecnico di Torino, 1999 PhD in Computer Engineering, Politecnico di Torino, 2004 Visiting Scholar, University of California, Santa Barbara, 2003 His research focuses on speech and audio processing , multimedia communications over wired and wireless networks , and real-time web-based multimedia applications . Key interests include WebRTC, Web Audio, HTTP adaptive streaming, and perceptual quality assessment. He has contributed to the development of secure multimedia transmission techniques, including selective encryption of speech and audio. The recent publications highlight a strong trend toward AI-driven modeling of subjective quality in multimedia, especially through deep learning for image and video quality prediction, understanding observer behavior, and remote music performance systems. His work often involves collaboration with researchers in the VQEG JEG-Hybrid group and the NEXA Center. Best Paper Award, Web Audio Conference 2021 Dr. Servetti has led and contributed to several research projects, including BRIC-2024 (acoustics in educational settings), PNRR HiFiReM (remote music education), and INAR (artistic research). He teaches courses such as 'Web Applications', 'Machine Learning for Vision and Multimedia', and 'Digital Audio Processing' across various engineering programs. He is also involved in educational governance as a member of academic councils for multiple degree programs. He is a core member of the Internet Media Group (IMG) , which focuses on multimedia processing and transmission, and contributes to the VQEG JEG-Hybrid working group on video quality assessment, where he develops frameworks for reproducible research and modeling of human perception.
Emma Frosina is an Associate Professor at the Department of Engineering (DING) of the University of Sannio, Italy. Her research focuses on fluid machinery, thermal management systems, and computational fluid dynamics (CFD) with applications in automotive and railway sectors. Academic Rank: Associate Professor Department: Engineering (DING) Email: frosina@unisannio.it Her work emphasizes fault detection in pumps using machine learning, optimization of liquid cold plates via DOE and CFD, and analysis of cavitation in spool valves. Recent publications highlight collaborations with researchers like Senatore, Romagnuolo, and Borriello. Key themes from her 15 most recent publications include: Advancements in condition monitoring for electric gear pumps Innovations in pressure ripple reduction via CFD Development of vibroacoustic tools for fault detection Optimization of thermal management systems in railway and automotive contexts
Assoc. Prof. Gültekin Işık is affiliated with the Department of Computer Hardware at Iğdır University's Faculty of Engineering. He holds a PhD in Computer Engineering from Hacettepe University (2011–2019), focusing on Turkish dialect recognition using deep learning. His academic roles include serving as Head of the Department from 2019 to 2021. His research interests span deep learning applications in computer vision (e.g., YOLO-based crowd detection), optimization algorithms (e.g., Slime Mould Algorithm), environmental modeling (solar power efficiency prediction), and healthcare (breast cancer detection). He has authored numerous peer-reviewed articles and books, including works on convolutional neural networks for plant disease identification and hybrid optimization techniques for data clustering. Prof. Işık teaches advanced courses such as İleri Derin Öğrenme (Advanced Deep Learning) and Sinir Ağları (Neural Networks). His recent work emphasizes real-time video analysis for public health (social distancing) and energy systems optimization. Despite no listed awards, his contributions to interdisciplinary fields like bioacoustics and renewable energy are notable. He has advised two master’s students: Mehmet Şirin Gündüz (2023) on YOLO-based crowd detection and Seda Bayat (2021) on bird species recognition using deep learning. His research integrates theoretical models with practical applications in domains ranging from agriculture to medical imaging.
Fabio Catania is a Postdoctoral Fellow at the McGovern Institute for Brain Research, Massachusetts Institute of Technology. His work focuses on developing conversational agents and AI systems tailored for neurodevelopmental disorders, emotion recognition in speech, and inclusive technology design. He has pioneered projects such as the Emozionalmente Italian emotional speech corpus and the Emoty conversational agent for individuals with autism spectrum disorder. Research Interests: AI-driven therapeutic interventions for neurodevelopmental disorders Multimodal emotion recognition systems Design of accessible conversational interfaces for marginalized groups Linguistic and acoustic analysis of non-verbal communication cues Key Contributions: Developed AI tools like Smemo (multimodal interface for children's creativity) and OK, DNA! (genomic data explorer) Created voice-controlled music production systems for motor-impaired users Explored irony and sarcasm detection through multimodal analysis His work bridges computational linguistics, human-computer interaction, and clinical applications, with a focus on creating ethical and inclusive AI systems for healthcare and education contexts.
