جست‌وجوی استاد و پژوهشگر

Keith Dookeranمشاهده پروفایل

دانشیار مهمان

• Cancer Epidemiology
• Health Disparities
• Advanced Data Analysis
• +۳ مورد دیگر

Keith Dookeran is an Associate Visiting Professor in the Department of Epidemiology at Zilber College, University of Wisconsin-Milwaukee. He holds multiple advanced degrees including a PhD in Public Health Sciences from the University of Illinois at Chicago, an MBA from the University of Chicago Booth School of Business, an MD from the University of Leicester, and an MBBS from the University of the West Indies. With over 20 years of experience in epidemiology, his expertise spans cancer, health disparities, data analysis, health policy, and global health. Research Focus Dr. Dookeran's research investigates health disparities through advanced epidemiological methods, with emphasis on: Cancer epidemiology : Racial/ethnic disparities in breast cancer outcomes and molecular subtypes Perinatal health : Neonatal abstinence syndrome and preterm infant care economics Environmental health : Chronic cadmium/lead exposure impacts on cognition and Alzheimer's Health services : Insurance cost structures and global health implementation challenges Quantitative methods : Mediation analysis of socioeconomic factors in disease outcomes Publication Trends His recent articles (2020-2025) demonstrate a strong focus on disentangling racial, socioeconomic, and geographic health disparities using large-scale datasets (SEER-MHOS, TCGA). Key themes include mediation analyses of environmental toxins on neurodegeneration, econometric evaluations of neonatal care, and molecular epidemiology in breast cancer. Methodologically, he employs regression modeling, cost-benefit analysis, and biomarker validation across diverse populations. Awards No scientific awards are mentioned in available sources. Teaching and Advising At Zilber College, he teaches core epidemiology courses including PH704 (Principles and Methods of Epidemiology), PH804 (Advanced Epidemiology Methods), and PH759 (Regression for Social Determinants of Health). His pedagogical approach emphasizes quantitative literacy and real-world data application. No doctoral advisees are listed in available materials.

Michelle Yongmei Wangمشاهده پروفایل

دانشیار

• Quantitative Psychology
• Statistical Methods
• Machine Learning
• +۴ مورد دیگر

Michelle Yongmei Wang serves as Associate Professor at the University of Illinois at Urbana-Champaign, holding joint appointments in the Department of Psychology (College of Liberal Arts & Sciences), Statistics, and Bioengineering. She maintains active research affiliations with the Beckman Institute and Center for Global Studies. Her research spans quantitative psychology with emphasis on statistical methods, machine learning, and neuroimaging analysis. Primary interests include covariance structure modeling, signal processing, precision medicine applications, and cognitive psychology frameworks. Her work integrates advanced mathematical techniques with psychological and biomedical data to develop robust analytical tools for brain imaging and behavioral research. Dr. Wang's publication history reveals consistent focus on methodological innovation in neuroimaging and statistical modeling, particularly in dynamic functional connectivity, outlier detection, and predictive modeling using fMRI data. Her research demonstrates strong interdisciplinary connections between psychology, engineering, and data science. She teaches advanced courses including Covariance Structure and Factor Models, Methods of Applied Statistics, Image and Neuroimage Analysis, and Signals and Systems, reflecting her technical expertise in quantitative methods.

Christopher Kurzمشاهده پروفایل

پژوهشگر

• Medical Physics
• Radiation Oncology
• MR-guided Radiation Therapy (MRgRT)
• +۱۲ مورد دیگر

