جستجوی استاد راهنمای دانشگاهی

Alistair Pooreمشاهده پروفایل

استاد

• Marine Ecology
• Evolutionary Biology
• Coastal Marine Ecosystems
• +۳ مورد دیگر

Professor Alistair Poore serves as Head of the School of Biological, Earth and Environmental Sciences at the University of New South Wales. With a PhD from UNSW (1999) and BSc(Hons) from Monash University (1992), he has established himself as a leading marine ecologist focusing on coastal ecosystems. His research program spans multiple interconnected themes: Ecology and evolution of interactions among marine organisms Impacts of herbivores on plant communities Human impacts on coastal marine ecosystems Adaptation of marine organisms to environmental stress Use of citizen science data for biodiversity monitoring Professor Poore's recent publications demonstrate a strong focus on threatened seagrass ecosystems, particularly Posidonia australis, examining responses to climate change, tropicalization, and physical disturbance. His work increasingly integrates citizen science approaches, using recreational diver photographs and other unconventional data sources for ecological monitoring. The research shows a progression from fundamental ecological theory toward applied conservation solutions, with extensive international collaborations across Australian, Pacific, and global marine environments. Scientific recognition includes: Elected Fellow of the Royal Society of New South Wales (2020) Provasoli Award from the Phycological Society of America (2014) Citation for Outstanding Contributions to Student Learning (2013) UNSW Vice Chancellor's Award for Teaching Excellence (2012) Professor Poore actively supervises PhD students working on diverse marine topics including amphibian detection using DNA, bluebottle behavior, artificial light impacts, seagrass restoration, and reef monitoring. His teaching portfolio includes BEES2041 Data Analysis for Life and Earth Scientists, BIOS2031 Biology of Invertebrates, and BIOS3091 Marine and Aquatic Ecology, complemented by freely available Environmental Computing tutorials. His research bridges fundamental ecological questions with practical conservation applications, particularly in Australian coastal environments, with growing emphasis on how climate change reshapes marine biodiversity and ecosystem function through processes like tropicalization and habitat fragmentation.

Ilker Hacihalilogluمشاهده پروفایل

دانشیار

• Artificial Intelligence in Healthcare
• Medical Image Processing
• Machine Learning Applications
• +۸ مورد دیگر

Dr. Ilker Hacihaliloglu is an Associate Professor in the Department of Radiology and Medicine at the University of British Columbia's Faculty of Medicine. He holds a PhD from UBC (2010) and has held faculty positions at Rutgers University (2014-2021). His research focuses on applying machine learning to medical imaging, aiming to bridge clinical and engineering research for practical healthcare solutions. Key areas include AI-driven diagnostics, ultrasound imaging, and personalized medicine. Education: B.Sc. and M.Sc., Istanbul Technical University (2001, 2004) Ph.D., University of British Columbia (2010) Postdoctoral Fellow, Vancouver General Hospital (2010-2013) Research Interests: Artificial Intelligence in Medical Imaging Ultrasound-Based Diagnostics Image-Guided Surgery Computer-Aided Diagnostics for Orthopedics and Neurosurgery His work emphasizes explainable AI, standardization in machine learning metrics, and clinical translation of technologies. Scientific Contributions: Over 35 peer-reviewed articles (2019-2025) highlight innovations in medical image segmentation, AI-driven radiomics, and cross-modality imaging. Recent trends focus on synthetic data augmentation, diffusion models for medical imaging, and curriculum development in AI for healthcare. Grants and Advising: No explicit grants mentioned, but active mentorship in interdisciplinary research teams. Collaborations span clinicians, engineers, and computer scientists to address real-world healthcare challenges. Labs/Teams: Engaged in computational ultrasound and AI-driven diagnostics initiatives, though specific lab names are not provided in the text.

