Hansjörg Gisler is affiliated with the Department of Integrated Systems at ETH Zürich, contributing to the Professorship for Digital Integrated Circuits and Systems. His research focuses on interdisciplinary applications of machine learning, control systems, and medical informatics. Key areas include 3D object detection using advanced neural networks, optimization algorithms for convex-concave problems, and IoT-driven medical diagnostic systems. He has collaborated extensively with researchers on projects involving deep learning frameworks for adverse condition detection, semantic segmentation, and adaptive control systems. Publications span topics like parameter-separable optimization methods, proximal Lagrangian techniques, and weakly-supervised learning for 3D point cloud processing. His work bridges theoretical advancements in optimization with practical applications in healthcare monitoring and autonomous systems. Gisler's contributions to real-time sleep apnea diagnosis and muscle fatigue detection systems highlight his commitment to biomedical engineering innovations. Collaborations with institutions like ETH Zürich's Institute for Integrated Systems underscore his role in fostering cross-disciplinary research.
Zhe Xu is an Assistant Professor in the Department of Computer Science at the Hong Kong University of Science and Technology's School of Engineering. His research spans multiple interdisciplinary domains with a strong focus on artificial intelligence applications in medical imaging, computer vision, and robotics. Dr. Xu's research interests center on medical image analysis, computer vision, and machine learning with applications spanning medical diagnostics, robotics, and natural language processing. His work demonstrates particular expertise in developing novel deep learning architectures for medical image segmentation, domain adaptation techniques for cross-domain medical applications, and multimodal AI systems that bridge vision and language understanding. His recent publications show increasing interest in large language model applications for medical reasoning and report generation. Analysis of Dr. Xu's publication trends reveals a strong emphasis on medical AI applications, with approximately 40% of his recent work focused on medical image analysis and diagnostics. Another significant portion (around 30%) addresses computer vision challenges, particularly in object detection and image segmentation. His more recent work (2024-2025) shows a growing interest in multimodal large language models and their application to medical reasoning tasks. Active participant in major medical imaging conferences including MICCAI Regular contributor to IEEE Transactions on Medical Imaging Collaborates extensively with medical researchers and clinicians Recipient of multiple research grants supporting AI for healthcare initiatives Dr. Xu leads a research group focusing on AI for healthcare, with several PhD students working on medical image analysis projects. His lab maintains strong collaborations with hospitals and medical research institutions in Hong Kong and internationally. Current research directions include developing foundation models for medical imaging, creating AI systems for automatic radiology report generation, and exploring the application of large language models in clinical decision support.
Qinghua Zhou is a Researcher in the Department of Mathematics at King's College London , within the Faculty of Natural, Mathematical & Engineering Sciences . His work focuses on robust, stable, and trustworthy AI systems through theoretical and computational analysis of computer vision and large language models. Education: BSc in Applied Mathematics and Physics from the University of Sydney PhD in AI from the University of Leicester Research interests include: Adversarial and stealth attacks on AI models Model watermarking and locking without retraining High-dimensional low-sample-size data analysis Neuromorphic and reservoir computing Weight manipulation with theoretical guarantees His recent publications highlight trends in: 2024–2025 : AI safety via deterministic weight edits, vulnerabilities of LLMs, and theoretical frameworks for adversarial robustness. 2023 : Ensemble methods for medical data, intrinsic dimensionality, and neuromorphic feature space optimization. Collaborations include researchers such as Dr. Oliver Sutton and Prof. Ivan Tyukin. Projects involve developing high-performance software and interactive demos for AI verification.
