جست‌وجوی استاد و پژوهشگر

• Technology-enhanced teaching
• Employability & Professionalism
• Technology-enhanced health education
• +۳ مورد دیگر

Debbi Marais is a Professor (Teaching Focussed) at the University of Warwick within the Warwick Medical School and Health Sciences department. She serves as the Director of Postgraduate Education and is a Principal Fellow of the UK Higher Education Academy , demonstrating strategic leadership in enhancing teaching quality, curriculum development, and student mentorship. With over 25 years of experience in higher education, she has held academic positions at Stellenbosch University , University of Aberdeen , and University of Warwick . Her expertise spans teaching, assessment, program coordination, quality assurance, and reflective practice. Marais’ research interests include pedagogical innovation , with a focus on technology-enhanced teaching and employability & professionalism , as well as public health nutrition covering infant and young child feeding , nutrition transition , and global health . She employs mixed methods in her research and has contributed to understanding exclusive breastfeeding barriers , maternal obesity trends in Africa, and Arabic weight-loss app design . Her work often intersects with technology-enhanced health education and interdisciplinary collaboration . Her recent publications highlight qualitative studies on food-based dietary guidelines in Kenya and South Africa, mobile health interventions for weight loss, and educational innovations in medical and nutrition training. These works reflect her commitment to global health equity and digital pedagogy , with recurring themes in African and Southeast Asian health contexts. She emphasizes participatory research methods and policy translation in maternal and child nutrition. Scientific Awards Principal Fellow of the UK Higher Education Academy Marais has supervised postgraduate research students (PhD and Masters) and contributed to curriculum reform through collaborative international projects. She has secured external funding for research and demonstrated interdisciplinary expertise in nutrition , public health , and health education .

Tommy Löfstedtمشاهده پروفایل

دانشیار

• Machine Learning
• Computer Vision
• Medical Image Analysis
• +۴ مورد دیگر

Tommy Löfstedt is an Associate Professor at Umeå University , affiliated with the Department of Computing Science and the Department of Mathematics and Mathematical Statistics. His research focuses on machine learning , computer vision , and medical image analysis , with applications in life sciences, radiation therapy, and biomedical imaging. He leads multiple research projects, including AI-driven delineation in radiation therapy, quantitative MRI for radiotherapy, and machine learning for plant nutrient uptake. Current research emphasizes structured regularization methods to improve model interpretability and robustness. Key applications include medical image segmentation , Alzheimer's classification , and uncertainty estimation in MRI . Recent publications highlight his work on morphological regularization , adversarial attack mitigation , and multi-task learning in medical imaging contexts. His projects span 2022–2026 with funding for pediatric oncology automation and gynecological cancer staging. Affiliated with both computing and mathematical departments, he bridges algorithm development with applied mathematical frameworks in medical and life science domains.

Xiaobo Liمشاهده پروفایل

استاد

• Neuroscience
• Medical Imaging
• Machine Learning Applications in Neurology
• +۳ مورد دیگر

Xiaobo Li is a Professor in the Department of Bio-Medical Engineering at New Jersey Institute of Technology. Holding a Ph.D. in Computer Aided Geometric Design from the University of Birmingham and a B.S. in Automation from Nanjing University of Aeronautics, their research bridges computational methods with neuroimaging and psychiatric disorder analysis. Ph.D., University of Birmingham (Computer Aided Geometric Design, 2004) B.S., Nanjing University of Aeronautics (Automation, 1999) Dr. Li’s work focuses on applying machine learning and graph theory to understand brain network abnormalities in conditions like ADHD , schizophrenia , and traumatic brain injury . Their studies analyze structural-functional connectivity , reward processing , and gut-brain axis interactions using fMRI , fNIRS , and diffusion tensor imaging . Recent publications highlight their development of tools like the GAT-FD MATLAB toolbox for brain network analysis and their exploration of multimodal MRI in schizophrenia diagnosis. They also investigate the neurobiological effects of photobiomodulation and vision therapy interventions.

