جست‌وجوی استاد و پژوهشگر

Niklas Mattsson-Carlgrenمشاهده پروفایل

دانشیار

• Alzheimer's Disease
• Neurodegenerative Diseases
• Tau Pathology
• +۷ مورد دیگر

Niklas Mattsson-Carlgren serves as an Associate Professor and Senior Lecturer at Lund University's Faculty of Medicine, Department of Clinical Sciences. He holds multiple significant roles including Deputy Research Team Manager and Principal Investigator for several major research projects. His affiliations extend to the Wallenberg Centre for Molecular Medicine (WCMM), LU Profile Area: Proactive Ageing, and MultiPark: Multidisciplinary Research Focused on Parkinson's Disease. Dr. Mattsson-Carlgren's research primarily focuses on Alzheimer's disease and other neurodegenerative conditions, with additional work on acute brain injuries such as those following cardiac arrest. His expertise spans neurochemistry, including the regulation of pain and sleep. He employs biochemical measurements, neuroimaging, and cognitive testing to study disease processes in vivo across the spectrum from preclinical disease to advanced dementia. His work aims to improve diagnostic and prognostic methods in clinical practice, enhance clinical trial design for novel therapies, and deepen understanding of disease mechanisms. His recent publications demonstrate a strong emphasis on plasma biomarkers for Alzheimer's disease detection and monitoring, with particular focus on tau and amyloid pathology. The research shows increasing integration of machine learning approaches with traditional biomarker analysis, reflecting a trend toward more sophisticated diagnostic tools that combine multiple data sources for improved accuracy in predicting disease progression. As Principal Investigator for multiple significant projects including 'Longitudinal plasma biomarkers, cognitive data and amyloid PET to improve early prevention of Alzheimer's disease' and 'Validation of an MTBR tau immunoassay for CSF and plasma,' he leads substantial research efforts funded by organizations including Familjen Rönströms stiftelse, Eli Lilly and Company, and the Knut and Alice Wallenberg Foundation. He also serves as supervisor for PhD students, notably for 'Studies of induced neuronal cells in Alzheimer's disease.' Dr. Mattsson-Carlgren is actively involved in the neuroscience community, serving on the program committee for Neuroscience Day 2025. His research contributes to UN Sustainable Development Goals related to health and wellbeing. His work has gained significant attention, with multiple papers being picked up by news outlets and referenced across social media platforms including X (Twitter) and Bluesky.

Sasan Mahmoodiمشاهده پروفایل

دانشیار

• Medical Image Analysis
• Biometrics
• Computer Vision
• +۱ مورد دیگر

Dr. Sasan Mahmoodi is an Associate Professor at the School of Electronic and Computer Science , University of Southampton. His research focuses on Medical Image Analysis , Biometrics , and Computer Vision , with applications in healthcare and security systems. Research Groups: Vision, Learning and Control; Institute for Life Sciences; Centre for Machine Intelligence His work spans deep learning , rule-based AI , and pattern recognition in medical imaging, including applications for neonatal brain injury prognosis and radiographic knee osteoarthritis classification. He also contributes to biometric technologies like facial profile recognition and gait analysis. Recent publications highlight his expertise in: Domain adaptation for biometric systems Infrared gait recognition databases Motion artefact correction in HRpQCT imaging Histopathology image segmentation using U-Net variants Dr. Mahmoodi supervises PhD students in computer science and human health development and collaborates on interdisciplinary projects involving machine learning and medical imaging.