Christopher Kurz is a dedicated researcher and group leader in the Department of Radiation Oncology at LMU University Hospital, affiliated with the Faculty of Medicine at LMU Munich. His work bridges medical physics and clinical oncology, focusing on advancing MRI-guided radiation therapy through artificial intelligence. Education: PhD in Medical Physics, 2014, LMU Munich Diploma in Physics, 2011, Heidelberg University Dr. Kurz's research is centered on integrating artificial intelligence into radiation oncology, with a strong emphasis on improving imaging, targeting, and treatment adaptation. His work spans deep learning applications for auto-segmentation, real-time tumor tracking, synthetic CT generation, motion modeling, and dose calculation in MRI-guided and CBCT-based radiotherapy. He has made significant contributions to the development of patient-specific AI models and methods for online adaptive therapy, particularly for abdominal and lung cancers. His research also explores low-field MR-linac applications and the clinical implementation of quantitative imaging. The recent publications highlight a strong trend toward leveraging foundation models, generative AI, and patient-specific deep learning frameworks to enhance precision, reduce imaging doses, and enable real-time adaptive radiotherapy. His work increasingly focuses on creating robust, clinically viable AI tools that can be integrated into routine workflows. Scientific Awards: Mildred Scheel Postdoctoral Scholarship, German Cancer Aid Dr. Kurz leads a research group, implying active mentorship and guidance of junior scientists and students in the fields of medical physics and AI in oncology. His research is supported by competitive funding, including the German Research Foundation (DFG), as evidenced by the DFG-funded project AIM-CBCT-ART, which focuses on uncertainty calibration for deep learning in adaptive radiotherapy. His leadership in creating challenge datasets (SynthRAD2025, TrackRAD2025) also indicates a strong collaborative and community-engaged research profile. Dr. Kurz leads the research group on MR-guided Radiation Therapy within the Department of Radiation Oncology. His team focuses on developing and validating AI-driven solutions for real-time imaging, target localization, and adaptive treatment planning. The group's work involves close collaboration between physicists, clinicians, and data scientists, aiming to translate advanced computational methods into clinical practice for improved cancer care.

• Multiple Sclerosis
• Neuromuscular Disorders
• Neuroimmunology
• +۵ مورد دیگر

Dr. Diego Cadavid is an Instructor in the Department of Neurology at UMass Chan Medical School (T.H. Chan School of Medicine). His research and clinical work focus on neurological disorders, with particular expertise in multiple sclerosis, neuromuscular diseases, and neuroinfectious conditions. Education: MD from Pontifical Xavierian University, Bogota, Colombia Dr. Cadavid's research spans multiple areas of neurology, with a strong emphasis on clinical trials and translational research. His work in multiple sclerosis has focused on novel therapeutic approaches, imaging biomarkers, and clinical outcome measures. He has also made significant contributions to research on facioscapulohumeral muscular dystrophy (FSHD), fragile X syndrome, and Lyme disease neuroborreliosis. His expertise in clinical trial design and execution is evident from his involvement in numerous phase 1-3 trials of novel therapeutics. Analysis of Dr. Cadavid's recent publications reveals a strong focus on neuromuscular disorders, particularly FSHD, where he has been involved in multiple clinical trials of losmapimod. He also maintains active research programs in multiple sclerosis therapeutics (including opicinumab trials) and fragile X syndrome biomarkers. His work often bridges basic science and clinical applications, with an emphasis on developing reliable biomarkers and outcome measures for neurological conditions. Dr. Cadavid has served as an investigator in numerous clinical trials, including the ReDUX4 trial of losmapimod for FSHD, the SYNERGY trial of opicinumab for multiple sclerosis, and various studies of fragile X syndrome. His research has been supported by both industry and institutional funding sources, reflecting the translational nature of his work.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۷ مورد دیگر