Richard Guestمشاهده پروفایل

استاد

• Biometric Systems
• Forensic Technologies
• Applied Artificial Intelligence
• +۳ مورد دیگر

Richard Guest is a Professor of Biometric Technologies at the University of Southampton's School of Electronics and Computer Science. He holds an Honorary Professorship at the University of Kent and a Visiting Researcher position at Purdue University. His expertise spans biometric systems, forensic technologies, and ethical AI applications. Previously, he served as Deputy Divisional Director and Head of Engineering at the University of Kent before joining Southampton in 2024. Education: BEng (Hons) in Computer Science (University of York, 1995), PhD in Electronic Engineering (University of Kent, 2000). Research focuses on applied AI, biometric security, forensic image analysis, and privacy-preserving techniques. He leads ISO/IEC standardization efforts for biometric data interchange and advises the UK Home Office on biometric ethics. His work includes mobile facial recognition systems and explainable AI for legal settings. Publications span mobile biometric verification, forensic verification tools, and transformer-based AI models. He has secured over £5M in research funding and supervises PhD students in Computer Science. Awards include Fellowships from the British Computer Society and IET, Chartered Engineer status, and membership in EPSRC's Peer Review College.

Sharib Aliمشاهده پروفایل

استادیار

• Biomedical Image Analysis
• Computer Vision
• Machine Learning
• +۱۲ مورد دیگر

Dr. Sharib Ali is a Lecturer (Assistant Professor) in the School of Computer Science at the University of Leeds, Faculty of Engineering and Physical Sciences. He is affiliated with the Leeds Cancer Research Centre and actively contributes to research in biomedical image analysis and computer vision. His work bridges cutting-edge AI with clinical applications, particularly in endoscopy and surgical technologies. PhD in Medical Image Analysis, University of Lorraine, France MSc in Computer Vision (by research), University of Burgundy, France Dr. Ali's research focuses on biomedical image analysis , computer vision , and machine learning , with applications in early cancer detection , computational endoscopy , and 3D reconstruction . He develops robust algorithms for segmentation, registration, depth estimation, and mosaicking, using both classical mathematical models and deep learning. His work emphasizes translational research and generalisability in real-world clinical settings. The recent publications highlight a strong trend in generalisability assessment , multi-modal data fusion , and AI benchmarking in endoscopy. His work spans from foundational algorithm development to clinical deployment, including federated learning , mixed reality in surgery , and multi-centre datasets , addressing key challenges like bias, data imbalance, and privacy. Dr. Ali has co-supervised multiple DPhil/PhD students and currently supervises several PhD candidates at the University of Leeds, University of Oxford, and Tec de Monterrey. He is actively involved in securing research funding and leading projects such as Leveraging multi-modality data for targeted biopsy and Federated learning in healthcare . He is a founding member of NAAMII, Nepal, where he volunteers to train students from LMICs. He also organizes international research initiatives including the EndoCV and P2ILF challenges at MICCAI, and serves on program committees and as a reviewer for journals like Nature Communications and Medical Image Analysis . His research is conducted within interdisciplinary teams, collaborating with clinicians from Oxford NHS University Hospitals, neuroscientists at Forschungszentrum Jülich, and engineers across Europe. He leads the development of open tools and datasets to advance the field of endoscopic computer vision.

• Artificial Intelligence
• Robotics
• Data Science
• +۴ مورد دیگر

Cecilio Angulo Bahón is a full Professor at the Polytechnic University of Catalonia (UPC), affiliated with the Barcelona School of Industrial Engineering (ETSEIB) and the Department of Systems, Automatics and Industrial Informatics Engineering . He leads research in Artificial Intelligence and Robotics , with significant contributions to healthcare data analytics, digital twins, and human-robot collaboration. His research spans machine learning for medical data harmonization, generative adversarial networks in health informatics, and evolutionary algorithms for control systems. Recent publications focus on synthetic healthcare data generation, climate-resilient agriculture , and UMAP-based data analysis . His work bridges AI theory with practical applications in industrial and healthcare domains. Scientific awards include the Sant Jordi 2023 Digital Polytechnic Initiative Award . He has supervised doctoral candidates like Carlos Flores-Vázquez and N. Raya, with key collaborations at the IDEAI-UPC Intelligent Data Science and AI Research Group and the Institute of Robotics and Industrial Informatics (CSIC-UPC).