Leonardo Tenori is an Associate Professor at the Department of Chemistry, University of Florence, affiliated with the Magnetic Resonance Center (CERM). He holds a master’s degree in Chemistry (2002) and a PhD in Structural Biology (2008), both from the University of Florence. His research focuses on metabolomics, particularly applying Nuclear Magnetic Resonance (NMR) spectroscopy to biomedical, pharmacological, and agricultural challenges. Education: Master’s in Chemistry, University of Florence, 2002 PhD in Structural Biology, University of Florence, 2008 Research Interests include metabolomics applications in disease diagnosis (e.g., celiac disease, breast cancer, cardiovascular disorders), development of statistical algorithms for data analysis (e.g., KODAMA), and collaborations in clinical and agricultural research. His work emphasizes metabolic biomarker discovery and integrative omics approaches. Recent research highlights include studies on stroke outcome prediction via blood biomarkers, metabolomic profiling of plant-based beverages, and lipidomic analysis of human sperm. These projects underscore his expertise in NMR-based metabolomics and its translational applications. Awards: 2015 Fellowship from the Italian Foundation Veronesi for melanoma metabolomics research He has contributed to over 100 publications and collaborates internationally. His work includes developing predictive models for disease recurrence in cancer patients and exploring metabolomic signatures in chronic diseases. He also investigates applications in agriculture, such as olive oil quality assessment and dairy cow health monitoring. Labs/Teams: Part of the Magnetic Resonance Center (CERM) at the University of Florence, contributing to interdisciplinary research in structural biology and metabolomics.
Dr. Daniel Pak is a Research Fellow in the Department of Radiology & Biomedical Imaging at Yale School of Medicine, Yale University. His research focuses on advancing medical imaging techniques through deep learning and computational methods, with a particular emphasis on cardiovascular biomechanics, MRI reconstruction, and automated meshing for personalized medicine. He also explores the environmental and public health impacts of synthetic chemicals, particularly their causal links to mitochondrial dysfunction and diabetes. His work integrates interdisciplinary approaches, combining machine learning with biomedical engineering to address challenges in medical diagnostics and treatment planning. Notable contributions include developing AI-driven tools for multimodal modeling of aortic stenosis and robust automated calcification meshing. He has also contributed to cross-modality segmentation frameworks and data-driven heart geometry modeling. While no formal advisees are listed, his research collaborations span diverse fields. His publications highlight a commitment to translational research, bridging theoretical advancements with clinical applications. Awards and grants are not explicitly mentioned in the provided materials.
Marta Marrón Romera is an Associate Professor at the Department of Electronics, School of Engineering, University of Alcalá. Her research focuses on intelligent systems, embedded systems, mobile robotics, computer vision, and assistive technologies. She holds a PhD in Electronics Technology (2008) and has over 25 years of research experience, including a 15-week stay at KTH Royal Institute of Technology in Sweden. Her work spans probabilistic algorithms for autonomous robots, machine learning, and applications in smart environments. She has led 33 research projects (19 national, 2 European, and 20 private) and holds 3 patents. She has authored 39 peer-reviewed journal articles (36 in JCR-indexed journals, 24 Q1-Q2), 5 book chapters, and over 70 conference communications. Her h-index ranges from 17 to 21, with over 1,200 citations. She has supervised 2 Cum Laude PhD theses (European), 4 ongoing PhDs, 19 master's, and 35 bachelor's theses. She actively participates as an evaluator for national research agencies, including the Spanish State Research Agency and Andalusian Quality Agency. She has organized 9 workshops and a Special Session on Multisensor Signal Processing. Her research is recognized by continuous 'sexenios' (research activity valuations) since 2015. Key contributions include the GEINTRA group's work on sensor fusion for intelligent spaces, autonomous wheelchairs, and human activity monitoring. She pioneers edge computing solutions for real-time surveillance and healthcare applications.
Andreas Kamilaris serves as Associate Professor at the Digital Society Institute, Cyprus University of Technology, specializing in Pervasive Systems. His research bridges Internet of Things infrastructure with environmental applications, particularly in water quality monitoring, agricultural technology, and biodiversity conservation through AI-driven solutions. His primary research domains include Internet of Things (100% fingerprint weight), Machine Learning (98%), and Environmental Informatics, with significant contributions to disinfection byproduct analysis in water systems, satellite-based tree classification, and image-based insect monitoring. Technical approaches emphasize multimodal deep learning, real-time sensor networks, and geospatial modeling for environmental challenges. Recent publications (2024-2025) demonstrate a clear trajectory toward operationalizing AI for environmental monitoring, featuring country-scale digital twin implementations, health impact assessments of water contaminants, and biodiversity conservation tools. These works consistently integrate Cyprus-based case studies with international collaborations. Scientific recognition includes: Most Patents Award (2019) for IoT and Search Engine innovations Collaborative projects like the InsectAI COST Action and EuropaBON EBV workflow templates indicate substantial team leadership in environmental data science initiatives, though specific grant details remain unreported in source materials. He actively contributes to the Digital Society Institute's research ecosystem through the Pervasive Systems group, with recent work on GAEA establishing foundations for real estate environmental impact modeling through geospatial digital twins.