Dr. Tryphon Lambrou is a Senior Lecturer in Computing Science at the School of Natural and Computing Sciences, University of Aberdeen, where he joined in January 2022. He previously served as a Senior Lecturer at the School of Computer Science, University of Lincoln, and holds an Honorary Lecturer position at the Department of Medical Physics & Bioengineering, University College London. He has held leadership roles including Interim Vice-Dean for the Joint Institute of Data Sciences and Artificial Intelligence (June 2022–January 2023) and Programme Director at the University of Lincoln. Senior Lecturer, University of Aberdeen (2022–present) Senior Lecturer, University of Lincoln (prior to 2022) Honorary Lecturer, UCL Medical Physics & Bioengineering External Examiner, MSc AI & Data Science, University of Hull Dr. Lambrou's research spans artificial intelligence, machine learning, and computer vision with strong applications in medical imaging and diagnostics. His work focuses on brain tumor segmentation, fetal ultrasound analysis, radiomics, and physiological monitoring using thermal imaging and robotics. He has made significant contributions to multimodal MRI analysis, automated biometry, and deep learning architectures such as MDA-Unet. His recent publications (2022–2024) emphasize deep learning for medical segmentation, out-of-distribution detection in gastrointestinal imaging, and weakly supervised pre-training in neuroimaging. Earlier works (2000–2018) highlight arterial blood flow modeling, PET-CT motion correction, and fetal heart segmentation, demonstrating a long-standing expertise in biomedical signal and image processing. Member, IEEE Professional Member, ACM Full Member, Institute of Physics and Engineering in Medicine (IPEM) Dr. Lambrou actively supervises PhD students in Computing Science and has led research degrees (PGR) at Lincoln. His research has been funded through EPSRC and MRC via the Interdisciplinary Research Consortium. He has contributed to major conferences and journals in medical imaging, AI, and biomedical engineering. He is affiliated with the Centre for Medical Image Computing (CMIC) at UCL and has collaborated extensively on projects bridging computer science and clinical applications, particularly in oncology, neurology, and obstetrics.

• Neuropsychopharmacology
• Psychiatry
• Neuroimaging
• +۷ مورد دیگر

Zubin Bhagwagar is a faculty member at the Yale School of Medicine , affiliated with the Department of Psychiatry . He holds the academic rank of Professor and has contributed extensively to research in neuropsychopharmacology, neuroimaging, and psychiatric disorders. Education : MBBS, MD, FRCPsych, DPhil His research interests span Major Depressive Disorder , Bipolar Disorder , Neurotransmitter Systems , Mitochondrial Genetics , and Alzheimer's Disease . He has published seminal work on serotonin receptors , ketamine effects , and neurochemical imaging in psychiatric conditions. Dr. Bhagwagar’s recent studies focus on lumateperone for treating depression and bipolar disorder with anxious distress. His work also includes clinical trial methodologies , drug safety assessments , and neurochemical biomarkers in mood disorders. He has collaborated on interdisciplinary projects involving mitochondrial gene expression , neuroimaging , and pharmacogenomics , though no specific scientific awards or student advisement details are mentioned in the provided texts.

Fernando Zelayaمشاهده پروفایل

دانشیار

• Neuroimaging
• Cerebral Metabolism
• Magnetic Resonance Imaging (MRI)
• +۵ مورد دیگر

Dr. Fernando Zelaya is an Associate Professor/Reader in Physiological Neuroimaging at King's College London's Department of Neuroimaging (School of Neuroscience). He specializes in developing novel MRI methods to study brain physiology and metabolism, particularly focusing on cerebral blood flow and oxygen metabolism. He is Deputy Lead of the Neuroimaging Theme at the NIHR Maudsley Biomedical Research Centre and holds affiliations with the Institute of Psychiatry, Psychology & Neuroscience and the Clinical Research Facility at King's College Hospital. His research interests span neuroimaging applications in schizophrenia, bipolar disorder, and other psychiatric conditions. Key projects include investigating cerebral metabolic rate for oxygen (CMRO₂) in clinical MRI scanners and understanding neurovascular dysregulation in psychosis. Collaborators include Prof. Federico Turkheimer, Prof. Juan Hernandez Tamames, and Dr. Florian Weisinger. Recent work includes studies on neurochemical signatures of cerebral blood flow alterations in schizophrenia, oxytocin's effects on brain connectivity in psychosis risk, and neuroimaging of bipolar disorder's neurometabolic vulnerabilities. He has been recognized with the Supervisory Excellence Award 2019/2020 for mentorship. Zelaya has led/co-investigated 62+ research projects, including studies on Huntington's disease imaging, hypoglycemia awareness in diabetes, and multi-modal neuroimaging of migraine mechanisms. His work often combines arterial spin labeling (ASL) and quantitative MRI techniques with clinical neuroscience applications.

