جست‌وجوی استاد و پژوهشگر

• Urban Planning
• Environmental Health
• Transportation Engineering
• +۵ مورد دیگر

Dr. Esra Suel is an Associate Professor in City Modelling at the University College London's Centre for Advanced Spatial Analysis (CASA) since 2024. She holds a part-time role as Senior Scientist at ETH Zurich's Future Cities Lab. Her expertise spans urban systems, environmental health, and geomatic engineering. She earned a PhD in Urban Systems and Transport Planning from Imperial College London (2016), and holds degrees from Sabanci University and the University of Michigan. Research Interests: Dr. Suel focuses on the intersection of urban environments and health, leveraging street-view imagery and machine learning to assess greenspace impacts on cardiovascular health, mental well-being, and urban inequality. Her work also explores transport equity, air quality modeling, and sustainable city indicators. Recent studies include large-scale slum mapping in Africa and global urban health benchmarking tools. Key Contributions: Her 2025 studies link greenspace exposure to depression risk reduction in US women and reveal cardiovascular health disparities through street-level analysis. Earlier work pioneered air quality estimation via satellite imagery in data-scarce regions. She co-developed KidSat, a poverty-mapping dataset using satellite imagery. Awards: MRC Rutherford Fellowship (2018) Labs: CASA's Urban Analytics Lab, Future Cities Lab (ETH Zurich) Grants: Wellcome Trust-funded Healthy Cities project, MRC funding Her interdisciplinary approach bridges geospatial technologies with public health, contributing to UN Sustainable Development Goals 3 and 11.

He Liمشاهده پروفایل

استادیار

• Multiscale Computational Modeling
• Artificial Intelligence in Medicine
• Biomedical Engineering
• +۳ مورد دیگر

Dr. He Li is an Assistant Professor at the University of Georgia, specializing in computational biomedical engineering. His research focuses on developing AI-driven multiscale models to bridge microscopic and macroscopic biological processes, with applications in disease modeling, medical imaging, and healthcare innovation. He leads the Computational Biomedical Engineering Lab, exploring interdisciplinary solutions in biomedical science using advanced computational methods. His work integrates physics-based modeling (e.g., molecular dynamics, spectral element methods) with AI techniques to assimilate diverse biological data sources. Recent research emphasizes predictive AI models for digital health, including radiology analysis, proton therapy optimization, and Alzheimer’s disease infodemiology. He has published extensively on large language models (LLMs) tailored for medical domains like radiology, oncology, and ophthalmology, addressing challenges in specialized clinical NLP tasks. Key contributions include frameworks like Radiology-GPT and AD-Autogpt, which advance AI applications in diagnostics and disease surveillance. His lab collaborates across disciplines to enhance clinical decision-making through AGI (Artificial General Intelligence) and multimodal data fusion. No awards or grants are explicitly listed in the provided texts. Labs/Teams: Computational Biomedical Engineering Lab @ UGA focuses on AI-driven biomedical innovation, with projects spanning medical imaging, disease modeling, and healthcare technology development.

Timothy McMahanمشاهده پروفایل

استادیار

• Adaptive Virtual Environments
• Neurogaming
• Virtual Reality
• +۷ مورد دیگر

Dr. Timothy McMahan is an Assistant Professor in the Department of Computer Science at the University of North Texas. His research focuses on adaptive virtual environments, neurogaming, and applying VR/AR technologies to neuropsychological assessment and training. He holds a Ph.D. in Computer Science (2016) and multiple advanced degrees from UNT. Education: Ph.D. Computer Science, University of North Texas (2014–2016) M.S. Computer Science, University of North Texas (2007–2013) B.S. Computer Science, University of North Texas (2003–2007) Postdoctorate in Computational Neuropsychology, University of North Texas (2016–2017) Research Interests: Adaptive VR environments for personalized user experiences Neurogaming applications in healthcare and education Machine learning for cognitive assessment and environmental adaptation EEG-based analysis of user engagement and cognitive workload VR tools for neuropsychological disorder diagnosis and treatment Recent work emphasizes VR applications in memory research, adaptive systems leveraging real-time ML predictions, and EEG-driven analyses of subjective experiences. Articles focus on bridging virtual and real-world interactions through computational neuroscience methods. No scientific awards or grants are explicitly listed in the provided materials. No advisees or lab affiliations are documented here.

