جستجوی استاد راهنمای دانشگاهی

Andreas Kamilarisمشاهده پروفایل

دانشیار

• Internet of Things
• Machine Learning
• Artificial Intelligence
• +۵ مورد دیگر

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 BENOITمشاهده پروفایل

دانشیار

• Machine Learning
• Measurement Science
• Information Fusion
• +۴ مورد دیگر

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 .

Boyu Kuangمشاهده پروفایل

پژوهشگر ارشد

• Semantic Segmentation
• Object Detection
• Weakly Supervised Learning
• +۹ مورد دیگر

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 .

• Signal Processing
• Biomedical Signal Processing
• Image Reconstruction
• +۱۲ مورد دیگر

Rabab K. Ward is a Professor in the Department of Electrical and Computer Engineering at the University of British Columbia, Faculty of Applied Science, Vancouver, Canada. She is a leading figure in signal processing with extensive contributions to biomedical engineering, image reconstruction, and machine learning. She served as President of the IEEE Signal Processing Society (2016–2017) and continues to be actively involved in research and leadership. Her research focuses on signal processing , biomedical signal analysis , compressed sensing , medical image fusion and reconstruction , and deep learning for healthcare applications . She has pioneered methods in EEG and CT image processing, epileptic seizure detection, and 3D human pose estimation. The recent publications highlight her work in 3D face and human modeling , GAN-based forensics , brain-computer interfaces , and medical video analysis . Her research integrates advanced machine learning with practical engineering solutions for clinical and robotic systems. Her scientific awards include being an IEEE Fellow , Distinguished Lecturer of the IEEE Signal Processing Society , and serving as President of the IEEE Signal Processing Society . She has advised numerous students and researchers in areas such as compressed sensing , EEG signal processing , image reconstruction , and deep learning . Her collaborative work includes significant grants and projects in biomedical engineering , neurotechnology , and autonomous systems . Dr. Ward is associated with research labs and teams focused on signal processing for healthcare , medical imaging , and intelligent systems at UBC, fostering interdisciplinary collaboration in engineering and medicine.

• Face Recognition
• Deep Learning
• Computer Vision
• +۵ مورد دیگر

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.

• Machine Learning
• Biomedical Engineering
• Signal Processing
• +۳ مورد دیگر

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.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۹ مورد دیگر

Han Chen is an active academic researcher in the fields of artificial intelligence, machine learning, computer vision, and biomedical informatics, with extensive publications in top-tier venues such as CVPR, ICLR, IEEE Transactions, and Bioinformatics. The research spans diverse applications including medical image analysis, deepfake detection, federated learning, and multimodal affective computing. The research interests of Han Chen include artificial intelligence, machine learning, computer vision, medical image analysis, graph neural networks, and cybersecurity. The work emphasizes deep learning architectures, multimodal fusion, and robustness in AI systems, particularly in healthcare and security applications. Key themes include disentangled representation learning, contrastive learning, and transformer-based models. The recent articles demonstrate a strong trend toward multimodal AI, with applications in medical diagnostics (e.g., mammography, nasopharyngeal imaging), trustworthy AI (e.g., deepfake detection, backdoor defense), and efficient learning paradigms (e.g., federated learning, weak supervision). There is a consistent focus on enhancing model interpretability, robustness, and real-world applicability across domains. Scientific Awards: No awards explicitly mentioned in the provided text. Advising and Grants: There is no explicit information about students or advising. However, the high volume of collaborative research suggests active involvement in research teams and likely supervision of graduate students. Funding sources are not mentioned, but the scope of work implies support from national or institutional research grants in AI and health informatics. Labs and Teams: While no specific lab or team is named, the collaborative nature of the work—especially with researchers in medical imaging and federated learning—suggests membership in a multidisciplinary AI research group, possibly affiliated with a medical school or engineering institute.

Abigail Koayمشاهده پروفایل

پژوهشگر ارشد

• Cyber Security
• Machine Learning
• Industrial Control System (ICS) Security
• +۳ مورد دیگر

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 Wangمشاهده پروفایل

استادیار

• Natural Language Processing
• Data Mining
• AI for Sciences
• +۷ مورد دیگر

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.

• Vibrational Spectroscopy
• Bioanalytical Spectroscopy
• Biomedical Spectroscopy
• +۳ مورد دیگر

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 Kalischمشاهده پروفایل

استاد

• Applied Mathematics
• Fluid Mechanics
• Partial Differential Equations
• +۷ مورد دیگر

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.