Gabriella Casalinoمشاهده پروفایل

استادیار

• Computational Intelligence
• eHealth
• Data Stream Mining
• +۵ مورد دیگر

Gabriella Casalino is an Assistant Professor at the University of Bari Aldo Moro, Department of Computer Science, and a key researcher at CILAB - Computational Intelligence Lab. Her work focuses on Computational Intelligence methods for interpretable data analysis, particularly in eHealth, Data Stream Mining, and eXplainable Artificial Intelligence (XAI) within medical and educational domains. She has contributed to innovative approaches in smartphone-based health monitoring, fuzzy logic applications, and remote vital sign detection via photoplethysmography. Education : Ph.D. in Computer Science, with advanced training at institutions like Universitat de Girona and Université de Mons. Research Trends : Recent publications highlight applications of evolving granular computing, neuro-fuzzy systems, and explainable AI in hypertension prediction, bipolar disorder monitoring, and educational data analysis. Key subfields include remote health monitoring, medical data streams, and hybrid AI models. Grants : Research funded by AIRC (Italian Cancer Research Foundation), focusing on computational methods for healthcare challenges. Labs & Collaborations : Active in CILAB, collaborating on projects involving mHealth solutions, cardiovascular risk assessment, and intelligent educational systems.

• Genetics of Dyslexia
• Learning Disabilities
• Language Impairment
• +۱۲ مورد دیگر

Jeffrey R. Gruen is Professor of Pediatrics (Neonatology) and of Genetics at Yale School of Medicine, and a faculty member in the Investigative Medicine Program at Yale Graduate School of Arts and Sciences. His research is affiliated with multiple centers including the Yale Center for Genomic Health, Wu Tsai Institute, and the Yale Child Health Research Center. Professor of Pediatrics (Neonatology), Yale School of Medicine Professor of Genetics, Yale School of Medicine Member, Investigative Medicine Program, Yale Graduate School Principal Investigator, Gruen Lab Education: MD, Tulane University, 1981 BS in Chemistry, Tulane University, 1977 Residency in Pediatrics, Yale-New Haven Hospital, 1984 Internship in Pediatrics, Yale-New Haven Hospital, 1982 Dr. Gruen's research centers on the genetic and molecular basis of dyslexia and language impairments. His lab pioneered the mapping of the DYX2 locus on chromosome 6 and discovered the DCDC2 gene, a major contributor to reading disability. His team identified READ1, a transcriptional control element that modulates risk for dyslexia, and demonstrated synergistic interactions between genetic variants in DCDC2 and KIAA0319 . His work integrates human genetics, molecular biology, and neuroimaging to understand the biological mechanisms underlying learning disabilities. He leads the Yale Genes, Reading and Dyslexia (GRaD) Study and the New Haven Lexinome Project, aiming to enable early diagnosis and personalized educational interventions. The recent publications reflect a strong trend in integrating genetic data with cognitive, behavioral, and educational assessments. Key themes include genome-wide association studies of dyslexia, gene-environment interactions (particularly involving phonological awareness and home environment), phenotype harmonization across cohorts, and the application of genetic findings to educational policy and practice. Imaging genetics and the study of comorbid conditions like Sluggish Cognitive Tempo are also prominent. Scientific Awards: Innovative Research Award, Kavli Institute, 2022 Dr. Gruen has served as Principal Investigator on numerous NIH-funded studies, including the GRaD Study, the Pediatric Imaging NeuroGenetics (PING) Study at Yale, and the New Haven Lexinome Project. He mentors a broad network of collaborators across institutions such as the University of Colorado, University of Bristol, and Johns Hopkins. His lab trains researchers in human genetics, molecular techniques, and cognitive phenotyping. He leads the Gruen Lab, which focuses on human genetic studies, molecular genetic mechanisms, imaging genetics, and longitudinal intervention studies. The lab collaborates extensively with national and international research centers and utilizes advanced techniques including GWAS, sequencing, chromatin immunoprecipitation, and MRI-based phenotyping.

