جست‌وجوی استاد و پژوهشگر

• Neuroradiology
• Brain Imaging
• Central Nervous System Disorders
• +۳ مورد دیگر

Sandra Abi Fadel, MD serves as an Assistant Professor of Radiology and Biomedical Imaging at Yale School of Medicine and acts as Associate Program Director of the Neuroradiology fellowship at Yale New Haven Hospital. Her clinical work focuses on advanced neuroimaging techniques for diagnosing conditions affecting the brain, spine, spinal cord, neck structures, and salivary glands using multiple modalities including X-ray, CT, and MRI. Dr. Abi Fadel's research interests center on neuroradiology with specific focus areas in brain imaging, central nervous system disorders, head and neck imaging, and neurological diagnostics. She particularly emphasizes correlating patients' neurological symptoms with specific anatomic regions in the brain and abnormalities on brain imaging. Her work demonstrates how advanced imaging techniques can significantly impact the timely and effective management of neurological conditions. Her recent publications reveal a strong trend toward integrating advanced imaging techniques with genetic analysis and addressing representation issues in neurointerventional fields. Her work spans from clinical imaging applications to molecular-level analysis of brain tumors, demonstrating a comprehensive approach to neurological diagnosis and treatment. RSNA fellow research award (2020) Fellow of the year from Yale New Haven Hospital (2020) Dr. Abi Fadel serves as Associate Program Director of the Neuroradiology fellowship, indicating her active role in mentoring and training future neuroradiologists. Her professional service includes participation in the American Society of Spine Radiology (2019-2021) as both an Abstract Grader and Award Committee Member, and the American Society of Neuroradiology (2019-2021) as a Peer Reviewer.

• Stroke Neurology
• Neuroimaging
• Cerebrovascular Diseases
• +۴ مورد دیگر

Robin Lemmens is a Professor at the Faculty of Medicine, KU Leuven, and leads the Laboratory of Neurobiology (VIB-KU Leuven). He serves as head of Academic Consultants Training in Specialist Medicine and Program Director of the POC Medical Specialist in Training. His affiliations include the LBI - KU Leuven Brain Institute and membership in multiple institutional committees including the Metaforum Steering Committee and Faculty Council of Medicine. Dr. Lemmens' research focuses on stroke neurology with particular expertise in neuroimaging, cerebrovascular diseases, and endovascular therapy. His work spans ischemic stroke management, anticoagulation therapy, intracerebral hemorrhage, and advanced imaging techniques for stroke diagnosis and treatment planning. Current research explores AI applications in stroke imaging, optimal timing of anticoagulation after stroke, and endovascular therapy in extended time windows. Analysis of recent publications reveals a strong emphasis on translational stroke research with significant contributions to clinical trial methodology. Key themes include validation of imaging biomarkers, optimization of thrombolytic and anticoagulant therapies, and development of AI tools for stroke lesion segmentation. His work frequently appears in high-impact journals including Stroke , Neurology , and The Lancet . Dr. Lemmens supervises research projects and students, with recent supervision including Oosterbos, C. on peroneal nerve entrapment research. His grant portfolio includes multiple active projects totaling millions in funding, with recent grants focusing on secondary stroke prevention, comprehensive stroke management systems, and AI for stroke outcome prediction. He leads the Laboratory of Neurobiology at VIB-KU Leuven, which collaborates extensively with international stroke research networks. Current research directions include developing patient-centered stroke care pathways, advanced imaging techniques for early ischemic changes, and personalized stroke diagnostics and treatment protocols.