Iris Burkholderمشاهده پروفایل

استاد

• Biostatistics
• Epidemiology
• Clinical Trials
• +۷ مورد دیگر

Iris Burkholder is a Professor of Statistics in the Health Sciences at Hochschule Fulda , a role she has held since March 2025. Previously, she served as Professor of Health Sciences at the University of Applied Sciences for Technology and Business in Saarland (htw saar) from 2013 to 2025. She is affiliated with the Fachbereich Gesundheitswissenschaften (Department of Health Sciences), specializing in statistical methodology and data analysis in clinical and public health contexts. PhD (Dr. rer. nat.) in Statistics, University of Dortmund (1998–2001) DEA in Biostatistics, University of Montpellier, France (1997–1998) Diplom-Statistik, University of Dortmund (1992–1998) Her research interests center on biostatistics, epidemiology, clinical trials, health data science, and quantitative methods . She emphasizes the application of statistical techniques in real-world healthcare settings, particularly in oncology, treatment evaluation, and data-driven decision-making. Her work bridges methodological rigor with practical implementation in medical research. Her recent publications reveal a strong focus on oncology clinical trials, chemotherapy monitoring, and supportive cancer care . She frequently collaborates on studies involving immunotherapy, personalized dosing, and quality-of-life interventions. Her methodological expertise is evident in randomized trials, meta-analyses, and observational studies across high-impact journals like Annals of Oncology and The Lancet Oncology . Her scientific awards highlight excellence in teaching: Landespreis Hochschullehre (2020) for 'Viel SPaSS mit SPSS' 2nd Prize, Best Teaching Material in Biometry (2016) with Dr. Jessica Brensing 2nd Prize, Best Teaching Material in Biometry (2013) Burkholder has played a significant role in academic and public health advisory through her membership in the Expert Group Off-Label §35c SGB V at the German Federal Ministry of Health (2020–2024, 2025–2028). She is an active member of the International Biometric Society (German Region) and the German Society for Medical Informatics, Biometry and Epidemiology (GMDS) . She also contributes to public understanding through her YouTube channel 'Iris Burkholder erklärt' , producing educational videos on statistical software and methods.

Alexander Nigel William Taylorمشاهده پروفایل

مدرس

• Affective Neuroscience
• Cognitive Psychology
• Aging
• +۷ مورد دیگر

Dr. Alexander Nigel William Taylor is a Lecturer in Psychology at the Department of Psychology, Aberystwyth University. He holds a PhD in Psychology from the University of Reading, an MSc in Neuroscience from the University of Manchester, and a BSc in Human Biology and Psychology from Staffordshire University. His teaching qualifications include PGCTHE from Aberystwyth University, and he is a Fellow of the Higher Education Academy. PhD in Psychology, University of Reading (2015) MSc (Hons) Neuroscience, University of Manchester (2004) BSc (Hons) Human Biology and Psychology, Staffordshire University (2002) His research focuses on the interaction between emotional and physical health in aging, particularly in healthy aging and Alzheimer’s disease. He combines neuroimaging (fNIRS, EEG-fMRI), body-based measures (heart rate, immune markers), and behavioral analysis to explore emotional-cognitive interactions. Recent publications highlight his work on anxiety induction models, prebiotics’ effects on cognitive wellbeing (Innovate UK funded), and nature-based interventions. He collaborates with institutions including the Institute for Stroke and Dementia (Munich) and University of Southampton. Chair, Psychology Department Ethics Committee Member, University Research Ethics Panel His teaching spans behavioral neuroscience, aging psychology, quantitative research methods, and neuropsychology. He has contributed to projects on youth justice services, cognitive development, and sustainable food-behavior change.