Caroline Ketchamمشاهده پروفایل

استاد

• Movement Neuroscience
• Neurodiversity
• Concussions
• +۵ مورد دیگر

Caroline Ketcham is a Professor of Exercise Science and the Associate Dean of Elon College, the College of Arts and Sciences at Elon University. Her research focuses on movement neuroscience, neurodiversity, concussions, and mental health, with expertise in neuromusculoskeletal control and rehabilitative strategies across diverse populations including children, elderly, neurological patients, and athletes. Education: PhD in Exercise Science/Motor Control, Arizona State University (2003) MS in Exercise Science/Motor Control, Arizona State University (1999) BA in Biology/Psychology, Colby College (1996) Her work spans motor control, concussion management, and mental wellness, with over 87 undergraduate mentees and 65+ publications. She co-edited Concussion in Athletics and Cultivating Capstones , and co-directs the Elon BrainCARE Research Institute, which advocates for concussion awareness and mental health. Recent research trends include concussion baseline testing as mental health screening, dual-task gait analysis, and global mentoring frameworks. Scientific awards highlight her contributions: Distinguished Scholar (2023), Ward Family Mentoring Award (2017), and multiple teaching distinctions. Scientific Awards: Distinguished Scholar Award (2023), Elon University Ward Family Excellence in Mentoring Award (2017), Elon University Elon College Service Award (2014), Elon University School of Education Excellence in Scholarship Award (2010), Elon University Teacher of the Year (2007), Texas A&M University She actively mentors students in concussion advocacy and neurodiverse movement research, with hundreds advised in health professions. Her labs focus on neuromuscular control and mental wellness initiatives.

• Sports Science
• Exercise Physiology
• Strength Training
• +۴ مورد دیگر

Hilde Stokvold Gundersen serves as an Associate Professor in the Department of Sport, Food and Natural Sciences at Western Norway University of Applied Sciences (HVL). Her academic position is based at the Bergen KRONSTAD campus where her office is located in room L215. She maintains an active research profile with multiple publications in 2024 across various sports science domains. Dr. Gundersen's research interests span multiple critical areas in sports science, with particular focus on adolescent development in athletic contexts, strength training methodologies, and aquatic safety education. Her work bridges theoretical sports science with practical applications in educational settings, especially in primary school physical education programs. She demonstrates expertise in examining physiological development markers like bone age and their relationship to athletic performance metrics. Analysis of her 2024 publications reveals a strong emphasis on practical applications of sports science research. Her work addresses gender-specific training considerations, developmental physiology in youth athletes, and safety education in aquatic environments. The publications appear in both Norwegian-language educational resources and international scientific journals including the Journal of Applied Physiology and Frontiers in Sports and Active Living, indicating both national and international scholarly impact. While specific awards are not mentioned in the available information, her publication record demonstrates active scholarly contribution to the field of sports science. Her collaborative work with researchers across different institutions suggests integration within broader research networks in exercise physiology and sports pedagogy.

Neil Lawrenceمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Uncertainty Quantification
• +۳ مورد دیگر

Neil Lawrence is the inaugural DeepMind Professor of Machine Learning at the University of Cambridge's Department of Computer Science and Technology. He also holds positions as a Senior AI Fellow at The Alan Turing Institute and a Visiting Professor of Machine Learning at the University of Sheffield. After three years as Director of Machine Learning at Amazon, Lawrence recently returned to academia, bringing extensive industry experience to his academic work. Lawrence's research focuses on the intersection of machine learning with the physical world, particularly in uncertainty quantification and end-to-end solutions for real-world applications. His work was initially inspired by deploying machine learning systems in African contexts, where comprehensive solutions are often required. His technical expertise spans over two decades in machine learning methods, with a growing interest in public understanding of machine learning, policy decisions, and data governance implications. His recent publications reveal a diverse research portfolio spanning climate science, healthcare applications, systems engineering for AI deployment, data governance, and theoretical machine learning. Lawrence's work demonstrates a consistent theme of bridging theoretical machine learning with practical applications across multiple domains, with particular attention to uncertainty quantification and the societal implications of AI systems. Lawrence serves on the board of the AISTATS conference and the ELLIS Foundation, and acts as the founding and series editor for the Proceedings of Machine Learning Research. He is also the co-host of the Talking Machines podcast, demonstrating his commitment to public engagement with machine learning concepts. At Cambridge, Lawrence teaches Advanced Data Science (Part II) and Machine Learning and the Physical World (MPhil ACS, Part III), contributing to both undergraduate and postgraduate education in computer science. His work with the Accelerate Programme for Scientific Discovery and the Data Trusts Initiative positions him at the forefront of developing frameworks for responsible and effective AI deployment in scientific and societal contexts.