Lijing Xinمشاهده پروفایل

استادیار

• Magnetic Resonance Spectroscopy (MRS)
• Brain Energy Metabolism
• Neuroimaging
• +۳ مورد دیگر

Lijing Xin is an Assistant Professor at the Department of Physics and a research staff scientist at the Center for Biomedical Imaging (CIBM) at Ecole polytechnique fédérale de Lausanne (EPFL), Switzerland. She teaches courses on Biomedical Imaging and Translational MR Neuroimaging, while also contributing to academic administration. PhD in Physics (2010, EPFL) Master's Project (2002-2005) on MRI instrumentation Her research focuses on high-field magnetic resonance spectroscopy (MRS) and MRI for studying brain function and neurological diseases. She develops novel acquisition and quantification methods for 1 H, 13 C, and 31 P nuclei, particularly on 7T clinical platforms . Her work bridges preclinical and clinical research , with collaborations in psychiatry to explore pathophysiology and biomarkers for disorders like schizophrenia and mood disorders. Recent publications include studies on epilepsy , Alzheimer's disease , brain energy metabolism , and neurochemical profiling using advanced MRS and deep learning for psychosis classification. She has contributed to RF coil design , macromolecule suppression , and metabolic pathway analysis across multiple disciplines. She advises PhD students and collaborates on interdisciplinary projects involving neuroimaging hardware , metabolic disease research , and psychiatric biomarker identification . Her lab at EPFL CIBM-AIT develops cutting-edge techniques for high-resolution brain metabolism analysis and clinical translation .

Natalie Pardeمشاهده پروفایل

دانشیار

• Natural Language Processing
• Interactive Systems
• Multimodality
• +۲ مورد دیگر

Natalie Parde is an Associate Professor and Co-Director of Undergraduate Studies in the Department of Computer Science at the University of Illinois Chicago (UIC). She co-directs the UIC Natural Language Processing Laboratory and holds affiliations with the AI.Health4All Center for Health Equity using Machine Learning and Artificial Intelligence as well as the UIC Honors College. Her research focuses on natural language processing with applications in healthcare, multimodal communication, and creative language analysis. Ph.D., Computer Science and Engineering, University of North Texas M.S., Computer Science, University of North Texas B.S., Computer Science, University of North Texas Parde's research spans three primary strands: NLP interfaces for educational technology, health-oriented summarization, and multimodal communication systems. Recent publications highlight advancements in dementia detection through spoken language analysis, empathy prediction in conversational systems, and multimodal emotion classification. Her work frequently addresses healthcare applications of NLP, including mental health assessment and medical self-disclosure detection. Scientific awards include the Dr. Hermann Zemlicka Award for Most Visionary Paper at Gmunden Retreat on NeuroIS 2024. She has advised numerous doctoral students, including Gyeongeun Lee working on empathy detection via figurative language, Shahla Farzana researching dementia detection methods, and Mohammad Arvan investigating reproducibility in NLP evaluations. The UIC NLP Lab under her direction focuses on developing NLP systems that positively impact society through educational technology interfaces and healthcare applications.

Hans van Dijkمشاهده پروفایل

پژوهشگر

• Biomedical Engineering
• Clinical Neurophysiology
• Electrophysiology
• +۷ مورد دیگر