Georgios Bardisمشاهده پروفایل

استادیار

• Intelligent Computer Graphics
• Declarative Modeling
• Multicriteria Decision Analysis
• +۴ مورد دیگر

Georgios Bardis is a permanent Assistant Professor at the Department of Informatics and Computer Engineering , School of Engineering , University of West Attica . He holds a PhD in Informatics from University of Limoges (2006), an MSc in Software Systems from University of California, Santa Barbara (1994), and a Diploma in Computer Engineering & Informatics from University of Patras (1992). His career spans multiple academic roles, including Lecturer at University of West Attica (2018-2021) and Professor of Applications at TEI of Athens (2010-2018). Research Interests : Focus on Intelligent Computer Graphics , Declarative Modeling , and Multicriteria Decision Analysis . His work integrates AI into 3D scene synthesis, urban planning, and semantic decision systems. Awards : Master Microsoft Office Specialist (MOS), 2003 NAT Scholarships for Academic Excellence (1989-1992) 1984 Monetary Prize from Hellenic Mathematical Society Leadership : Member of AKIIS Research Lab (University of West Attica), Editorial Board of International Journal of Systems Biology and Biomedical Technologies , and Reviewer for International Journal of Digital Earth . Publications : 5 peer-reviewed journals, 2 books, 8 book chapters, and 22 conference papers. Key areas include WebGL avatars, urban data analysis, and 3D modeling with AI.

Jason Raymondمشاهده پروفایل

دانشگاهی

• Therapeutic Ultrasound
• Focused Ultrasound Surgery
• High-Intensity Focused Ultrasound (HIFU)
• +۱۲ مورد دیگر

Jason Raymond, Ph.D., is a Research Assistant Professor at the Fralin Biomedical Research Institute (FBRI) at Virginia Tech-Carilion, where he also serves as the Focused Ultrasound Core Manager. He leads advanced research in therapeutic ultrasound and manages state-of-the-art facilities including MRI-guided focused ultrasound systems and a 9.4T small-bore MR-imaging platform. His work supports both preclinical and clinical trials in focused ultrasound applications. Ph.D. in Biomedical Engineering, University of Cincinnati Postdoctoral Research Fellow, University of Oxford Lecturer and Senior Research Associate, Department of Engineering Science, University of Oxford Junior Research Fellow, Kellogg College, Oxford B.S. and M.S. in Engineering Acoustics and Mechanical Engineering, Boston University Dr. Raymond’s research focuses on biomedical therapeutic ultrasound, with applications in drug delivery, blood-brain barrier opening, high-intensity focused ultrasound (HIFU) ablation, and acoustic cavitation. His expertise also extends to ultrasound contrast agents, photoacoustic imaging, and the physical interactions of sound and light in biological tissues. He has pioneered work in sonochemistry, microbubble dynamics, and non-invasive neuromodulation. His recent publications reveal a strong trend in leveraging ultrasound for chemical and biological applications, including sonochemical degradation, hydroxyl radical monitoring, and genetic engineering of biofilms. His work integrates acoustics, chemistry, and biomedical engineering to develop novel therapeutic and diagnostic tools. 38th F.V. Hunt Postdoctoral Research Fellowship, Acoustical Society of America Whitaker International Fellowship, Thoraxcenter–Erasmus Medical Center Junior Research Fellowship, Kellogg College, Oxford Dr. Raymond has been instrumental in establishing physical acoustics laboratories and has contributed to major advancements in focused ultrasound technology. He actively mentors researchers, supports collaborative projects across Virginia Tech, and provides technical guidance for industry and academic partners. His leadership in core facilities enables broad access to cutting-edge ultrasound technologies. He manages the Focused Ultrasound Technical Facilities at FBRI, which include a clinical transcranial MRI-guided focused ultrasound system (InSightec and Siemens) and a Bruker 9.4T MR system for small animal studies. These labs support research in non-invasive surgery, hyperthermia, ablation, and blood-brain barrier modulation.