Zhaozheng Yinمشاهده پروفایل

دانشیار

• Biomedical Image Analysis
• Computer Vision
• Machine Learning
• +۲ مورد دیگر

Zhaozheng Yin is an Associate Professor in the Department of Biomedical Informatics and Department of Computer Science at Stony Brook University, affiliated with the College of Engineering and Applied Sciences. His research focuses on biomedical image analysis, computer vision, machine learning, and human-robot collaboration in smart manufacturing contexts. Ph.D. in Computer Science and Engineering from Pennsylvania State University (2009) M.S. in Electrical and Computer Engineering from University of Wisconsin-Madison B.S. in Automation from Tsinghua University Active in advancing microscopy image analysis through novel algorithmic approaches, Dr. Yin's work bridges theoretical computer science with practical applications in healthcare and industrial automation. His research emphasizes: Intelligent human-robot collaboration systems Cyber-physical sensing and augmented reality for manufacturing Medical image segmentation and classification techniques Temporal action localization and counting algorithms His publications demonstrate expertise in domain adaptation, vision-language integration, and graph-based modeling. Grant-funded projects include NSF CAREER support for microscopy analysis and NRI/CPS grants for collaborative robotics research. Best Doctoral Spotlight Award, CVPR 2009 Young Scientist Awards at MICCAI (2010-2015) NSF CAREER Award recipient (2014) Best Paper Awards at CVPR workshop (2015) and IISE (2018)

Or Patashnikمشاهده پروفایل

مدرس ارشد

• Computer Graphics
• Computer Vision
• Machine Learning
• +۵ مورد دیگر

Or Patashnik is a Senior Lecturer at the School of Computer Science , Tel Aviv University . His research lies at the intersection of Computer Graphics , Computer Vision , and Machine Learning , focusing on Generative Models for Image/Video Generation , Semantic Editing , and Personalization with controllable user intent. PhD in Computer Science from Tel Aviv University under Daniel Cohen-Or His work addresses challenges in localizing shape variations in text-to-image diffusion models, developing prompt-mixing techniques and attention-based localization methods. Recent projects include Sharp-It for 3D synthesis, Stable Flow for training-free editing, and LCM-Lookahead for encoder-based personalization. Key publication trends span Diffusion Models , Generative Adversarial Networks (GANs) , Attention Mechanisms , and Text-to-Image Manipulation . Collaborations include researchers like Daniel Cohen-Or, Rinon Gal, and Dani Lischinski across institutions such as Stanford and Carnegie Mellon.

John D. E. Gabrieliمشاهده پروفایل

استاد

• Cognitive Neuroscience
• Dyslexia
• Memory Systems
• +۷ مورد دیگر

John D. E. Gabrieli is a Professor at the Massachusetts Institute of Technology (MIT) in the Department of Brain and Cognitive Sciences, holding the Grover Hermann Professorship in Health Sciences and Technology and Cognitive Neuroscience. He serves as Director of the Athinoula A. Martinos Imaging Center at the McGovern Institute for Brain Research and leads the MIT Integrated Learning Initiative. His research spans cognitive neuroscience, focusing on memory, thought, and emotion, with clinical applications in dyslexia, autism, and psychiatric disorders. Director, Athinoula A. Martinos Imaging Center (2016–present) Grover Hermann Professor, MIT (2005–present) Faculty, Harvard Graduate School of Education (2006–present) Visiting Professor, Rush Medical College (1990–present) Gabrieli’s work combines brain imaging with behavioral studies to explore learning mechanisms in children, early intervention for reading difficulties, and neuroimaging applications in psychiatric diagnosis. Recent studies examine altered brain activity in dyslexia , neurodevelopmental patterns in autism , and predictive modeling for mental health . His 2024 dataset on adolescent anxiety/depression in the Human Connectome Project highlights longitudinal neuroimaging approaches. Scientific accolades include election to the American Academy of Arts & Sciences (2016), multiple teaching awards at MIT and Stanford, and the Alice H. Garside Lifetime Achievement Award (2017). He contributes to editorial boards of journals like Psychological Science and Biological Psychiatry . His lab emphasizes educational neuroscience , investigating how socioeconomic factors, white matter plasticity, and mindfulness interventions impact learning outcomes. Collaborations span Massachusetts General Hospital, McLean Hospital, and the Harvard Graduate School of Education, integrating clinical and educational applications of cognitive neuroscience.

