جست‌وجوی استاد و پژوهشگر

• Cardiovascular Adaptations
• Neuromuscular Adaptations
• Strength and Endurance Training
• +۷ مورد دیگر

Xurxo Dopico Calvo is a Professor at the University of A Coruña's Faculty of Sport and Physical Education Sciences. He leads the Performance and Health Group research team and holds administrative roles such as Program Director for the Degree in Sports Science and Physical Activity. His research focuses on cardiovascular adaptations to strength training, neuromuscular adaptations, judo performance analysis, and physical activity in pediatric populations. Teaching responsibilities include courses like Final Year Dissertation , Performance Analysis in Sports , and Teaching Fight Skills . He has directed over 20 research theses since 2013, including studies on judo contest dynamics and strength training optimization. Dopico actively participates in institutional committees, including the Comisión de Asuntos Económicos e Infraestructuras as President since 2013. Key research interests include: cardiovascular and neuromuscular adaptations to resistance training, judo performance metrics, health-related fitness in school-age children, and high-intensity interval training efficacy. His work bridges exercise physiology with practical applications in sports and chronic disease management.

• Pharmacoepidemiology
• Biostatistics
• Cancer Epidemiology
• +۴ مورد دیگر

Kathleen Bennett is Professor of Biostatistics and Head of the Data Science Centre at RCSI's School of Population Health. With over 25 years of medical research experience, she holds a PhD in Statistics from Southampton University (1994) and a BSc in Mathematics with Statistics from Southampton (1988). Her research spans pharmacoepidemiology, cancer epidemiology, and biostatistical methods. Research focuses: Cancer pharmacoepidemiology and medication safety Cardiovascular disease modeling COVID-19 impacts on healthcare utilization Drug-drug interactions in aging populations Health data analytics for policy development She co-leads the EU COLOSSUS program on metastatic colorectal cancer and previously contributed to BREAST-PREDICT. Current grants include BRAIN-Restore (rehabilitation needs in brain tumors) and StrokeCog-R (post-stroke cognitive rehabilitation). Her work employs advanced statistical modeling of population health data to inform clinical practice and health policy.

Pranesh Kumarمشاهده پروفایل

استاد

• Biomedical
• Climate Change
• Mathematics
• +۸ مورد دیگر

Dr. Pranesh Kumar is a Professor in the Department of Mathematics and Statistics at the University of Northern British Columbia (UNBC), Canada. Prior to UNBC, he held academic positions at institutions including Memorial University of Newfoundland, University of Transkei (South Africa), Bilkent University (Turkey), and the Indian Agricultural Statistics Research Institute. He holds a PhD and MSc in Statistics from the Indian Agricultural Research Institute, New Delhi. His research focuses on applied statistics and methodology, with key areas including copula functions, information measures, statistical modeling, fuzzy logic applications, climate change modeling, and financial data analysis. He has authored over 100 peer-reviewed publications and has secured research grants from agencies like NSERC and UNBC. Dr. Kumar has supervised numerous graduate students and contributed to editorial boards of journals such as the Australian Journal of Mathematical Analysis and Applications. His work bridges theoretical statistics with practical applications in engineering, finance, and environmental science. Education: PhD (Statistics), Indian Agricultural Research Institute, New Delhi MSc (Statistics), Indian Agricultural Research Institute, New Delhi Professional Activities: Editorial Board Member of 6+ international journals Reviewer for 20+ prestigious journals and funding agencies Grants: NSERC grants for prediction modeling and statistical analysis UNBC-funded projects on climate indicators and financial risk His advising efforts include guiding students in MSc and PhD programs, focusing on topics like copula-based resampling and climate change predictions. Collaborative projects include archaeological risk frameworks and healthcare analytics.

Jeffrey Curtisمشاهده پروفایل

استاد

• Biomedical Data Analytics
• Rheumatoid Arthritis
• Psoriatic Arthritis
• +۷ مورد دیگر

Jeffrey Curtis is a Professor in the Department of Computer Science at the University of Alabama at Birmingham (UAB), affiliated with the College of Arts and Sciences. His research focuses on rheumatology and biomedical data analytics, particularly in inflammatory arthritis conditions like rheumatoid arthritis, psoriatic arthritis, and ankylosing spondylitis, as well as osteoporosis and musculoskeletal disease outcomes. He has pioneered work in telemedicine applications for rheumatology care and digital health tools for patient monitoring. His research integrates clinical informatics with epidemiological studies to address gaps in treatment efficacy, comorbidity management, and patient-reported outcomes. Recent projects include evaluating Shingrix vaccine efficacy in inflammatory arthritis populations, long-term diabetes prevention strategies from the DPP trial, and comparative effectiveness of osteoporosis therapies like abaloparatide versus teriparatide. Key themes in his work include: Optimizing biologic drug use in rheumatoid arthritis-associated interstitial lung disease Assessing cardiovascular risk factors in rheumatology patients Developing sensor-based tools for spinal mobility assessment (e.g., Bend Ease) Exploring telemedicine satisfaction and its implications for chronic disease management No specific scientific awards or grants were explicitly listed in the provided text. His work emphasizes real-world evidence generation and clinical guideline development through large cohort studies and registry analyses.