Professor Patrick Doncaster is an academic faculty member at the School of Biological Sciences, University of Southampton, within the Faculty of Environmental and Life Sciences. His research spans population ecology, conservation management of forest and freshwater lake ecosystems, and development of conservation technology and data analysis tools. BSc in Environmental Sciences (1st-class) from University of East Anglia D.Phil. in Zoology from University of Oxford Research interests include: Population ecology Forest biodiversity Human-predator-shared prey interactions Acoustic ecology Open-source conservation technology Early warning signals for ecosystem tipping points Climate change mitigation mechanisms Robust evidence-based analysis methods His publications from 2023-2025 demonstrate expertise in statistical ecology, conservation technology, and ecosystem dynamics across terrestrial, freshwater, and marine environments. He develops tools for ANOVA analysis, radio-tracking data, and evidence-based conservation. Academic contributions include: Active projects on lake ecosystem recovery pathways Completed NERC and PTES-funded field studies Methods development for statistical power and ecological modeling Interdisciplinary collaborations across environmental science and technology Teaching materials on statistics and evolutionary genetics Leadership in the Ecology and Evolution Centre and Institute for Life Sciences He supervises PhD students and maintains active research groups focused on ecological theory and conservation practice.
Rosita Guido is an Associate Professor in the Department of Mechanical, Energy and Management Engineering at Università della Calabria, specializing in Operations Research (MATH-06/A). Her academic role focuses on mathematical optimization and artificial intelligence applications across healthcare, manufacturing, and sustainable systems. She teaches graduate courses including Tools and Methods for Innovation in Healthcare and Business Intelligence within the Management Engineering program. Her research interests span healthcare optimization, predictive maintenance, machine learning applications, and sustainable supply chain management. Guido develops advanced mathematical models and algorithms for complex decision problems under uncertainty, with particular emphasis on healthcare systems optimization, tool condition monitoring, and resource allocation. Her methodological expertise includes mathematical programming, combinatorial optimization, stochastic programming, and hybrid AI techniques. Analysis of her recent publications reveals a strong interdisciplinary focus bridging engineering and healthcare. Key trends include the application of machine learning (particularly SVMs and CNNs) to medical diagnostics, optimization of healthcare operations (patient admission scheduling, bed management), and integration of AI with IoT for industrial applications. Her work demonstrates consistent innovation in solving real-world problems through mathematical modeling and algorithm development. As an active member of the Operations Research (Ricerca Operativa) research group at DIMEG, she contributes to projects addressing logistics optimization, green transportation systems, energy infrastructure management, and clinical process optimization. The group maintains international collaborations with prestigious research centers and companies including Amazon. Guido's teaching portfolio includes graduate-level courses in Management Engineering, where she integrates her research expertise into curriculum development. Her academic service includes participation in departmental governance and research initiatives within the Department of Mechanical, Energy and Management Engineering.
Rafik Goubran is a Professor in the Department of Systems and Computer Engineering at the Faculty of Engineering and Design, Carleton University. He holds the distinguished title of Chancellor’s Professor and currently serves as the Vice-President (Research and International). He earned his Ph.D. from Carleton University and is a licensed Professional Engineer (P.Eng.). His research expertise spans digital signal processing, biomedical engineering, audio processing, and the development of smart environments for senior independent living, with applications in patient monitoring, sensor systems, and real-time analytics. Dr. Goubran's recent publications highlight a strong focus on applying machine learning and sensor technologies to healthcare challenges, particularly in gerontechnology. His work involves non-invasive monitoring of sleep apnea, cognitive decline, driving behavior in older adults, and ambient health monitoring using smart homes and IoT systems. He explores innovative methods such as using pressure mats, audio analysis, and video magnification for vital sign detection and behavioral assessment. Life Fellow, IEEE Fellow, Canadian Academy of Engineering (CAE) Chancellor’s Professor, Carleton University Dr. Goubran has co-supervised 24 Ph.D. and 72 Master’s students and has secured significant research funding from NSERC, CIHR, NCE, and OCE for projects related to aging, health monitoring, and smart technologies. He is the co-leader of the TAFETA project and was the founding Director of the Ottawa-Carleton Institute for Biomedical Engineering. His work involves extensive collaboration with institutions like the Bruyère Research Institute and industry partners such as QNX and BlackBerry. He leads a research lab focused on technology-assisted environments, developing systems for patient monitoring, fall detection, and cognitive assessment, contributing significantly to the field of assistive and ambient intelligence for healthcare.