Peter J. Basserمشاهده پروفایل

استاد پژوهشی

• Quantitative Imaging
• Tissue Sciences
• Diffusion MRI
• +۷ مورد دیگر

Peter J. Basser is a leading research scientist at the National Institutes of Health (NIH), specifically within the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), where he heads the Section on Quantitative Imaging and Tissue Sciences (SQITS). His work bridges physics, engineering, and neuroscience to develop non-invasive MRI methods for probing tissue microstructure, particularly in the brain. His educational background is not explicitly mentioned, but his scientific achievements reflect deep training in biophysics and medical imaging. He earned his Ph.D. and has built a career at NIH as a principal inventor of key neuroimaging technologies. Basser's research focuses on quantitative imaging and tissue sciences , especially using diffusion MRI to study brain structure and function. He pioneered Diffusion Tensor MRI (DTI) , Streamline Tractography , and advanced methods like MAP MRI , CHARMED , and AxCaliber , enabling in vivo measurement of axon diameters and microstructural features previously accessible only through histology. His work aims to translate these tools into clinical use for diagnosing developmental disorders, trauma, and neurodegeneration. The 15 most recent articles reflect a consistent focus on developing novel MRI biomarkers, particularly through diffusion and relaxometry methods. They explore water exchange, restriction, glymphatic clearance, latency connectomes, and cortical microstructure, demonstrating a trajectory toward in vivo MRI histology and precision imaging for pediatric and neurological applications. Scientific Awards: National Academy of Engineering (NAE), Inducted 2020 National Academy of Inventors (NAI) Fellow, 2024 Eduard Rhein Technology Award, 2021 ISMRM Gold Medal, 2008 ISMRM Lauterbur Lecturer, 2020 American Society of Neuroradiology Honorary Member, 2019 Victor M. Haughton Award, 2017 ISMRM Fellow, 2010 AIMBE Fellow Best Paper Award, Frontiers in Physics, 2023 Basser leads a dynamic research group that mentors postdoctoral fellows and trainees, many of whom have received prestigious awards. His lab has secured significant grants from the NIH BRAIN Initiative, NICHD, USUHS, and the Bill & Melinda Gates Foundation. The SQITS lab develops open-source software tools like TORTOISE , dmritool , and HI-SPEED , which are widely used in the neuroimaging community. The lab collaborates with institutions such as Uniformed Services University and participates in major initiatives like the Human Placenta Project and the Human Connectome Project. Basser’s vision is to transform clinical MRI scanners into quantitative scientific instruments for precision medicine and large-scale brain mapping. Labs and Teams: Section on Quantitative Imaging and Tissue Sciences (SQITS), NICHD, NIH Neuropathology-Neuroradiology Integration Core (with USUHS) Advanced Translational Neuroimaging Research & Development Core Diffusion – Data Processing Center (DPC)

• Extragalactic Astrophysics
• Cosmology
• Weak Gravitational Lensing
• +۷ مورد دیگر