Heidi Ottevaereمشاهده پروفایل

استاد

• Photonics
• Biophotonics
• Optical fiber sensors
• +۷ مورد دیگر

Heidi Ottevaere is a Professor at the Faculty of Engineering of the Vrije Universiteit Brussel (VUB) since October 1, 2009. She serves as the head of the Instrumentation and Metrology platform at the Photonics Innovation Center and leads the 'biophotonics' research unit of the Brussels Photonics Team (B-PHOT), which is chaired by Prof. Hugo Thienpont. Her work focuses on the design, fabrication, and characterization of photonic components and systems for diverse applications in medical diagnostics, environmental monitoring, and industrial processes. Dr. Ottevaere earned her Electrotechnical Engineering degree with majors in Photonics from Vrije Universiteit Brussel in 1997 and completed her PhD in Applied Sciences at the same institution in 2003. Her doctoral research focused on 'Refractive microlenses and micro-optical structures for multi-parameter sensing: a touch of micro-photonics.' Professor Ottevaere's research spans multiple cutting-edge areas of photonics with particular emphasis on biophotonics, micro-optics, and optical metrology . Her work bridges fundamental science with practical applications, developing novel photonic components and systems that address real-world challenges. She has pioneered research in miniaturized optical systems for medical diagnostics, environmental monitoring, and industrial applications. Her current research focuses on advancing lab-on-a-chip technologies, microfluidic optical sensors, and novel optical fiber systems for biomedical applications. She has developed microminiaturized, integrated plastic detection units for absorbance and laser-induced fluorescence measurements in microfluidic channels, enabling portable, robust, and disposable diagnostic systems. Her recent publications demonstrate a strong trend toward integrated optical sensing systems with applications in medical diagnostics and environmental monitoring. There's a clear progression from fundamental optical component design to complete system integration, with increasing emphasis on artificial intelligence for data analysis and computational imaging techniques. Her work bridges photonics with biomedical engineering, materials science, and data science, reflecting the interdisciplinary nature of modern photonics research. Dr. Ottevaere has been recognized with several prestigious awards: Best Application award (2008) Educational award - Bronze (2019) MOC09 Contribution Award Winners (2009) As an educator and mentor, Professor Ottevaere has promoted 9 PhD students and supervised numerous master's theses. She has secured substantial research funding from diverse sources including the Fund for Scientific Research Flanders (FWO), the Institute for the Promotion of Innovation by Science and Technology in Flanders (IWT), and multiple European Framework Programs. Her current portfolio includes projects on miniaturized biosensors for drinking water screening, precision manufacturing, and photonics education initiatives in Uzbekistan. She has coordinated multiple strategic research and networking projects with regional, national, and international funding bodies. Professor Ottevaere leads the biophotonics research unit within the Brussels Photonics Team (B-PHOT), one of Europe's leading photonics research groups. Her team includes researchers working on optical metrology, micro-optics fabrication, and biophotonic applications. She collaborates extensively with industry partners including Melexis, Umicore, and Anteryon, as well as academic institutions across Europe through various EU-funded projects. She has been instrumental in developing the interuniversity engineering curriculum 'Master in Photonics' which received the EC Erasmus Mundus quality label in 2006, and continues to be the driving force behind photonics education at VUB.

• Bayesian Machine Learning
• Medical Image Analysis
• Graph-based Learning
• +۳ مورد دیگر

Raghavendra Selvan, an Assistant Professor (Tenure Track) at the University of Copenhagen, holds joint appointments in the Machine Learning Section (Department of Computer Science), Kiehn Lab (Department of Neuroscience), and the Data Science Laboratory. His academic journey includes a PhD in Medical Image Analysis (2018), MSc in Communication Engineering (2015), and BSc in Electronics and Communication Engineering (2009). PhD - Medical Image Analysis, University of Copenhagen (2018) MSc - Communication Engineering, Chalmers University (2015) BSc - Electronics and Communication Engineering, BMS Institute of Technology, India (2009) His research focuses on Bayesian Machine Learning with emphasis on Medical Image Analysis, Graph-based Learning, Tensor Networks, Approximate Inference, and Multi-Object Tracking Theory. Recent publications highlight his contributions to environmentally sustainable AI practices, efficient deep learning in medical imaging, and novel applications of tensor networks. Key research areas: Green AI and Environmental Sustainability Medical Image Analysis Graph Neural Networks Crystal Structure Prediction Model Compression Materials Science Applications

Alvaro Köhn-Luqueمشاهده پروفایل

دانشیار

• Mathematical Oncology
• Computational Biology
• Biostatistics
• +۷ مورد دیگر

Alvaro Köhn-Luque is an Associate Professor at the Oslo Center for Biostatistics and Epidemiology, University of Oslo, and Group Leader at the Department of Medical Genetics, Oslo University Hospital. His work bridges mathematical modeling with clinical applications, particularly in cancer research. His academic background includes a PhD in Mathematical and Computational Biology from Complutense University of Madrid (2012), preceded by multiple Master's degrees in Mathematics and Physics from Spanish universities. Dr. Köhn-Luque's research focuses on mathematical oncology , developing computational models to understand cancer dynamics and improve treatment strategies. His work spans multiscale modeling of tumor growth, personalized cancer medicine through computer simulations, and biomarker discovery using machine learning approaches. He has made significant contributions to modeling breast cancer progression and treatment response, particularly in the context of endocrine therapy and CDK4/6 inhibition. His recent publications demonstrate a strong trend toward integrating mechanistic learning approaches that combine mathematical models with machine learning techniques. This hybrid methodology allows for more accurate prediction of treatment outcomes while maintaining biological interpretability. His work frequently involves collaboration with clinical researchers to ensure models are grounded in real patient data and have direct translational potential. Computational modeling of tumor heterogeneity and drug response Development of methods for phenotypic deconvolution in cancer cell populations Integration of multi-omics data for personalized treatment prediction Application of birth-death processes to model tumor evolution Creation of user-friendly computational tools for biomedical researchers Dr. Köhn-Luque has supervised multiple PhD students including Even M Myklebust, Salim Ghannoum, and Xiaoran Lai, and has secured funding for projects including RESCUE, BigInsight, and Integreat. His research demonstrates a consistent trajectory from theoretical mathematical biology toward increasingly clinically relevant applications in personalized cancer medicine.