• Computer Vision
• Pattern Recognition
• Facial Analysis
• +۵ مورد دیگر

Jorge Manuel M. C. Pereira Batista is an Associate Professor at the Department of Electrical and Computer Engineering , University of Coimbra, Portugal. He serves as a senior researcher at the Institute for Systems and Robotics (ISR-UC) and leads the Computer Vision Group. His career spans academic roles, research coordination, and industry collaboration. Academic Affiliation: University of Coimbra (Electrical & Computer Engineering Department) Research Institute: Institute for Systems and Robotics (ISR-UC) His research focuses on Computer Vision , Pattern Recognition , and applications of Differential Geometry in these fields. Key subdomains include facial analysis, visual surveillance, real-time vision systems, and machine learning integration. From 2010–2023, his recent publications highlight advancements in probabilistic models , transformer architectures , multi-branch learning , and biomimetic robotics . He has coordinated multiple funded projects such as: STORK : Avian protection systems NeuroCity : Intelligent street lighting 4D Facial Dynamics : Identity recognition iTRAFFIC : BRISA highway traffic monitoring His work bridges theoretical innovation (e.g., Riemannian manifold applications) with practical deployments in transportation, energy, and healthcare sectors.

Sebastiano VASCONمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Computer Vision
• Machine Learning
• +۴ مورد دیگر

Sebastiano Vascon is an Associate Professor at Ca' Foscari University of Venice's Department of Environmental Sciences, Computer Science and Statistics (DAIS), and affiliated with the European Center for Living Technology. He earned his PhD in 2016 from the Italian Institute of Technology and University of Genoa, focusing on evolutionary game theory in pattern analysis and computer vision. His postdoctoral work spanned institutions like the Technical University of Munich and ETH Zurich, where he specialized in Active Learning and multi-object tracking. His research merges AI with interdisciplinary challenges, including climate change, environmental science, and cultural heritage preservation. Key areas include graph neural networks, computer vision, and game-theoretic models. He leads projects like RePAIR (AI for cultural heritage reassembly) and EasyWalk (AI-driven mobility solutions), and contributes to initiatives like MEMEX (digital storytelling). Teaching spans courses in Deep Learning, Machine Learning for Environmental Applications, and AI in Cultural Management. Research projects include: RePAIR: AI-driven 3D puzzle solving for artifact reconstruction EasyWalk: Socially-aware navigation systems MEMEX: AI for inclusive digital storytelling Climate modeling with IceBoost framework Publications highlight innovations in trajectory forecasting, environmental risk assessment, and graph-based methods. He actively reviews for top conferences (CVPR, ECCV) and journals.

Birgit Kemmerlingمشاهده پروفایل

پژوهشگر

• Plant Molecular Biology
• Plant Pathology
• Immunology
• +۵ مورد دیگر

Dr. Birgit Kemmerling is a research group leader at the Center for Plant Molecular Biology (ZMBP) within the Faculty of Science at the University of Tübingen, Germany. Her research focuses on molecular mechanisms of plant innate immunity, particularly the role of leucine-rich repeat receptor-like kinases (LRR-RLKs) in pathogen recognition and defense signaling. Key projects include studying BAK1 and BIR3 kinases involved in immune receptor complex formation and regulation. She collaborates with institutions worldwide and has secured funding from DFG and EU programs such as AFGN, Bravissimo, and SFB1101. Teaching responsibilities include lectures and seminars on comparative immunity in plants and animals, practical courses in molecular mechanisms of innate immunity, and supervision of bachelor/master theses. Her group actively engages in interdisciplinary projects, integrating nanotechnology studies to assess immune responses to nanoparticles across species. Notable collaborators include Prof. Thomas Boller (Basel), Cyril Zipfel (Norwich), and Frans Tax (Tucson). Research highlights include discovering BAK1's dual role in brassinosteroid signaling and immune response regulation, as well as identifying novel LRR-RLK genes activated by pathogen infection. Current work explores nanomaterial biocompatibility and immune system interactions, leveraging advanced microscopy techniques like super-resolution imaging (OneFlowTraX software development).