Tim Schrabback is a Full Professor at the Institute for Astro- and Particle Physics , Faculty of Mathematics, Computer Science and Physics, University of Innsbruck . He leads research in extragalactic astrophysics, focusing on weak gravitational lensing, galaxy clusters, and cosmology through major international collaborations such as the Euclid Mission , eROSITA , DES , SPT , and HSC . His research interests include: Observational cosmology using galaxy clusters Weak and strong gravitational lensing Dark energy and large-scale structure X-ray and Sunyaev-Zel'dovich cluster surveys Machine learning applications in astrophysics Calibration of space-based instruments His recent publications (2023–2025) span high-impact journals including Astronomy & Astrophysics , Physical Review D , and Monthly Notices of the Royal Astronomical Society . The work emphasizes cosmological parameter estimation , cluster mass calibration , systematic error mitigation in weak lensing , and multi-messenger cosmology . A strong trend is the integration of data from optical, infrared, X-ray, and microwave surveys to constrain models of dark energy and modified gravity. Scientific contributions include: Leading roles in Euclid’s weak lensing and cluster science working groups Co-authorship on foundational Euclid mission papers Key contributions to eROSITA all-sky survey analysis Development of shear calibration techniques using deep learning Mass calibration of galaxy clusters via weak lensing He actively participates in advising and collaborative research, working closely with postdocs and early-career scientists such as Sebastian Grandis , Florian Kleinebreil , Henrik Jansen , and Lukas Linke . He has secured access to major datasets and leads analysis efforts in joint cluster cosmology programs. His public engagement includes frequent outreach lectures on astrophysics and telescope observation, particularly through the annual Astronacht events at the University of Innsbruck. He has also contributed to media interviews on cosmological tensions and galaxy cluster physics. He leads or participates in several research labs and teams: Euclid Weak Lensing Science Working Group eROSITA Cluster & Cosmology Working Group Institute for Astro- and Particle Physics Observing Team Alpine Cosmology Collaboration

• Active Matter
• Soft Matter Physics
• Ferrofluids
• +۱۲ مورد دیگر

Jaakko Timonen is an Associate Professor and Vice Head of the Department of Applied Physics at Aalto University, where he leads the Active Matter research group. He is also Deputy Director of the Center of Excellence in Life-Inspired Hybrid Materials (LIBER), and holds significant research funding from the Academy of Finland and the European Research Council (ERC StG). His academic journey includes a doctoral degree from Aalto (2013) and postdoctoral research at Northwestern and Harvard Universities. His research interests lie at the intersection of physics, chemistry, and biology, focusing on soft and active matter systems. Key areas include ferrofluids, electrohydrodynamics, liquid-liquid phase separation, colloidal nanoparticles, magnetic control of non-magnetic matter, and advanced optical microscopy. His recent publications span high-impact journals and reveal a strong trend toward bioinspired materials, responsive systems, and biomedical applications such as 3D bioprinting and cancer diagnostics. His work often involves interdisciplinary collaboration and the development of novel experimental techniques. ERC Starting Grant (2019–2024) Academy of Finland Research Fellow (2019–2023) Distinction prize for doctoral thesis (Aalto, 2013) Distinction prize for master’s thesis (Helsinki UT, 2009) Timonen actively supervises PhD and early-career researchers, with several students contributing to recent publications and projects. He leads multiple active grants, including projects on multiscale electrostatic phenomena in nanoparticles and food applications of Pickering emulsions. He also organizes academic events and participates in public outreach, such as the 'Life inspired materials' seminar. His research group maintains strong ties to both fundamental physics and real-world applications in health, sustainability, and advanced materials. He is involved in several research labs and collaborative networks, particularly through the LIBER Center of Excellence, focusing on hybrid materials with life-inspired functions. His team develops custom imaging systems and explores emergent behaviors in active and biological matter, contributing to fields such as non-equilibrium physics, microfluidics, and synthetic biology.