Hans van Dijk serves as a Senior Researcher in the Signal Processing Systems group within the Department of Electrical Engineering at Eindhoven University of Technology. He simultaneously holds leadership and research positions at multiple institutions, heading the Clinical Physics department at Kempenhaeghe (an institute specializing in Epileptology and Sleep medicine) and working as a Senior Researcher at the University Hospital in Ulm, Germany. His multidisciplinary career bridges engineering, clinical neurophysiology, and sleep medicine. Dr. van Dijk's research focuses on biomedical engineering applications in Clinical Neurophysiology, with particular expertise in electrophysiological techniques including EEG, EMG, and ECG. His work centers on developing and applying signal processing methods to diagnose neurological disorders, with specific emphasis on seizure detection using EEG , High-Density EMG for facial and masticatory muscles , and alternative diagnostic methods for sleep disorders . His recent publications demonstrate a strong focus on applying machine learning techniques to sleep medicine and epilepsy monitoring. Analysis of Dr. van Dijk's recent publications (2021-2025) reveals a consistent research trajectory in developing advanced signal processing methods for clinical neurophysiology. His work spans three main domains: epilepsy monitoring and seizure detection (particularly in children), sleep disorder diagnostics (using multimodal approaches beyond traditional polysomnography), and advanced EMG techniques for motor unit analysis. His research increasingly incorporates deep learning and multi-modal sensor fusion approaches to improve diagnostic accuracy while reducing the invasiveness of monitoring procedures. 2699 citations according to Scopus metrics 101 research outputs documented in institutional repository Active research collaboration with Philips Research and Kempenhaeghe Contributions to UN Sustainable Development Goals through medical diagnostics research Dr. van Dijk supervises research activities with 8 documented supervised works, collaborating extensively with clinical and engineering researchers across multiple institutions. His research program integrates signal processing expertise with clinical needs in neurology and sleep medicine, developing practical solutions for monitoring and diagnosing neurological disorders. His work at Kempenhaeghe focuses on translating engineering innovations into clinical practice for epilepsy and sleep disorder patients.

Daniel Benjaminمشاهده پروفایل

استادیار

• Decision Science
• Behavioral Economics
• Judgment and Decision-Making
• +۱۱ مورد دیگر

Daniel Benjamin is an Assistant Professor of Decision Sciences at Nova Southeastern University's H. Wayne Huizenga College of Business & Entrepreneurship. His research explores how complexity influences judgment and decision-making, particularly in interactions with model outputs and technology, applied to policy-relevant domains such as geopolitical prediction, meta-research in social and biomedical sciences, climate change perception, and expert forecasting. Education: Ph.D. in Psychometrics and Quantitative Psychology, Fordham University M.A. in Psychology, Fordham University B.A. in Mathematics and Psychology, University of North Carolina at Chapel Hill Dr. Benjamin's work focuses on quantifying human biases in forecasting, evaluating reproducibility in preclinical research, and analyzing machine influence on human judgment. His recent publications examine clinical trial prediction accuracy, hybrid geopolitical forecasting models, and knowledge graph applications in scientific evaluation. Scientific Awards: NPR coverage for "Can cancer researchers accurately judge whether preclinical reports will reproduce?" Nature coverage for "Can cancer researchers accurately judge whether preclinical reports will reproduce?" PLoS Biology Top Media Hits of 2018 At NSU, he teaches QNT 5000 Foundations of Business Statistics and QNT 5160 Analytical Modeling for Decision Making . His research has been funded by DARPA, IARPA, NSF, CIHR, Genome Canada, and BioCanRx.

• Infectious Diseases
• Clinical Microbiology
• Antibiotic Resistance
• +۵ مورد دیگر

Jan-Erik Berdal is an Associate Professor at the Faculty of Medicine, University of Oslo, affiliated with the Department of Microbiology and Infectious Diseases. His research focuses on infectious disease epidemiology, antibiotic resistance, and critical care microbiology, with extensive contributions to understanding hospital-acquired infections, sepsis, and SARS-CoV-2 dynamics. He collaborates with Akershus University Hospital and participates in multinational studies like ISARIC-COVID-19. Research interests include: Microbial pathogenesis in critical care settings Antibiotic therapy and resistance patterns Viral infections and immune responses (notably SARS-CoV-2) Development of diagnostic tools for infections Recent publications highlight his work on fecal microbiota transplantation for C. difficile , longitudinal transcriptomics in COVID-19, and biomarker utility in predicting outcomes. Studies span clinical trials, cohort analyses, and computational biology tools. Key collaborations include institutions across Norway and international networks (e.g., ISARIC). His work bridges clinical microbiology, public health, and data-driven diagnostics, emphasizing real-world applications in hospital settings.