John Darrell Van Hornمشاهده پروفایل

استاد

• Human Neuroimaging
• Brain Health and Disease Biomarkers
• Computational Neuroscience
• +۱۲ مورد دیگر

John Darrell Van Horn is a Professor at the University of Virginia, holding dual appointments in the School of Data Science and the Department of Psychology . He joined UVA in 2019 and is actively engaged in research and teaching, including mentoring capstone projects in the M.S. in Data Science program and teaching brain mapping with MRI. Education: Ph.D. in Psychology, University of London M.S. in Electrical and Computer Engineering, University of Maryland, College Park B.A. in Psychology, Eastern Washington University Dr. Van Horn's research centers on human neuroimaging and the application of data science to understand brain health and disease. His work integrates high-performance computation , multivariate statistics , network theory , and image processing to extract meaningful patterns and biomarkers from brain imaging data. He is a strong advocate for FAIR data principles and open science , promoting transparency and reproducibility in neuroscience research. His publications span top journals including Science , Nature , Nature Neuroscience , NeuroImage , and Biological Psychiatry , reflecting a sustained impact in the fields of neuroscience and data science. The body of work demonstrates a consistent focus on computational modeling of brain structure and function, with applications in psychiatry and cognitive neuroscience. Scientific Recognition: Fellow, Organization for Human Brain Mapping (OHBM) Dr. Van Horn has served as a mentor for capstone research projects in the M.S. in Data Science program. While specific grant funding is not detailed, his research has been widely disseminated and featured in both academic and popular media, indicating successful funding and recognition. His prior academic roles at UCLA, USC, Dartmouth, and NIH fellowships underscore a distinguished career trajectory. He is affiliated with the broader neuroscience and data science communities through publications, open science advocacy, and professional recognition. His lab or research group is not explicitly named, but his work clearly involves advanced computational analysis of neuroimaging datasets, likely involving collaborative teams focused on brain mapping and data sharing initiatives.

Lim Jit Yanمشاهده پروفایل

مدرس

• Few-Shot Learning
• Computer Vision
• Deep Learning
• +۳ مورد دیگر

Lim Jit Yan is a Lecturer at the School of Information Technology, Monash University Malaysia. He holds a PhD in Information Technology (2023) from Multimedia University, specializing in self-supervised metric-based meta-learning for few-shot image classification, and a B.IT (Hons) in Artificial Intelligence (2019) from the same institution. His research focuses on few-shot learning, computer vision, and deep learning, with contributions to medical image analysis (e.g., Covid-19 detection), transfer learning applications, and generative models. He has published extensively since 2021, with notable work on self-supervised feature fusion and prototypical networks for few-shot learning. Research interests include few-shot learning techniques, neural architecture design for image classification, and real-world applications like healthcare diagnostics and autonomous systems. Recent work emphasizes self-supervised learning and transformer-based models to address data scarcity challenges in AI. No formal scientific awards are listed. Advising details are not provided in the text, though his publications suggest collaborative research with colleagues like Lim K.M. and Lee C.P. His research spans theoretical advancements and applied projects, including work on pill image recognition, traffic sign detection, and brain tumor classification.

Abdullah Karamanمشاهده پروفایل

استاد

• Geophysical Engineering
• Hydrothermal Systems
• Seismic Data Analysis
• +۳ مورد دیگر

Abdullah Karaman is a Professor at the Department of Geophysical Engineering, Istanbul Technical University (ITU). His research focuses on geophysical modeling, subsidence due to longwall mining, hydrothermal systems, and seismic data analysis. He leads projects on geothermal applications using particle swarm optimization and collaborates internationally. Dr. Karaman has supervised 8 ongoing theses and has authored/co-authored over 17 publications since 1997. His work spans geophysical exploration, environmental geohazards, and subsurface imaging techniques. Education: Master of Science in Geophysics (1989) Research Interests: Hydrothermal system characterization using magnetotelluric methods Seismic inversion and diffraction imaging Geophysical monitoring of coal mine subsidence Optimization algorithms in geophysical data analysis Awards: None explicitly mentioned. Grants & Projects: Geothermal Applications of Particle Swarm Optimization Modeling Technique (2016–2021) Kordil Engineering Company: Geophysical consulting for international projects (2021) His lab focuses on integrating geophysical techniques with machine learning for high-resolution subsurface imaging.