Wolfgang Müllerمشاهده پروفایل

دانشیار

• Scientific Databases
• Visualization
• Systems Biology
• +۵ مورد دیگر

Dr. Wolfgang Müller is a Privatdozent (Associate Professor) at the University of Bamberg's Faculty of Business Informatics and Applied Informatics. Since 2008, he has led the Scientific Databases and Visualization (SDBV) group at the Heidelberg Institute for Theoretical Studies (HITS), focusing on data management and visualization in systems biology.

Amrita Basuمشاهده پروفایل

پژوهشگر ارشد

• Computational Biology
• Cancer Informatics
• Breast Cancer
• +۵ مورد دیگر

Amrita Basu is a computational biologist at the University of California San Francisco (UCSF) specializing in cancer informatics and machine learning applications for breast cancer research. Her work focuses on developing computational models for early cancer detection, metastatic progression prediction, and integration of patient-reported outcomes (PROs) into clinical trials. Education: Ph.D. in Computational Biology (Rockefeller University, Tri-Institutional Program); B.S. in Electrical Engineering (Cornell University); Postdoctoral Fellow in Computational Chemical Biology (Broad Institute of Harvard and MIT) Basu leads research projects leveraging high-throughput genomic data and clinical datasets to stratify cancer patients by molecular networks, including transcriptional regulation and epigenetic signaling. She actively develops infrastructure for PRO collection and analysis in breast cancer trials. Her selected publications (2013-2025) demonstrate expertise in computational oncology, drug repositioning, and clinical trial biomarker discovery. Current funding includes NCI grants and Burroughs Wellcome Fund support for projects on patient toxicity frameworks and immune-related adverse event prediction. Scientific Awards: Burroughs Wellcome Fund Innovation in Regulatory Science Award (2021); Interstellar Award (2019); White House Presidential Innovation Fellow (2016); Sage Bionetworks Young Investigator Award (2013) Basu serves as Principal Investigator on multiple NIH/NCI-funded trials (2017-2028) and collaborates with the I-SPY Consortium on neoadjuvant chemotherapy efficacy metrics. Her work bridges computational methods with clinical implementation to improve breast cancer outcomes.

• Developmental Psychology
• Cognitive Neuroscience
• Emotion Regulation
• +۱۲ مورد دیگر

Dr. Joao Guassi Moreira is an Assistant Professor in the Department of Psychology at the University of Wisconsin–Madison, where he leads the Computational Developmental Neuroscience Laboratory (CDNLab). His research lies at the intersection of developmental, social, and cognitive neuroscience, with a focus on emotion regulation, decision-making, and social interactions during adolescence and emerging adulthood. His research interests center on understanding how cognitive and affective skills develop in youth navigating complex environments. He investigates how the brain changes to support improved emotion regulation, why individuals differ in risk-taking behaviors during adolescence, and how mental models of social partners evolve through experience. His lab employs functional MRI, naturalistic text analysis, ecological momentary assessment (EMA), and large-scale datasets (e.g., ABCD study), combined with computational and statistical modeling techniques from computer science, behavioral economics, and quantitative psychology. The recent publications from his lab reflect a strong trajectory in using advanced neuroimaging and data science methods to uncover mechanisms underlying developmental affective processes. Key themes include data-driven classification of emotion regulation strategies, neural predictors of social decisions, impacts of early adversity on reward processing, and dynamic cortical changes during emotional regulation. His work increasingly integrates naturalistic paradigms and methodological innovation, including applications of Large Language Models (LLMs) and NLP to psychological questions. Dr. Guassi Moreira is actively recruiting PhD students in Developmental, Social, Cognition, and Cognitive Neuroscience areas. He values intellectual curiosity, interdisciplinary training, and prior experience in programming or statistics. He encourages prospective applicants to reach out with their CV and research interests. He is committed to building a collaborative, inclusive, and supportive lab environment. His mentoring philosophy emphasizes the development of independent research programs and strong community integration among lab members.