Ryan Gallagherمشاهده پروفایل

مدرس ارشد

• Physiotherapy
• Data Science
• Machine Learning
• +۶ مورد دیگر

Ryan Gallagher is a Conjoint Senior Lecturer at the School of Health Sciences, College of Health, Medicine and Wellbeing at the University of Newcastle. He is also a Senior Physiotherapist and Data Scientist with expertise in leveraging healthcare data to improve patient outcomes. His work bridges clinical practice and data science to address healthcare challenges. Dr. Gallagher's educational background includes: Doctor of Philosophy in Physiotherapy from the University of New South Wales Bachelor of Physiotherapy from the University of Newcastle Master in Data Science majoring in Computational Intelligence His research focuses on applying data science and machine learning to healthcare challenges, particularly in the areas of stroke rehabilitation, telehealth, and advanced practice clinical roles. Dr. Gallagher is particularly interested in how E-Health and technology can transform healthcare delivery and the physiotherapy profession. His work on idiopathic Normal Pressure Hydrocephalus (iNPH) has made significant contributions to understanding how gait and balance assessments can predict surgical outcomes following CSF tap tests. He has extensively researched the application of machine learning to analyze clinical trends and outcomes on a large scale. Analysis of Dr. Gallagher's publication record reveals a strong focus on translational research that bridges clinical practice with data science. His work spans multiple domains including stroke rehabilitation, telehealth implementation, musculoskeletal care, and neurological conditions like iNPH. A significant thread throughout his research is the application of data-driven approaches to improve healthcare efficiency, patient outcomes, and access to services, particularly in rural settings. His more recent work shows increasing emphasis on healthcare system redesign and value-based care. Dr. Gallagher has received notable recognition for his research: Best HDR Publication for 2019 from the School of Health Sciences at the University of Newcastle Currently, Dr. Gallagher serves as co-supervisor for a PhD candidate researching "Understanding the Drivers of Telehealth Usage among Patients in Australia and how it Reduces Costs over In-person Healthcare Services." His supervisory work reflects his dual expertise in healthcare delivery and data science. While specific grant information isn't detailed in the provided text, his publication record suggests involvement in multiple research projects related to stroke care, telehealth implementation, and neurological rehabilitation. Dr. Gallagher's work appears to be closely connected with clinical teams at John Hunter Hospital and involves collaboration with multiple healthcare professionals across various specialties. His research on rural healthcare models suggests engagement with regional healthcare networks in New South Wales. His recent work on practice-based research networks indicates leadership in developing collaborative research infrastructure among physiotherapists in regional Australia.

• Neuroscience
• Neurology
• Biomedical Engineering
• +۶ مورد دیگر

Sana Amoozegar is a Research Fellow at the Department of Neurology within the University of Minnesota Medical School . Her work bridges Neuroscience , Biomedical Engineering , and Neurotechnology through deep brain stimulation (DBS) research. Her research focuses on: Therapeutic applications of DBS in Parkinsonian models Neural network modulation in subthalamic pathways Development of closed-loop neuromodulation systems Neuroimaging techniques for seizure localization Recent publications demonstrate expertise in Neuroengineering , including DBS optimization (2025), subthalamic coherence analysis (2024), and LFP-based stimulation systems (2022). She also contributes to Cardiac Risk Prediction (2018) and Neurological Signal Processing (2013).