Martin Bendszusمشاهده پروفایل

استاد

• Magnetic Resonance Imaging
• Neuroradiology
• Neuroimaging
• +۷ مورد دیگر

Professor Martin Bendszus serves as the Medical Director of the Department of Neuroradiology at Heidelberg University Hospital. He has held this position since 2007 and is a leading figure in advanced neuroimaging techniques. His academic career includes medical studies in Bonn, specialized training in Neuroradiology at the University of Würzburg (2003-2007), and a professorship in Neuroimaging at the University of Würzburg. Professor Bendszus's research focuses on innovative imaging methods, particularly in Magnetic Resonance Imaging (MRI). He pioneered Magnet Resonance Neurography for diagnosing peripheral nervous system disorders and made significant contributions to Dental-MRI as a radiation-free diagnostic tool for dental conditions. His expertise also extends to brain and spinal cord diagnostics, including aneurysms, strokes, and arteriovenous malformations. His work bridges clinical practice with advanced imaging research, resulting in numerous high-impact publications across neurology, radiology, and oncology. His publication record shows consistent high-impact contributions, with recent work spanning stroke intervention, neuropathic pain, brain tumor imaging, and advanced MRI techniques. The research trends demonstrate his leadership in translating imaging advances into clinical practice, particularly in time-sensitive neurological conditions where imaging guides critical treatment decisions. Kurt-Decker-Preis Röntgen-Preis Coolidge-Award Lucien-Appel-Award Hermann-Holthusen-Ring der Deutschen Röntgengesellschaft Professor Bendszus leads major multicenter clinical trials and collaborative guideline development efforts, including work with the Response Assessment in Neuro-Oncology (RANO) group and European Association for Neuro-Oncology (EANO). His research has secured significant funding for advancing neuroimaging techniques and their clinical applications. He collaborates extensively with neurologists, neurosurgeons, oncologists, and radiologists to improve diagnostic and therapeutic approaches for neurological conditions. His department serves as a reference center for complex neuroradiological cases and trains the next generation of neuroradiologists. Professor Bendszus maintains active leadership roles in professional societies and regularly contributes to shaping clinical guidelines in neuroimaging and stroke care.

• Alzheimer's Disease
• Huntington's Disease
• Parkinson's Disease
• +۵ مورد دیگر

Frank Longo is a Professor in the Department of Neurology and Neurological Sciences at Stanford University School of Medicine. He actively teaches across the neuroscience curriculum including Medical Scholars Research (NENS 370), Directed Reading courses (NEPR 299/NENS 299), Graduate Research (NENS 399/NEPR 399), and Early Clinical Experience (NENS 280), demonstrating his commitment to training the next generation of neuroscientists and clinicians. His research program centers on neurodegenerative diseases with particular emphasis on Alzheimer's, Huntington's, and Parkinson's diseases. Longo investigates the therapeutic potential of neurotrophin receptor modulation (especially p75NTR and TrkB) using small-molecule ligands like LM11A-31, exploring mechanisms spanning protein aggregation, synaptic dysfunction, neuroinflammation, and metabolic dysregulation. His work bridges molecular neuroscience with translational applications, utilizing advanced techniques including transcriptomics, neuroimaging, and biomarker analysis in cellular and animal models. Analysis of his 2024-2025 publications reveals consistent focus on targeting neurotrophin pathways to reverse disease phenotypes. Key themes include: 1) Development of small-molecule modulators for p75NTR/TrkB to rescue synaptic and cognitive deficits; 2) Biomarker discovery for early detection (e.g., plasma Aβ42/Aβ40 ratios); 3) Mechanistic studies on autophagy, white matter changes, and metabolic dysfunction; and 4) Leadership in collaborative initiatives like the TREAT-AD Center for target validation. His research demonstrates both breadth across neurodegenerative disorders and depth in neurotrophin signaling mechanisms. Through undergraduate and graduate research courses, Longo mentors students in neuroscience research despite no specific advisees being listed. His extensive publication record and involvement in multi-institutional consortia (including EU-US task forces) indicate significant research funding and national leadership in neurodegenerative disease research, though specific grant details aren't provided in the source material.