Qipei Meiمشاهده پروفایل

استادیار

• Civil Engineering
• Artificial Intelligence
• Robotics
• +۴ مورد دیگر

Qipei Mei is an Assistant Professor in the Department of Civil and Environmental Engineering at the University of Alberta's Faculty of Engineering. With an MSc in Computer Science and a PhD in Structural Engineering, he bridges civil engineering with artificial intelligence to enhance infrastructure productivity and sustainability. His research spans AI-driven design automation, robotics for construction safety, and IoT-based condition assessment. PhD, Structural Engineering - University of Alberta (2020) MSc, Computer Science - Georgia Institute of Technology (2018) MSc, Structural Engineering - University of Alberta (2014) B.E., Civil Engineering - Huazhong University of Science and Technology (2011) Mei's work focuses on three key areas: leveraging data-driven methods for design automation, applying sensing/robotics to construction operations, and using digital twins for infrastructure assessment. His team explores generative AI for housing design, robotic construction in remote communities, and smart monitoring systems. Recent publications highlight advancements in: lateral capacity prediction for monopile foundations, transformer-based architectural layout analysis, large language models for building code compliance, vision-language models for safety hazard detection, and sensor networks for bridge monitoring. These works demonstrate interdisciplinary integration of AI, structural engineering, and IoT. Mei actively collaborates with diverse researchers and welcomes graduate students to his Smart Infrastructure Technologies (SITE) Research Group, part of the Infrastructure and Human Tech Lab (IHT-Lab). He teaches advanced topics in structural and civil engineering while pursuing industry-funded projects through NSERC, CFI, and Alberta Innovates.

Laura E. Martinمشاهده پروفایل

استاد

• Cognitive Neuroscience
• Neuroimaging
• Health Behaviors
• +۷ مورد دیگر

Laura E. Martin, PhD, is a Professor in the Department of Population Health at the University of Kansas Medical Center (KUMC) and serves as the Executive Associate Director at Hoglund Biomedical Imaging Center. Her academic journey began with undergraduate training in psychology and dance at the University of Kansas, followed by a Ph.D. in psychology with an emphasis in cognitive neuroscience from Rice University, and a postdoctoral fellowship at KUMC. Dr. Martin is a cognitive neuroscientist whose research focuses on applying neuroimaging methods to understand health behaviors, particularly addictive behaviors such as smoking and eating. Her work examines self-regulation, impulsivity, reward processing, and decision making through functional neuroimaging techniques. As Executive Associate Director at Hoglund Biomedical Imaging Center, she provides technical expertise to investigators and trainees interested in incorporating neuroimaging into their research programs. Her collaborations span diverse areas including aging, depression, obesity, gambling, social neuroscience, decision-making, resting state fMRI, and neuroeconomics. Her research is currently funded by the National Institutes of Health, with a recent focus on using guided episodic future thinking to increase physical activity adherence and promote healthy brain aging among mid-life adults. Dr. Martin is passionate about mentoring students at all levels and sharing her enthusiasm for research methodology, critical evaluation of research, and appropriate method selection for specific research questions. Current research emphasizes health neuroscience examining how health behaviors change the brain and how the brain can be changed to change health behaviors Leads and contributes to team science approaches addressing complex research questions Helps conceptualize research projects and brings specialized data collection and analytic tools to interdisciplinary teams Dr. Martin has published extensively in high-impact journals, with recent work focusing on dementia prevention, neuroimaging methodology, eating disorders, addiction research, and physical activity interventions. Her approach combines cognitive neuroscience with practical applications for improving health behaviors across the lifespan. As an educator and mentor, Dr. Martin works with trainees from high school to faculty level, emphasizing the integration of neuroimaging research with broader research questions and training students in research methodology and critical thinking.