Franziska Mathis-Ullrichمشاهده پروفایل

استاد

• Surgical Robotics
• Medical Robotics
• Artificial Intelligence in Medicine
• +۳ مورد دیگر

Prof. Dr. Franziska Mathis-Ullrich is a Professor at Friedrich-Alexander-University Erlangen-Nuremberg (FAU) leading the Surgical Planning and Robotic Cognition Lab (SPARC) in the Department of Artificial Intelligence in Biomedical Engineering. Previously, she was an Assistant Professor at Karlsruhe Institute of Technology (KIT) from 2019 to 2023. Her research focuses on minimally invasive robotic systems, soft robotics, and embedded machine learning for surgical applications. She holds a PhD in Microrobotics from ETH Zurich (2017), with earlier degrees from the same institution. Education: B.Sc. and M.Sc. in Mechanical Engineering and Robotics (ETH Zurich, 2009–2012) Ph.D. in Microrobotics (ETH Zurich, 2017) Research Interests: Minimally invasive medical robotics, soft robotic systems, AI-driven surgical assistance, microrobotics, and robot-assisted surgery. Her work emphasizes translating robotics innovations into clinical applications through interdisciplinary collaboration. Key Awards: IEEE ICRA Best Paper Award in Medical Robotics (2014) IEEE BioRob Best Student Paper Award (2016) ICRA Microassembly Challenge First Prize (2014 & 2015) Forbes 30 under 30 (2017) Grants & Projects: Leading a Bavarian State Ministry-funded project on endometriosis diagnostics (€3M). Active in multidisciplinary collaborations with Erlangen University Hospital. Serves as Vice-President of the German Society for Computer- and Robot-assisted Surgery (CURAC). Labs & Teams: Directs the SPARC Lab, which develops cognitive robotic systems for surgical planning and execution. Collaborates with institutions like Max Planck, Fraunhofer, and Helmholtz.

Loretta Hsuehمشاهده پروفایل

استادیار

• Health Disparities
• Behavioral Health
• Public Health
• +۵ مورد دیگر

Loretta Hsueh, PhD, is an Assistant Professor at the University of Illinois at Chicago (UIC), specializing in clinical health psychology and health services research. Her work integrates behavioral, psychosocial, structural, political, and biomedical science to address health disparities, particularly focusing on type 2 diabetes among immigrants and people of color. She currently holds a position in the Department of Psychology and is associated with UIC's clinical research initiatives. Education: BA in Psychology and Communication, University of California, Santa Barbara MA in Psychology, San Diego State University Predoctoral Clinical Internship, UIC School of Medicine PhD in Clinical Psychology, Indiana University–Purdue University Indianapolis (IUPUI) T32 Diabetes Translational Research Postdoctoral Fellowship, Kaiser Permanente Northern California Division of Research Research Focus: Dr. Hsueh's research explores two key areas: (1) identifying root causes of diabetes disparities among marginalized groups and developing prevention strategies, and (2) investigating healthcare experiences and their impact on disease progression. Her work emphasizes telemedicine access, patient-provider communication (especially language concordance), and interventions targeting mental health and chronic disease management in diverse populations. Recent Trends in Publications: Her recent work highlights telemedicine's role in pandemic-era healthcare, disparities in video visit access for limited English proficiency patients, and sociocultural factors influencing mental health outcomes among immigrants. She frequently collaborates with interdisciplinary teams to address systemic barriers in healthcare delivery. Awards & Recognition: No formal awards listed, though her research contributes significantly to policy-relevant evidence for improving healthcare equity. Advising & Grants: Dr. Hsueh is actively reviewing applications for doctoral students starting Fall 2026, focusing on candidates interested in health disparities and clinical research. Her grants likely support projects aligned with her NIH-funded diabetes and telemedicine research, though specific grant details are not provided here. Labs & Teams: She leads a research lab focused on multicultural health equity, collaborating with institutions like Kaiser Permanente and UIC to conduct population-level studies and clinical trials.

Alison M. Elliottمشاهده پروفایل

دانشیار

• Rare Disease Genomics
• Congenital Malformations
• Skeletal and Limb Anomalies
• +۲ مورد دیگر

Dr. Alison M. Elliott is an Associate Professor in the Department of Medical Genetics at the University of British Columbia , and a Principal Investigator at the BC Children's Hospital Research Institute . She specializes in genetic counseling , genomics , and congenital skeletal anomalies , with a focus on improving clinical service delivery through implementation science.