Reza Zadehمشاهده پروفایل

استاد مدعو

• Machine Learning
• Distributed Computing
• Discrete Mathematics
• +۷ مورد دیگر

Reza Zadeh is a Computational Mathematics professor at Stanford University's School of Engineering and Founder & CEO of Matroid . He previously served as a Technical Advisory Board member for Databricks and leads the Spark Tutorial at Stanford. Research Interests: Specializing in Machine Learning and Distributed Computing , his work bridges theoretical mathematics with practical implementations in big data systems. Key focus areas include Optimization of Apache Spark 3D Convolutional Neural Networks Discrete Mathematics and Graph Theory Medical Imaging Applications Academic Contributions: His publications reveal trends across multiple disciplines: Adapting machine learning for medical diagnostics (2019-2022) Advancing distributed computing frameworks (2014-2016) Developing mathematical foundations for social networks (2009-2013) Creating scalable optimization algorithms (2014-2016) Scientific Awards: Best Paper Award runner-up at KDD 2016 Academic Leadership: He has taught SMACC Consulting and designed courses including CME 323: Distributed Algorithms and Optimization (2015-2024) and CME 305: Discrete Mathematics and Algorithms (2010-2017). His lectures cover graph theory, approximation algorithms, and spectral sparsification. Labs & Teams: Organized Spark Summit workshops and leads Scaled Machine Learning Conference . Collaborates with Stanford's ICME computational consulting services.

James C. Davisمشاهده پروفایل

استادیار

• Software Engineering
• Cybersecurity
• Human-Computer Interaction
• +۵ مورد دیگر

James C. Davis is an Assistant Professor at the Elmore Family School of Electrical and Computer Engineering (Purdue University). His research focuses on socio-technical approaches to software engineering , emphasizing security , correctness , and usability in software-intensive systems. He leads the Duality Lab , which publishes in top venues like ICSE , USENIX Security , and EMSE , with funding from the NSF , Google , Cisco , and Rolls Royce . PhD in Computer Science from Virginia Tech Current research themes: software supply chain security, pre-trained model engineering, and empirical studies of developer practices His recent publications (2025) analyze LLM jailbreaking , software signing (Sigstore), IoT model optimization , and deep learning reproducibility . Supported by grants from NSF OAC Core (multi-modal deep learning infrastructure) and RFE (prompt engineering education). Recognized with the ASEE Best Paper Award (2024) for trust engineering research. Current students include Paschal Amusuo (PhD candidate), Wenxin Jiang (now at Socket), and Dharun Venkatraman (Chateaubriand Fellow). Advising highlights : Guided 7 PhD/MSc theses (2022-2025) Developed CodeQL experience report (709 defects in 258 projects) Industry collaborations: Google , Microsoft Research , Qualcomm Research trends from 2022-2025 show sustained focus on: Software security (supply chains, signing, ReDoS) Model engineering (IoT optimization, naming conventions) Empirical methods (interview studies, defect analysis) Human factors (developer practices, training effectiveness)

Fan-Chi Linمشاهده پروفایل

دانشیار

• Seismic Interferometry
• Seismic Tomography
• Earth Structure Analysis
• +۶ مورد دیگر

Fan-Chi Lin is an Associate Professor in the Department of Geology and Geophysics at the University of Utah. With expertise in seismic methods and earth structure analysis, Dr. Lin leads research in seismic interferometry and tomography to understand Earth's structure from shallow to deep. Dr. Lin earned a Ph.D. in Geophysics from the University of Colorado Boulder in 2009. Since then, they have established themselves as a leading researcher in seismic methods development and application. Dr. Lin's research focuses primarily on seismic interferometry and seismic tomography. Seismic interferometry is a method that extracts useful information from diffusive wavefields (like ambient noise and coda wavefields) that were traditionally considered unusable noise. Their work has demonstrated that signals extracted through seismic interferometry provide important new constraints on Earth structure across various scales. This research has applications in studying 3D sedimentary basin structure, regional/continental crust and upper mantle structure, volcano magma bodies, and deeper mantle and core structure. As a member of the University of Utah, Dr. Lin also applies these techniques to model the 3D structure of the Salt Lake Valley and the geometry of the Wasatch fault system to better understand seismic hazards in the area. Analysis of Dr. Lin's recent publications (2021-2025) reveals a strong focus on applying dense seismic arrays and advanced processing techniques to study geological structures. Their work spans multiple geographical areas including Yellowstone National Park, the Wasatch fault system, Taiwan, Hispaniola Island, and the Wyoming Craton. The research demonstrates expertise in Rayleigh wave analysis, ambient noise tomography, and joint inversion techniques. A notable trend is the increasing use of dense linear arrays and double beamforming techniques to achieve higher resolution imaging of subsurface structures. Dr. Lin maintains an active research program with numerous collaborations across institutions. Their work has been featured in high-impact journals including Nature, Science, and Geophysical Research Letters, with several papers receiving media attention from outlets like BBC Science Focus, Discover Magazine, and Phys.org. Dr. Lin leads the "noise.earth.utah.edu" research group, which focuses on developing and applying seismic noise-based methods for Earth structure imaging. The lab utilizes both permanent and temporary seismic arrays to study various geological settings, with particular emphasis on geothermal systems, fault zones, and volcanic regions.