• Optical Imaging
• Biomedical Device and Instrumentation
• Medical Image Analysis
• +۲ مورد دیگر

Jungeun (Jenny) Won is an Assistant Professor of Research in the Department of Biomedical Engineering at the School of Engineering and Applied Sciences, University at Buffalo. Her research focuses on optical imaging , biomedical device development , medical image analysis , and artificial intelligence in OCT . She leads the Translational Biophotonics Laboratory , where she develops advanced OCT techniques for medical applications such as diabetic retinopathy , otitis media , and biofilm analysis . Contact: 215J Bonner Hall, Buffalo NY 14260, jungeunw@buffalo.edu Related Links: CV PDF , Google Scholar , Lab Website Her recent work involves high-resolution OCT for longitudinal studies on retinal degeneration, VISTA OCTA for blood flow analysis, and 3D motion correction algorithms to enhance image quality. She also explores multimodal imaging combining OCT with Raman spectroscopy for bacterial differentiation and microplasma-based therapies for ear infections.

Laura Barnesمشاهده پروفایل

استاد

• Wireless Health
• Human Machine Interface
• Biomedical Data Sciences
• +۳ مورد دیگر

Laura Barnes is a Professor in the Department of Systems and Information Engineering at the University of Virginia, and Associate Director of the Link Lab. She directs the Sensing Systems for Health Lab, focusing on technology-enabled health solutions. Her academic background includes a B.S. in Computer Science from Texas Tech University (2003), and M.S. (2007) and Ph.D. (2008) in Computer Science from the University of South Florida. Research interests span wireless health , human-machine interfaces , biomedical data sciences , and machine learning applications . Current projects include wearable sensor systems for mental health monitoring, clinician-patient communication frameworks (CommSense), and mobile interventions for anxiety reduction. Funding sources include NIH, National Institute of Aerospace, and the U.S. Army. Recent publications emphasize real-time monitoring , digital phenotyping , and context-aware interventions . Key themes include leveraging large language models for health analysis, developing scalable mobile sensing frameworks, and addressing health equity through technology. Labs/Teams : Sensing Systems for Health Lab, Link Lab Grants : NIH-funded projects on medication adherence and sleep disturbance analysis

• Cybersecurity
• Data Encryption
• Access Control Systems
• +۳ مورد دیگر

Willem Jonker is a Full Professor at the Digital Society Institute, specializing in Semantics, Cybersecurity & Services. His research focuses on encryption schemes, access control, and privacy-preserving technologies. He has contributed to over 120 publications, with recent work addressing CVE-to-CWE mapping, anomaly detection in network traffic, and functional encryption systems. His expertise aligns with UN Sustainable Development Goals related to secure digital systems and privacy. Jonker has supervised 10 students and actively participates in academic conferences, presenting on topics like secure data management and cryptographic protocols. Research interests include cryptographic protocols, secure data management, and cybersecurity solutions. Notable projects involve developing methods for detecting covert channels, enhancing data privacy in healthcare, and improving secure search over encrypted data. He has also contributed to standards in digital rights management and forensic image recognition.

Zahraa Abdallahمشاهده پروفایل

مدرس ارشد

• Machine Learning
• Data Science
• Time Series Analysis
• +۶ مورد دیگر

Zahraa Abdallah is a Senior Lecturer at the School of Engineering Mathematics and Technology, University of Bristol. She holds a PhD and BSc in relevant fields. Her research focuses on Machine Learning, Data Science, Time Series Analysis, and their applications in Health Informatics, Neuroscience, and Bioinformatics. She leads projects on wearable technology integration for diabetes management and EEG-based disease classification. Her work emphasizes explainable AI and multimodal approaches. Zahraa is affiliated with the Bristol Doctoral College Initiative (BDFI) as an Academic Co-Director and collaborates with experts like Prof. Raul Santos-Rodriguez. Contact: zahraa.abdallah@bristol.ac.uk | Website: zahraa-abdallah.com Research Interests: Time Series Clustering & Forecasting EEG-based Disease Detection (Parkinson’s, Alzheimer’s) Smartwatch-Driven Healthcare Systems Explainable AI in Biomedical Applications Key Projects: Development of the CSTS benchmark for time series clustering Investigating insulin needs using automated delivery data Gene essentiality classification via graph neural networks Collaborations: Professor Raul Santos-Rodriguez (BDFI) Lucia Marucci (Systems & Engineering Biology)