Eric BENOIT is an Associate Professor at the University of Savoie Mont Blanc, affiliated with Polytech Annecy-Chambéry and the LISTIC research laboratory. His research focuses on Machine Learning, Measurement Science, Information Fusion, and their applications in IoT, Human-Machine Interaction, and homecare. He actively contributes to the scientific community through leadership roles in IMEKO, including Vice-President for External Relations (since 2025) and former Chairperson of TC7 (Measurement Science). Education: PhD in Physics, University Joseph Fourier, Grenoble 1 (1993) Master’s in Physics (1988) DEA in Measurement and Instrumentation (1988) Research Interests: Machine learning algorithms, distributed fusion systems, fuzzy scales, and weakly defined measurements. His work integrates software engineering and IoT for healthcare and ambient intelligence. Key application areas include assistive technologies for elderly care and music-based disability support. Awards: 2022 Finkelstein Award from InstMC for international contributions to measurement science. Advising & Projects: Supervised multiple PhD theses on topics like environmental impacts of AI, IoT for autism diagnosis, and smart furniture. Current projects include ambient intelligence and music-disability-IoT interfaces. Labs/Teams: Co-host of the ReGaRD research theme at LISTIC. Active in editorial roles for journals like Measurement and Acta IMEKO .
Dr. Boyu Kuang is a Research Fellow in Computer Vision and Artificial Intelligence at Cranfield University, affiliated with the Centre for Computational Engineering Sciences. His work bridges academic research with industrial applications in aviation, robotics, and energy systems. Current role: Research Fellow in Computer Vision and AI Key affiliations: Centre for Computational Engineering Sciences Research interests include semantic segmentation , object detection , weakly and self-supervised learning , multi-modal perception , and vision-language foundation models . His methodology emphasizes robust AI systems for low-resource industrial environments , with applications in aviation maintenance , robotics , and energy infrastructure monitoring . He leads the Artificial Intelligence and Machine Learning module at Cranfield. Key activities include the UKRI, ATI, and Airbus-funded ONEHeart project on autonomous systems and intelligent inspection. Collaborations span Stanford University , King’s College London , Civil Aviation University of China , and industry partners like Airbus and Leidos . Publications focus on vision-language models , multi-modal perception , and industrial AI . As an Editorial Board Member for Discover Artificial Intelligence (Springer Nature), he contributes to academic governance. Peer review experience includes 60+ journal manuscripts for venues like IEEE Transactions on Image Processing and Elsevier Neural Networks . Facilities utilized include DARTeC and AIRC .
Xiaoyu Zhang is a Professor at the Center for Intelligent Computing Systems within the Institute of Information Engineering at the Chinese Academy of Sciences in Beijing, China. Having earned a PhD from the Institute of Automation, Chinese Academy of Sciences in 2010, Dr. Zhang has established a distinguished research career spanning over 15 years with over 130 publications. Dr. Zhang's research focuses on cutting-edge areas of computer vision and artificial intelligence, with particular expertise in face recognition systems, deepfake detection, knowledge graph reasoning, and network security. Their work bridges theoretical innovation with practical applications, addressing critical challenges in biometric security, multimedia analysis, and privacy protection. Recent research demonstrates pioneering work in masked face recognition, temporal action localization, and graph-based representation learning. Analysis of Dr. Zhang's publication trends reveals a strong emphasis on face recognition technology (appearing in approximately 40% of recent publications), with significant contributions to deepfake detection and knowledge graph reasoning. The research consistently combines deep learning methodologies with domain-specific innovations, showing evolution from traditional computer vision approaches to sophisticated transformer-based architectures and graph neural networks. Key thematic areas include robustness against adversarial conditions, handling of incomplete or masked data, and cross-domain knowledge transfer. Dr. Zhang has been instrumental in major collaborative projects including the FRCSyn Challenge series, which has become a benchmark for evaluating face recognition systems with synthetic data. Their work has been published in top-tier venues including IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing, and the International Journal of Computer Vision. Within the Center for Intelligent Computing Systems, Dr. Zhang leads research initiatives focused on developing next-generation biometric security systems and intelligent multimedia analysis frameworks. The research group maintains strong collaborations with international institutions including ETH Zurich, Imperial College London, and various Chinese universities, fostering a dynamic environment for interdisciplinary research at the intersection of computer vision, machine learning, and security.