Naohiro Okadaمشاهده پروفایل

دانشیار

• Neuroimaging
• Psychiatric Disorders
• Adolescent Development
• +۷ مورد دیگر

Naohiro Okada is an Associate Professor at the Graduate School of Medicine, Department of Psychiatry, The University of Tokyo, and concurrently serves as an Associate Professor at the International Research Center for Neurointelligence (IRCN). He is also the Core Manager of the Human fMRI Core facility. Dr. Okada has extensive experience in neuroimaging research, particularly focusing on psychiatric disorders and adolescent brain development. Dr. Okada obtained his M.D. from the University of Tokyo Faculty of Medicine in 2004. After completing his residency, he began psychiatry training at the University of Tokyo Hospital in 2006 and worked as a psychiatrist at Tokyo Metropolitan Matsuzawa Hospital from 2007 to 2012. He earned his Ph.D. from the Graduate School of Medicine, University of Tokyo in 2017, and joined IRCN in 2019 where he manages the human fMRI scanner facility. His research primarily focuses on using functional magnetic resonance imaging (fMRI) to understand brain structure and function in healthy adolescents and patients with psychiatric disorders. Dr. Okada's work aims to elucidate the neurobiological substrates of psychiatric disorders and develop biomarkers for clinical applications. He has published extensively on topics including schizophrenia, autism spectrum disorder, depression, and adolescent brain development, with a particular emphasis on multi-site collaborative studies and data-driven approaches to psychiatric classification. Dr. Okada's recent publications demonstrate a strong focus on developing novel diagnostic classifications based on neuroimaging data, investigating resting-state neural signatures across psychiatric disorders, and establishing robust infrastructure for multi-center MRI research. His work bridges human and animal research while incorporating machine learning and AI techniques to advance the field of psychiatric neuroimaging. Dr. Okada is actively involved in several research projects funded by the Japan Society for the Promotion of Science, including the Advanced Bioimaging Support Platform (2022-2028), MRS analysis of basal ganglia GABA dysfunction in schizophrenia (2021-2025), and studies on dose reduction and brain function changes in schizophrenia patients (2018-2022). He is a member of the Society for Neuroscience and the Japanese Society of Biological Psychiatry, reflecting his commitment to advancing neuroscience research and its applications in understanding and treating psychiatric disorders.

• Sleep Medicine
• Neurodegenerative Diseases
• Epidemiology
• +۶ مورد دیگر

Dr. Yue Leng is an Associate Professor in Psychiatry at the University of California, San Francisco (UCSF) School of Medicine , and leads the Moon (Sleep) Lab for Artificial Intelligence Technology . Her work bridges epidemiology and neurodegenerative disease research through innovative digital biomarker development and machine learning applications. PhD in Epidemiology from University of Cambridge Postdoctoral training in sleep and neurodegeneration at UCSF Research Interests : Investigating sleep-cognition relationships in aging populations Developing behavioral sleep interventions for mild cognitive impairment Exploring circadian rhythm disruption as early dementia markers Creating wearable-based sleep biomarkers through AI/ML Studying ethnic and sex disparities in sleep disorders Examining genetic-sleep interactions in Alzheimer's pathogenesis Key Contributions : Developed LifeSPAN cohort for life-course sleep-neurodegeneration analysis Led STAI SHARP project on statistics/AI in sleep health Pioneered WeSleep wearable technology research Scientific Awards : NIH Pathway to Independence Award (K99/R00) Global Brain Health Institute Atlantic Fellow Cambridge International Scholarship China Medical Board Next Generation Fellowship Research Funding : NIH R01AG083836 (2024-2029): Multidimensional sleep patterns and Alzheimer's NIH R21AG085495 (2023-2025): Digital biomarker development NIH R00AG056598 (2017-2024): Sleep and cognitive decline

• Biomedical Engineering
• Machine Learning
• Medical Imaging
• +۳ مورد دیگر

Burcu Oltu is a Researcher in the Biomedical Engineering Department at Başkent University. She holds both a Master's Degree (2020) and Bachelor's Degree (2017) from the same institution. Her work focuses on applying machine learning techniques to biomedical signal processing and medical imaging. Education: MSc & BSc in Biomedical Engineering, Başkent University Email: boltu@baskent.edu.tr Her research integrates machine learning with biomedical engineering to solve medical diagnostic challenges. Key areas include: Deep learning for pulmonary disease classification from chest X-rays EEG signal analysis for Alzheimer's disease detection Transfer learning approaches for diabetic retinopathy detection Photoplethysmography and blood pressure signal analysis for sleep apnea detection Heart sound signal processing using wavelet transforms Her publications demonstrate a strong focus on developing AI-powered diagnostic tools for various medical conditions, with recent work appearing in journals like BMC MEDICAL IMAGING and Biomedical Signal Processing and Control. She has received recognition for her work, including: Bronze Medal - Istanbul International Invention Fair (ISIF) 2021 Burcu collaborates with researchers like Prof. Dr. Berna Dengiz and Doç. Dr. Selda Güney on projects involving medical imaging analysis and physiological signal processing. Her technical expertise spans algorithm development, neural networks, and biomedical device design.