Rafeef Garbiمشاهده پروفایل

استاد

• Artificial Intelligence
• Medical Image Computing
• Computer Vision
• +۷ مورد دیگر

Dr. Rafeef Garbi is a Professor at the Department of Electrical and Computer Engineering, University of British Columbia, and the Founder/Director of the Biomedical Signal and Image Computing Laboratory (BiSICL). Her multidisciplinary research integrates artificial intelligence, computer vision, and medical imaging for clinical applications in pediatric orthopedics, oncology, and neurology. PhD (Chalmers University, Sweden), MSc (with distinction), Technical Licentiate Research Focus: Specializing in Medical Image Computing and Visual Computing , her lab develops AI-driven solutions for: Automated segmentation and analysis of multi-dimensional biomedical data Clinically-translatable biomarkers for disease assessment Computer-aided intervention systems in surgical contexts Scientific Leadership: UBC Killam Faculty Research Fellow Peter Wall Institute for Advanced Studies Early Career Scholar Senior IEEE Member & Founding IEEE EMBS Vancouver Section Member Key Collaborations: Active in the Medical Image Computing and Computer Assisted Intervention (MICCAI) Society and CAIDA: UBC ICICS Centre for Artificial Intelligence Decision-making and Action. Her team bridges engineering, medicine, and computational biology through translational research.

• Computer Vision
• Natural Language Processing
• Machine Learning
• +۱ مورد دیگر

Dr. Zhenghao Chen is an Assistant Professor at the University of Newcastle. He holds a B.Eng. H1 and Ph.D. from the University of Sydney (2017 and 2022). His research focuses on Computer Vision, NLP, and Machine Learning, with expertise in Generative AI. He has published in top conferences like CVPR and journals such as IEEE T-IP. Awards include the Google Australia Prize and ACM SIGMM Outstanding Thesis Award. He previously worked at TikTok and Disney Research, and serves on program committees for major conferences. Research interests emphasize generative models and industrial applications. His publications span topics like image compression, facial recognition, and medical imaging. Awards highlight academic and industrial recognition. Teaching includes courses on visual signal understanding and video intelligence. Current roles include HDR recruitment and organizing international workshops.

Masatoshi Okutomiمشاهده پروفایل

استاد

• Medical Imaging
• Computer Vision
• Deep Learning
• +۵ مورد دیگر

Professor Masatoshi Okutomi is affiliated with the Department of Systems and Control Engineering at the School of Engineering, Institute of Science Tokyo. His research focuses on advanced medical imaging techniques, particularly in endoscopy and 3D reconstruction, leveraging deep learning and neural networks. Key interests include virtual chromoendoscopy for cancer detection, image restoration, and stereo matching under challenging conditions. His work bridges computer vision and healthcare, addressing real-world applications such as MRI reconstruction and foggy stereo matching. Notable contributions include developing lightweight medical segmentation networks for edge devices and advancing neural radiance fields (NeRF) for novel view synthesis. His research spans diverse domains: from improving video quality assessment to enhancing object detection in high-dynamic-range images. Collaborative projects emphasize practical solutions for medical diagnostics and robust image processing in adverse environments. Recent articles highlight advancements in temporally-consistent video restoration, few-shot view synthesis, and degraded image classification using knowledge distillation. These innovations underscore his commitment to pushing boundaries in both theoretical computer vision and applied medical technology.

• Deep Learning
• Medical Image Analysis
• Biomedical Engineering
• +۲ مورد دیگر

Maria Chiara Fiorentino is a Research Fellow at the Department of Information Engineering, Polytechnic University of Marche, Italy. Her work focuses on applying deep learning techniques to medical image analysis, particularly in ultrasound, MRI, and CT imaging. Education Master’s in Biomedical Engineering, Università Politecnica delle Marche (Honors) Ph.D. in Information Engineering, Università Politecnica delle Marche (Laude) Research Interests: Dr. Fiorentino specializes in deep learning for medical imaging, with applications in diagnosing neurodegenerative diseases like Parkinson’s, cardiovascular conditions, and musculoskeletal disorders. Her recent work includes federated learning for fetal ultrasound analysis, AI-driven vocal fold pose estimation, and domain adaptation in MRI segmentation. Scientific Awards: Paolo Marziali Thesis Prize for her Master’s research Gruppo Nazionale di Bioingegneria award for her Ph.D. thesis Publications: Dr. Fiorentino’s work spans fetal brain image synthesis, zero-shot learning robustness, and machine learning for catheterization management and stenosis detection.