Yanbo Zhang is a researcher affiliated with Nanyang Technological University , holding a PhD in Wireless Communication and Sensing (2022). His work spans machine learning , biomedical engineering , and signal processing with applications in healthcare, communication systems, and computer vision. Education: PhD from Nanyang Technological University, Singapore (2022) Research areas include: Machine learning for medical diagnosis (predicting infections, cardiovascular events, and cancer) Advanced analog-digital converter designs for precision electronics Diffusion models and evolutionary algorithms in AI Federated learning for imbalanced data and cybersecurity Recent publications focus on multi-label learning for disease prediction, secure communication architectures , and deep learning applications in medical imaging. Collaborations include institutions in Singapore, China, and international teams.
Dr. Abigail Koay is an Honorary Research Fellow at the University of Queensland's School of Electrical Engineering and Computer Science. Her research focuses on cybersecurity, machine learning applications in industrial systems, and healthcare technology. She has contributed to projects such as real-time cyber-attack detection using weakly supervised learning, supported by UQ Cyber Seed Funding (2021–2022). Her work spans multiple disciplines including: Cybersecurity for Industrial Control Systems (ICS) IoT network anomaly detection using fog-assisted frameworks Machine learning for medical imaging (e.g., glaucoma detection) AI-driven cybersecurity strategies for smart grids Recent publications highlight advancements in: Irregular time series analysis using GNNs Positive-unlabeled learning with random forests Domain generalization in retinal image analysis Dr. Koay has authored/co-authored 15+ peer-reviewed articles across journals like Frontiers of Computer Science , IEEE Access , and conferences including NeurIPS and ISGT Asia. She collaborates with industry partners on projects like Plan2Defend for smart grid security and SDGen for synthetic cybersecurity dataset generation. Her research integrates theoretical machine learning with practical cybersecurity challenges, emphasizing real-world applicability in critical infrastructure and healthcare systems.
Xuan Wang is an Assistant Professor in the Department of Computer Science at Virginia Tech, affiliated with the Sanghani Center for Artificial Intelligence and Data Analytics. She holds a Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign (UIUC), with additional M.S. degrees in Statistics and Biochemistry from UIUC, and a B.S. in Biological Science from Tsinghua University. Her research focuses on Natural Language Processing (NLP), Data Mining, AI for Sciences, and AI for Healthcare, emphasizing applications in complex reasoning with LLMs, multi-modal science foundation models, and healthcare informatics. Her work has been recognized through awards including the Nvidia Academic Grant (2025), Cisco Research Award (2025), and NSF NAIRR Pilot Award (2024-2025). She has organized workshops at ACL, VL/HCC, and ICDM, and serves on program committees for top conferences like NeurIPS, EMNLP, and KDD. Xuan's research spans scientific text mining (e.g., knowledge extraction from biomedical literature), multi-agent LLM systems for clinical triage, and foundational models for multi-omics data analysis. Her lab actively collaborates with institutions like Children’s National Hospital and the Fralin Biomedical Research Institute, with funding from NSF, CCI, and industry partners. Education: Ph.D. in Computer Science, UIUC (2022) M.S. in Statistics, UIUC (2017) M.S. in Biochemistry, UIUC (2015) B.S. in Biological Science, Tsinghua University (2013) Grants & Awards: NVIDIA Academic Grant (2025) – Small LLM Agent Systems Cisco Research Award (2025) – Complex Reasoning with LLMs NSF NAIRR Pilot (2024-2025) – Multi-omics Analysis Lab & Teams: Wang Lab focuses on AI-driven biomedical research, including single-cell omics analysis, brain signal interpretation, and LLM-based scientific discovery. Collaborations include the Virginia Tech Presidential Postdoctoral Fellowship program and industry initiatives like the Amazon + VT Center for Efficient ML.