Ean Hin Ooi is an Associate Professor in the School of Engineering at Monash University Malaysia, where he leads the Computational Modelling team. He holds a PhD in Mechanical Engineering from Nanyang Technological University, Singapore, and a Bachelor's degree in Mechanical Engineering from the University of Technology, Malaysia. His research spans biomedical engineering, computational modeling, and numerical methods, with applications in cancer therapy and medical diagnostics. Doctor of Philosophy, Mechanical Engineering, Nanyang Technological University (NTU), 2009 Bachelor's Degree, Mechanical Engineering, University of Technology, Malaysia Dr. Ooi’s research focuses on the application of mathematical and computational modeling to understand biophysical phenomena in healthcare. His work targets improving cancer treatments such as thermal ablation (radiofrequency, cryoablation, photothermal), developing diagnostic tools like shear wave elastography for kidney disease, and advancing numerical techniques including meshless and boundary element methods. He is a pioneer in the radial basis integral equation method, contributing significantly to computational mechanics. The recent publications reflect a strong trend in interdisciplinary research combining engineering, medicine, and materials science. Key themes include thermal therapy optimization, nanomedicine (gold nanorods, graphene), microfluidics, and machine learning integration in sensor systems. His modeling expertise extends from organ-level simulations (liver, kidney) to nanoscale heat transfer, demonstrating versatility across scales and applications. Dr. Ooi serves as an Associate Editor for Computer Methods and Programs in Biomedicine and Journal of Mechanics in Medicine and Biology , highlighting his scholarly impact. He has received research funding for projects such as photothermal therapy for liver cancer and thermochemical ablation, indicating sustained grant support. He mentors students and is currently accepting PhD candidates in areas related to wound healing mechanics and computational prediction tools for thermal therapy. He is actively involved in professional service, including participation in the Asia-Pacific Society for Artificial Organs and visiting researcher roles at institutions like the University of Nottingham Malaysia. His lab, the Computational Modelling team, focuses on developing predictive frameworks for medical interventions, aiming to bridge computational science with clinical applications.
Dr. Henry Hong-Ning Dai is an Associate Professor in the Department of Computer Science at the Faculty of Science, Hong Kong Baptist University (HKBU). He previously held academic positions at Lingnan University and Macau University of Science and Technology, where he advanced from Assistant to Associate Professor. He holds a Ph.D. from the Chinese University of Hong Kong and a D.Eng. from Shanghai Jiao Tong University. Education: Ph.D. in Computer Science and Engineering, Chinese University of Hong Kong (2008) D.Eng. in Computer Technology Application, Shanghai Jiao Tong University (2012) M.Eng. in Computer Science and Engineering, South China University of Technology (2003) B.Eng. in Computer Science and Engineering, South China University of Technology (2000) Dr. Dai's research focuses on security and reliability of VR/AR systems , Internet of Things , blockchain and distributed systems , federated learning , and cyber-physical systems . His work integrates AI, networking, and software engineering to address real-world security and performance challenges in emerging technologies. He has published over 300 papers in top journals and conferences such as IEEE JSAC, TMC, ICSE, INFOCOM, and AAAI, accumulating more than 24,000 citations. The 15 most recent publications (2023–2025) highlight his leadership in VR/AR security (e.g., AcouListener, Meta VR study), blockchain scalability and fairness (e.g., Porygon, Auncel, Justitia), federated and robust learning (e.g., EBS-CFL, FedDP), and edge-AI and wireless security (e.g., HARBOR, Smart Shield). His recent work also explores AI-generated art evaluation and LLM-driven manufacturing systems , showcasing interdisciplinary innovation. Scientific Awards and Recognition: Holder of 1 U.S. patent and 1 Australia innovation patent Winner of more than 17 awards Senior Member of ACM, IEEE, and EAI Dr. Dai has been Principal or Co-Investigator on over 12 research projects totaling HK$16 million, funded by UGC, NSFC, FDCT, and HKBU. He serves as an Associate Editor for IEEE Communications Surveys & Tutorials , IEEE Transactions on Intelligent Transportation Systems , and several other IEEE journals. He has chaired program committees and served on the PC of top conferences including ICSE, KDD, and INFOCOM. He is actively recruiting Ph.D. students and RAs in security, blockchain, and AI. Laboratories and Research Teams: While not explicitly named, Dr. Dai leads a research group focused on secure and intelligent distributed systems, with active projects in blockchain, VR security, and edge AI. His team has developed open-source tools such as VR-SP Detector , PrettySmart , and RLF for smart contract analysis and security assessment.
Dr. Alexander Bertrand is a Professor at the Faculty of Engineering Sciences , KU Leuven, heading the Dynamic Systems, Signal Processing and Data Analysis (STADIUS) division. He leads the Department of Electrical Engineering (ESAT) and contributes to Leuven.AI institute, with expertise spanning wireless sensor networks, brain-computer interfaces (BCI), and biomedical signal processing. Research Focus : Wireless acoustic/EEG sensor networks, distributed signal enhancement, adaptive filtering, neural decoding of auditory/visual attention, and AI-driven time series analysis. Key Projects : EEG-Linx platform for modular brain recordings (2025-2027) Calibration-free BCI systems (2025-2029) AI quality assessment for time series data (2024-2028) Wireless EEG patches for hearing technology (2024) Publications (2023-2025) demonstrate leadership in distributed signal processing , auditory attention BCI , and scalable sensor architectures , with applications in education, healthcare, and wearable tech. Teaching includes courses on digital signal processing, biomedical data analysis, and medical technology design.