Lucia Ciciollaمشاهده پروفایل

دانشیار

• Developmental Psychopathology
• Parent-Child Interactions
• Maternal Mental Health
• +۶ مورد دیگر

Dr. Lucia Ciciolla is an Associate Professor in the Department of Psychology at Oklahoma State University. She leads the Child Adaptation and Maternal Psychopathology (CAMP) Laboratory, focusing on intergenerational transmission of psychopathology, maternal mental health, and parent-child interactions. Her work integrates clinical, developmental, and neuroscientific approaches to address risk and resilience factors in early child development. Current projects include the HEALthy Brain and Child Development (HBCD) Study (NIH HEAL Initiative), EMBRACE (Epigenetics of Mother-Baby Reactivity), and studies on perinatal mental health interventions like the Perinatal Mental Illness Screening Tool (PMIST) and BLOOM (maternal-fetal bonding). Research themes emphasize maternal adverse childhood experiences (ACEs), stress reactivity, sleep regulation, and mindfulness-based interventions to improve maternal and infant outcomes. Dr. Ciciolla collaborates with institutions such as the OSU Center for Family Resilience, CIRCA (CoBRE), and national consortia. Her lab supports graduate and undergraduate training, with students involved in clinical research, thesis work, and community-based initiatives like Mighty Mouth Camp for children with Selective Mutism.

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

Arturo Vargas Bustamanteمشاهده پروفایل

دانشیار

• Health Policy
• Health Services Research
• Public Health
• +۱۳ مورد دیگر

Arturo Vargas Bustamante is an Associate Professor in the Department of Health Policy and Management at the UCLA Fielding School of Public Health. He specializes in health policy, health services research, and public health, with a focus on addressing disparities in healthcare access and utilization among Latino/Hispanic and immigrant populations in the United States. His work also extends to comparative analyses of health care delivery systems in Latin American countries. PhD in Public Policy (2008), MA in Economics (2006), and MPP (2004) from UC Berkeley Research expertise: healthcare cost estimation, program evaluation, economic valuation, and survey research His research explores the intersection of socioeconomic and demographic factors with health system variables like insurance status and usual source of care. Key areas include telehealth equity, Medicaid policy, cross-border health insurance initiatives (e.g., Salud Migrante), and the impact of legislation such as the Affordable Care Act on immigrant communities. He has published extensively in journals like Health Affairs , Health Services Research , and American Journal of Preventive Medicine . Recent trends in his publications emphasize telehealth disparities during the pandemic, immigrant health equity post-Medicaid expansion, and cross-border healthcare challenges. His work frequently combines health economics with policy analysis, often addressing marginalized populations in the U.S. and Latin America. Professor Vargas Bustamante teaches graduate courses in healthcare organization and financing, vulnerable populations, and health services research. He has consulted for the Inter-American Development Bank and the Mexican Ministry of Health, integrating practical insights into his academic work.

Mark Kramerمشاهده پروفایل

استاد

• Neuroscience
• Computational Neuroscience
• Mathematical Modeling of Neural Activity
• +۵ مورد دیگر

Mark Kramer is a Professor in the Department of Mathematics & Statistics at Boston University. He belongs to the Applied Mathematics research group, focusing on mathematical, statistical, and machine learning approaches to characterize brain activity. His work bridges data-driven neuroscience with computational methods, exploring topics like biophysical models of neurons, field models of neural populations in epilepsy, and theoretical questions about brain rhythms. His research interests include: Biophysical modeling of single-neuron dynamics Neural population activity in pathological states Machine learning for detecting abnormal brain rhythms Analysis of cross-frequency coupling and coherence Kramer has developed educational resources like Case Studies in Neural Data Analysis using both MATLAB and Python. These materials teach practical data analysis techniques for spike trains and field data, emphasizing hands-on implementation over theoretical mathematics. He has received funding from NIH and NSF for computational neuroscience projects. His recent publications focus on epilepsy research, sleep spindle analysis, and neural signal processing. The work spans from developing statistical frameworks to understanding network dynamics in seizure termination and exploring phase consistency in neural data. Notably, his coherence studies revealed non-intuitive coupling patterns between brain regions, demonstrating that low-amplitude rhythms can be more informative than dominant ones.