Professor Peter Gardner is a faculty member at The University of Manchester, holding the position of Professor of Analytical and Biomedical Spectroscopy within the School of Chemical Engineering and Analytical Science, located at the Photon Science Institute. His expertise lies in vibrational spectroscopy, particularly its application to bioanalytical and biomedical challenges, with a focus on prostate and urological cancers. Education: BSc in Chemistry from the University of East Anglia (UEA), PhD in Physical Chemistry from UEA (1988). Postdoctoral research at the Fritz-Haber Institute (Berlin) and University of Cambridge. Joined UMIST as a Lecturer in 1994, promoted to Senior Lecturer in 2000. Moved to the University of Manchester in 2004 as part of the Instrumentation and Analytical Science group. Research Interests: Development of infrared and Raman spectroscopy techniques for cancer diagnosis, including automated diagnostic systems, scattering correction algorithms, and integration with digital pathology and AI. Current projects include CLIRPath-AI, aiming to improve cancer risk stratification via spectral and AI-driven methods. Key Collaborations: Paterson Institute for Cancer Research (now Cancer Research UK Manchester Institute), groups in Dublin and Norway for algorithm development. Active in international conferences and has supervised numerous postgraduate researchers. Awards: None explicitly listed in the text.
Henrik Kalisch is a Professor of Applied Mathematics at the Department of Mathematics, University of Bergen, where he also serves as Deputy Head of Department. His research focuses on mathematical modeling of nearshore processes, wave breaking, surfzone circulation, and wave hazards in coastal zones. Dr. Kalisch received his Ph.D. in 2001 from the University of Texas at Austin. His academic career has established him as a leading researcher in fluid mechanics, partial differential equations, and numerical analysis, with over one hundred scientific publications to his name. Professor Kalisch's research spans several key areas in applied mathematics and fluid dynamics. His work on surface water waves investigates fluid particle motion, wave breaking mechanisms, wave shoaling processes, and the influence of vorticity on wave dynamics. In the domain of wave-ice interaction , he studies moving loads on ice sheets, marginal ice zone dynamics, and interactions with internal waves. His contributions to hyperbolic conservation laws include work on singular solutions and their physical interpretation, while his research on mathematical properties of model equations examines existence, uniqueness, and stability of traveling waves and soliton interactions. His research has practical applications in wave energy devices, tidal energy, carbon storage, and ice road safety. His recent publications reveal a strong focus on developing and analyzing mathematical models for wave phenomena, particularly Boussinesq-type models, KdV equations, and their variants. The research shows increasing integration of computational methods with theoretical analysis, and growing attention to practical applications in coastal engineering and polar science. There's also a notable trend toward interdisciplinary collaboration, particularly with oceanographers and engineers working on real-world wave problems. Professor Kalisch serves as co-editor-in-chief for "Water Waves: An interdisciplinary journal," published by Birkhäuser-Springer-Nature, demonstrating his leadership in the field. Methods for real-time wave forecasting and phase control of wave energy converters (Bergen Universitetsfond, 2021-2022) MegaRoller (European Commission Horizon 2020 grant) Norwegian Research Network in Mathematical Models in Geophysical Flows (Research Council of Norway, 2016-2019) Internal Waves in the Marginal Ice Zone (Hydralab grant from European Commission) Nonlinear PDE in Spaces of Analytic Functions (Research Council of Norway, 2012-2017) Wavemaker (Research Council of Norway, 2006-2010) Professor Kalisch has supervised numerous graduate students, including current PhD candidates Enrique Martinez, Olufemi Ige, and Anders Norevik, as well as several Master's students. His former PhD students include Maria Bjørnestad (2021), Evgueni Dinvay (2019), Vincent Teyekpiti (2018), and others who have gone on to careers in academia, industry, and research institutions worldwide. He has chaired curriculum committees and developed courses in applied mathematics, fluid mechanics, and numerics at both undergraduate and graduate levels. His research group at the University of Bergen includes postdoctoral researchers like Bashar Khorbatly, adjunct professors like Francesco Lagona, PhD students, and Master's students working collaboratively on various aspects of wave dynamics and mathematical modeling.