• Neurodegenerative Diseases
• Alzheimer's Disease
• Parkinson's Disease
• +۳ مورد دیگر

Mary Johnson is a researcher at Newcastle University's College of Medical Sciences , specializing in neurodegenerative diseases including Alzheimer's, Parkinson's, and Lewy body dementias. Her work focuses on protein aggregation, neurochemical alterations, and pathological mechanisms in these disorders. Key research areas: Neuropathology , Proteomics , Neurochemistry Recent trends: Alpha-synuclein modulation , Vascular biomarkers , Synaptic dysfunction Her publications since 2013 demonstrate expertise in quantitative neuropathological methods, immune interactions with amyloid-β plaques, and autophagy-related therapies. Collaborations span neuroimaging, neuropharmacology, and protein aggregation studies.

• Multiple Sclerosis
• Neuroimaging
• MRI Techniques
• +۳ مورد دیگر

Roland Henry, PhD is a Professor of Neurology at the University of California, San Francisco (UCSF), affiliated with the UCSF Weill Institute for Neurosciences within the School of Medicine. His research focuses on advanced neuroimaging techniques to study neurological disorders, particularly multiple sclerosis. Dr. Henry's research interests span multiple areas within neurology and medical imaging, with a primary focus on multiple sclerosis. He has made significant contributions to the development and application of MRI techniques for studying brain and spinal cord pathology in MS patients. His work has particularly emphasized quantitative imaging methods to assess disease progression, treatment response, and the relationship between imaging findings and clinical outcomes. Dr. Henry's research has shown particular strength in the areas of spinal cord imaging, brain atrophy measurement, and the development of imaging biomarkers for multiple sclerosis. His work on the central vein sign as a diagnostic tool for MS has been influential in the field, contributing to consensus statements and clinical guidelines. His scientific contributions have been recognized through awards including the UCSF 2012 Rachleff Distinguished Professor award. Dr. Henry has led numerous NIH-funded research projects, including R01 and R21 grants focused on multimodal connectivity for surgical mapping and image-guided determination of functional pathways in neurological oncology. Dr. Henry has been actively involved in mentoring and has likely advised numerous graduate students and postdoctoral fellows throughout his career, though specific names are not provided in the available information. His collaborative approach is evident in the extensive list of co-authored publications across multiple institutions. He has been instrumental in establishing multicenter collaborations, particularly through the North American Imaging in Multiple Sclerosis (NAIMS) Cooperative, where his work has helped standardize imaging protocols and develop consensus statements on important imaging biomarkers in multiple sclerosis.

• Biomedical Engineering
• Medical Image Processing
• Artificial Intelligence in Healthcare
• +۳ مورد دیگر

Zbisław Tabor is a Professor and Deputy Head of the Department of Biocybernetics and Biomedical Engineering within the Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering at AGH University of Science and Technology in Kraków, Poland. His office is located in building C-2, room 411 (4th floor), with contact phone +48 12 617 46 60 and email ztabor@agh.edu.pl. He also serves on the Biomedical Engineering Discipline Council, contributing to academic governance in his field. Professor Tabor's research spans the critical intersection of biomedical engineering and artificial intelligence, with emphasis on medical image analysis and clinical decision support systems . His work focuses on: Advanced medical image segmentation using deep learning architectures (e.g., nnU-Net) Uncertainty quantification in AI-driven diagnostic tools Quantitative imaging for radiation therapy planning and monitoring Computer-aided diagnosis systems for oncology and cardiology Improving reliability of medical image annotation through computational methods Analysis of his recent publications (2022-2025) reveals a strong trajectory toward clinically applicable AI solutions. Key trends include the development of automated body composition analysis for cancer prognosis, SPECT/CT imaging protocols for targeted alpha therapy of brain tumors, and robust deep learning frameworks for wound assessment and cardiomegaly screening. His research consistently addresses multi-center validation and real-world implementation challenges in medical AI. No scientific awards are documented in the available information. There is no mention of student advising, research grants, or laboratory facilities in the provided materials.