Martin Wulf Gerdesمشاهده پروفایل

دانشیار

• eHealth
• Artificial Intelligence
• Machine Learning
• +۹ مورد دیگر

Martin Wulf Gerdes is an Associate Professor at the Department of Information and Communication Technology, University of Agder, Norway. With a Ph.D. in eHealth and over two decades of experience in telecommunications and healthcare technology research, he focuses on developing user-friendly, interoperable eHealth solutions using ICT and IoT. His career spans roles at Ericsson Eurolab and academic leadership in Norway. Education: M.Sc. equivalent in General Electrical Engineering (1998) from RWTH Aachen, emphasizing ICT. Ph.D.: Information and Communication Technologies with eHealth specialization (2019) from University of Agder. Research Interests: Gerdes explores AI-driven decision support systems, distributed eHealth infrastructure, telemedicine, and assistive technologies for chronic disease management. His work bridges IoT, data privacy, and user-centered design in healthcare robotics. Teaching: Since 2014, he has taught at bachelor's and master's levels, currently leading courses in Communication & Cooperation, Technology Understanding, and seminar projects in eHealth. Scientific Publications: His 15 most recent articles (2020-2024) span AI in dental radiology, blood glucose prediction, digital twins, and ethical considerations in healthcare algorithms. Topics include IoT integration, data privacy, and pandemic analytics.

Karin Leistnerمشاهده پروفایل

پژوهشگر

• Magneto-ionic Materials
• Nanoelectrodeposition
• Magnetic Nanostructures
• +۵ مورد دیگر

Dr. Karin Leistner serves as Group Leader for Nanoelectrodeposition and Magneto-ionic Materials at the Leibniz Institute for Solid State and Materials Research Dresden (IFW Dresden). Her research focuses on the intersection of electrochemistry and magnetism, developing energy-efficient methods for voltage-controlled magnetic nanostructures. Her primary research interests include Magneto-ionic Materials , Nanoelectrodeposition , Magnetic Nanostructures , and Voltage-Controlled Magnetism . Through electrochemical approaches, she pioneers methods to manipulate magnetic properties at the nanoscale without requiring external magnetic fields, enabling applications in low-power spintronics and memory devices. Her work emphasizes redox transformations, electrolytic gating, and interfacial engineering to achieve programmable magnetism in hybrid metal/oxide systems. Analysis of her publication trends reveals consistent innovation in magneto-ionic effects (2021-2025), with increasing focus on microscale patterning (2023-2025) and energy-efficient device applications . Her research spans fundamental electrochemistry (e.g., self-terminated electrodeposition) to applied nanotechnology (e.g., magnetoresistance switching aerogels), demonstrating strong translational potential. Recent work integrates advanced characterization techniques like Kerr microscopy with electrochemical control for precise magnetic manipulation. Dr. Leistner has delivered over 20 invited talks at international institutions including TU Chemnitz, Forschungszentrum Jülich, and Simon Fraser University, highlighting her recognition as a leading expert in electrochemically controlled magnetism. Her collaborative research spans multiple continents, with publications co-authored by teams in Germany, USA, Mexico, Austria, and Slovenia. She leads research activities within IFW Dresden's nanoelectrodeposition laboratory, utilizing specialized electrochemical cells coupled with in situ magnetic characterization. Her group maintains strong collaborations with transmission electron microscopy facilities for real-time observation of electrochemical deposition processes, as demonstrated in joint work with the Wigner Research Centre for Physics.