Kathleen Curranمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۳ مورد دیگر

Prof. Kathleen Curran is a Professor at University College Dublin (UCD) and director of the UCD machine learning in medical imaging and diagnostics innovative research lab ( https://www.ucd-ml-mi.com/ ). She serves as an Affiliated Principal Investigator in the Centre for Biomedical Engineering, an INSIGHT funded investigator, and a funded investigator in the Science Foundation Ireland centre for research training in machine learning (ML-Labs). Her research integrates artificial intelligence, computer vision, and clinical medicine to develop interpretable AI solutions for medical diagnostics. Key focus areas include fetal ultrasound imaging, cardiac MRI reconstruction, neuroimaging for Alzheimer's disease and multiple sclerosis, and biomarker discovery for conditions like lymphangioleiomyomatosis and placenta accreta spectrum. She pioneers techniques in diffusion models, explainable AI, and multi-modal learning to address challenges in low-data medical scenarios. Analysis of her recent publications reveals dominant trends in applying generative models for medical data augmentation, developing uncertainty-aware diagnostic systems, and creating interpretable clinical AI tools. Her work consistently targets high-impact clinical applications including fetal development monitoring, cardiovascular disease management, and neurological disorder detection, with strong emphasis on real-world clinical implementation. Scientific recognition includes: 2019 InterTrade Ireland FUSION Project Exemplar Award (with Axial Medical Printing Ltd.) Three Enterprise Ireland Commercialisation Fund awards as Principal Investigator Horizon Europe consortium funding for SMASH-HCM project (Stratification, Management, and Guidance of Hypertrophic Cardiomyopathy Patients using Hybrid Digital Twin Solutions) Prof. Curran leads significant research funding initiatives including Horizon Europe and multiple Enterprise Ireland awards. Her group actively collaborates with industry partners like Axial Medical Printing Ltd. and participates in national research centers such as INSIGHT and ML-Labs, driving translational AI research from bench to bedside. The UCD machine learning in medical imaging and diagnostics lab ( https://www.ucd-ml-mi.com/ ) serves as her primary research hub, fostering interdisciplinary collaborations between computer scientists, clinicians, and biomedical engineers to advance clinical AI solutions.

• Transfer Learning
• Recommender Systems
• Medical Imaging
• +۳ مورد دیگر

Honghui Du serves as a Research Fellow at the Insight Centre for Data Analytics, a leading Irish research institution specializing in data science with nodes across multiple universities. The role centers within the Decision Making research group, focusing on algorithmic solutions for dynamic environments. Research spans transfer learning in non-stationary data streams , recommender systems (notably news personalization using LLMs and diffusion models), and medical imaging under label scarcity. Key emphases include handling concept drift, optimizing active learning for medical diagnostics, and developing generative approaches for user-item interaction modeling. Emerging work explores gamification for sustainable behavior change and entity resolution via language models. Recent publications (2023-2025) reveal accelerating integration of diffusion models and transformers into recommendation frameworks, while maintaining core expertise in transfer learning for evolving data streams. Medical imaging research increasingly addresses practical constraints like limited annotations through adaptive curriculum strategies. The work operates within the Decision Making research group at the Insight Centre, which investigates algorithmic decision processes under uncertainty and dynamic conditions.