Aamna AlShehhiمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Big Data Analytics
• +۷ مورد دیگر

Dr. Aamna AlShehhi serves as an Assistant Professor in the Department of Biomedical Engineering and Biotechnology at Khalifa University, where she has pioneered AI-driven healthcare solutions since joining in 2020. Her interdisciplinary expertise bridges electrical engineering, computer science, and clinical medicine through innovative research collaborations with MIT, Imperial College London, and Novartis. Her educational foundation includes: Bachelor's in Software Engineering, United Arab Emirates University (2009) Master's in Computing and Information Science, Masdar Institute (MIT collaboration) (2013) PhD in Interdisciplinary Engineering, Masdar Institute (MIT collaboration) (2017) Dr. AlShehhi's research centers on artificial intelligence for healthcare transformation , with emphasis on neurodegenerative disease prediction, rare disease diagnostics, and wearable-based gait analysis. She develops constrained deep learning models for Alzheimer's biomarker discovery, self-supervised frameworks for emotion recognition using smartphone data, and physics-informed neural networks for biomechanical analysis. Her work integrates multimodal data including fMRI, genomic sequences, and real-world behavioral metrics to address critical gaps in dementia screening, cancer detection, and mental health monitoring. Analysis of her 2023-2025 publications reveals three dominant trends: (1) Explainable AI architectures for clinical decision support, particularly constrained graph neural networks analyzing comorbidity networks in Alzheimer's; (2) Multimodal sensor fusion systems combining wearables, keystroke dynamics, and fMRI for neurological assessment; (3) Self-supervised learning approaches enabling robust performance with limited medical data. Her work consistently targets real-world clinical deployment through portable systems like smart gait analyzers and remote depression screening tools. Dr. AlShehhi actively mentors the next generation of biomedical engineers: Ferial J. Abuhantash (Ph.D. candidate) Khalid Ahmed Abdulla (Master's student) Her research is supported through strategic partnerships with Novartis Institute for Biomedical Research and collaborative grants enabling AI-driven drug repurposing for dementia and genomic cancer detection. She leads projects analyzing skin lesions for melanoma screening and developing deep learning models for early-stage cancer detection using genomic data. As a core member of Khalifa University's Healthcare Engineering Innovation Group , she directs a multidisciplinary team including Postdoctoral Fellow Dr. Sherlyn Jemimah and Research Assistant Chahd Maher Musthafa Chabib. Current initiatives focus on creating deployable AI systems for clinical settings, with ongoing work on portable gait assessment tools for rehabilitation centers and real-time emotion recognition platforms for mental health monitoring.

Menino Osbert Cottaمشاهده پروفایل

پژوهشگر ارشد

• Pharmacokinetics
• Antimicrobial Stewardship
• Critical Care Medicine
• +۳ مورد دیگر

Dr. Menino Osbert Cotta is an Honorary Research Fellow at the University of Queensland Centre for Clinical Research, specializing in pharmacokinetics/pharmacodynamics (PK/PD) of antimicrobials and therapeutic drug monitoring (TDM) in critically ill patients. His work focuses on optimizing beta-lactam antibiotics and anti-infective agents for sepsis, neurocritical care, and resistant infections. Key Research Areas: Antimicrobial stewardship, precision dosing software, ICU pharmacotherapy, cerebrospinal fluid drug penetration, and machine learning applications in TDM. Collaborations: Works with international teams across Australia, UK, and LMICs, particularly on beta-lactam infusion strategies and hospital antimicrobial programs . Recent Trends: 2024-2025 publications emphasize pediatric ICU antibiotic compatibility cost-effectiveness of outpatient parenteral antimicrobial therapy (OPAT) CSF pharmacokinetics of ceftriaxone Methodology: Utilizes population PK/PD modeling, systematic reviews, and clinical trials like BLING III to evaluate continuous vs. intermittent infusions in sepsis. Contact: Email m.o.cotta@uq.edu.au for collaboration or inquiries.