Sourya Royمشاهده پروفایل

استادیار

• Theoretical Computer Science
• Pseudorandomness
• Coding Theory
• +۴ مورد دیگر

Sourya Roy is an Assistant Professor in the Department of Computer Science at the University of Iowa. Prior to his academic career, he worked as a Data Scientist at Foursquare. He earned his PhD in Computer Science from the University of California, Riverside in 2022, advised by Silas Richelson and Amey Bhangale. Education: PhD in Computer Science (University of California, Riverside, 2022) His research spans theoretical and applied domains, focusing on pseudorandomness, coding theory, and cryptography in theoretical computer science, while also contributing to computer vision and machine learning applications. Recent publications emphasize expander graph theory, coding algorithms, and spatio-temporal modeling. Dr. Roy is actively seeking PhD students to collaborate on theoretical computer science projects. His publications highlight expertise in pseudorandomness, expander graphs, and scalable unsupervised learning techniques. Key conferences include FOCS, ECCV, and IEEE Transactions on Information Theory.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۷ مورد دیگر

Dr. Tryphon Lambrou is a Senior Lecturer in Computing Science at the School of Natural and Computing Sciences, University of Aberdeen, where he joined in January 2022. He previously served as a Senior Lecturer at the School of Computer Science, University of Lincoln, and holds an Honorary Lecturer position at the Department of Medical Physics & Bioengineering, University College London. He has held leadership roles including Interim Vice-Dean for the Joint Institute of Data Sciences and Artificial Intelligence (June 2022–January 2023) and Programme Director at the University of Lincoln. Senior Lecturer, University of Aberdeen (2022–present) Senior Lecturer, University of Lincoln (prior to 2022) Honorary Lecturer, UCL Medical Physics & Bioengineering External Examiner, MSc AI & Data Science, University of Hull Dr. Lambrou's research spans artificial intelligence, machine learning, and computer vision with strong applications in medical imaging and diagnostics. His work focuses on brain tumor segmentation, fetal ultrasound analysis, radiomics, and physiological monitoring using thermal imaging and robotics. He has made significant contributions to multimodal MRI analysis, automated biometry, and deep learning architectures such as MDA-Unet. His recent publications (2022–2024) emphasize deep learning for medical segmentation, out-of-distribution detection in gastrointestinal imaging, and weakly supervised pre-training in neuroimaging. Earlier works (2000–2018) highlight arterial blood flow modeling, PET-CT motion correction, and fetal heart segmentation, demonstrating a long-standing expertise in biomedical signal and image processing. Member, IEEE Professional Member, ACM Full Member, Institute of Physics and Engineering in Medicine (IPEM) Dr. Lambrou actively supervises PhD students in Computing Science and has led research degrees (PGR) at Lincoln. His research has been funded through EPSRC and MRC via the Interdisciplinary Research Consortium. He has contributed to major conferences and journals in medical imaging, AI, and biomedical engineering. He is affiliated with the Centre for Medical Image Computing (CMIC) at UCL and has collaborated extensively on projects bridging computer science and clinical applications, particularly in oncology, neurology, and obstetrics.

Jyoti Joshi Dhallمشاهده پروفایل

مدرس ارشد

• Affective Computing
• Multimodal Behaviour Analytics
• Digital Health
• +۴ مورد دیگر

Jyoti Joshi Dhall is a Senior Lecturer in the Department of Human Centred Computing at Monash University, where she conducts interdisciplinary research at the intersection of artificial intelligence, behavioral analytics, and digital health. Her work contributes to UN Sustainable Development Goals, particularly in health and well-being. Her research focuses on affective computing , multimodal behavior analytics , and human-centered AI , with applications in mental and physical health monitoring. She employs advanced machine learning techniques to analyze behavioral signals such as facial expressions, speech, and body movements for detecting conditions like pain, apathy, and depression. Recent publications show a strong trend in applying deep learning models—such as LSTM-DNN and anomaly detection networks—to clinical and assistive technologies. Her work appears in top-tier venues including IEEE conferences and the ACM Handbook series, reflecting impactful contributions to multimodal user interfaces and emotion-aware systems. She has collaborated with international researchers and her work has been cited in Scopus and referenced in patents. Although no specific awards are listed, her research has attracted attention from news outlets and academic platforms like Mendeley. Jyoti Joshi Dhall advises students and likely supervises research projects in human-centered computing, though specific advisees are not listed. Her research is supported by institutional and possibly external grants, given the scope and publication record. She is part of a broader research network focused on AI for healthcare, contributing to both theoretical and applied advancements in the field. She is affiliated with a research team or lab working on multimodal interaction and affective computing, though the specific lab name is not mentioned in the provided text.

Asad Malikمشاهده پروفایل

مدرس

• Multimedia Forensics and Security
• Image Processing
• Deep Learning
• +۴ مورد دیگر

Asad Malik is a Lecturer at the School of Information Technology, Monash University, Malaysia, since December 2023. Previously, he served as an Assistant Professor in the Department of Computer Science at Aligarh Muslim University, India, for two years. He holds a Ph.D. in Information Security from Southwest Jiaotong University, China (2020), an MCA from Jamia Millia Islamia (2015), and a BSc (Hons.) in Computer Application from Aligarh Muslim University (2012). His research focuses on Multimedia Forensics and Security, Deep Learning for Cyber Security, and DeepFake detection. He is a technical member of the Asia-Pacific Signal and Information Processing Association (APSIPA) and actively reviews for top journals and conferences. His work aligns with UN Sustainable Development Goals, particularly in advancing Education and Innovation. Malik’s publications (34+ outputs) emphasize robust watermarking techniques, reversible data hiding in encrypted media, and AI-driven solutions for security and environmental challenges. Recent trends highlight innovations in deepfake detection, transformer-based audio classification, and AI applications in renewable energy modeling. He is currently accepting PhD students and collaborates on projects combining cybersecurity, digital forensics, and machine learning. No notable awards are listed, but his research contributions reflect significant academic engagement in information security domains.