Ramaraju Rudrarajuمشاهده پروفایل

استادیار

• Medical Informatics
• Machine Learning in Healthcare
• Cardiovascular Surgery Outcomes
• +۴ مورد دیگر

Ramaraju Rudraraju is an Assistant Professor in the Department of Computer Science within the College of Arts and Sciences at the University of Alabama at Birmingham (UAB). He holds a dual affiliation with the Department of Cardiovascular/Thoracic Surgery since 2017. His work bridges computational methods with medical and educational challenges, focusing on healthcare informatics, predictive modeling, and educational technology. Research interests include machine learning applications in cardiovascular surgery outcomes, pediatric transplantation equity, intelligent tutoring systems, and assistive technologies for disabled populations. He has contributed to frameworks for collaborative knowledge management in educational settings and developed voice-based programming interfaces for motor-impaired individuals. His publications highlight trends in analyzing clinical data from LVAD implantations, optimizing healthcare systems through patient experience, and leveraging deep learning for disease phenotyping. Notable works include studies on racial disparities in pediatric heart transplants and improving mortality predictions via advanced machine learning techniques. Rudraraju’s academic contributions span healthcare innovation, medical device analytics, and educational technology design, reflecting a multidisciplinary approach to solving complex real-world problems through computational methods.

V. K. Cody Bumgardnerمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Digital Pathology
• +۷ مورد دیگر

V. K. Cody Bumgardner serves as Associate Professor of Pathology and Laboratory Medicine , Assistant Dean for Artificial Intelligence and Data Science , and Division Chief of Pathology Informatics at the University of Kentucky College of Medicine . He also directs the IBI Center for Applied AI , focusing on AI integration in healthcare. Academic Rank: Associate Professor Leadership Roles: Assistant Dean, Division Chief, Director Research Focus: AI/ML in pathology, medical imaging, and clinical data Education: PhD in Computer Science (University of Kentucky, 2017). Research Themes combine artificial intelligence with clinical applications, including: Digital pathology automation Edge computing for healthcare Large language models in structured medical tasks Opioid crisis forecasting systems Medical imaging enhancement techniques Recent Publications demonstrate cross-disciplinary innovation in AI-driven clinical diagnostics , secure healthcare infrastructure , medical imaging enhancement , and addiction epidemiology .

• Software Engineering Education
• Computational Technology in Education
• Machine Learning Applications
• +۵ مورد دیگر

Saheed Popoola, PhD, is an Assistant Professor at the University of Cincinnati's CECH - School of Information Technology. His research spans software engineering education, computational technology in K-12 classrooms, and model-driven engineering. He actively explores strategies to enhance collaborative learning environments through student-driven software projects and investigates challenges in open-source development and cybersecurity incident analysis. His work bridges empirical software engineering practices with educational technology, focusing on topics such as static analysis tool efficacy, user sentiment analysis in software communities, and ensemble learning techniques for network security. He has contributed to tools like Loupe for model analysis and has examined LabVIEW/Simulink model evolution patterns. Recent studies include the impact of computational thinking in K-12 education, early heart disease detection via machine learning, and strategies for managing diverse classroom environments. His research emphasizes practical applications in both academic and industrial software development contexts. Notably absent from the provided data are specific details about grants, awards, or direct student advising. However, his involvement in initiatives like the student software solutions center highlights a focus on experiential learning and community-driven development practices.

Serengul Smithمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Computer Networks
• Educational Technology
• +۳ مورد دیگر

Dr. Serengul Smith is an Associate Professor in the Department of Computer Science at Middlesex University. She oversees academic programs as the Director of Programmes and focuses on advancing educational technology, human-computer interaction, and AI applications in learning environments. Her research explores tangible user interfaces (TUIs), collaborative online learning systems, and sensor network protocols like IoT medium access control. Smith’s work bridges technical innovation with pedagogical practice, emphasizing tools that reduce domain knowledge barriers for learners. She has contributed to frameworks for teaching complex subjects such as artificial intelligence and robotics through tactile interfaces. Her publications span topics from bullying prevention in schools to optimizing surgical training via mobile apps, reflecting a multidisciplinary approach to technology integration. Key Research Areas: AI in Education | TUI Frameworks | IoT Protocols | Collaborative Learning Systems Notable Contributions: Developed TangiBoard toolkit, evaluated AI-enhanced wiki collaboration, and analyzed cardiopulmonary surgery outcomes. Her recent work emphasizes ethical considerations in educational technology and improving accessibility for distance learners. Smith collaborates with interdisciplinary teams on projects ranging from cognitive assessment modalities to immunotherapy research, showcasing her commitment to impactful, cross-domain innovation.

Fares Qeadanمشاهده پروفایل

دانشیار

• Biostatistics
• Public Health
• Epidemiology
• +۳ مورد دیگر

Fares Qeadan is an Associate Professor in the Department of Public Health Sciences at Loyola University Chicago's Parkinson School of Health Sciences and Public Health. He holds a PhD in Statistics from the University of New Mexico, alongside multiple master's degrees and an undergraduate degree. His research focuses on statistical methodology for immunology data, mixed effects modeling, and applying big data to public health challenges like the opioid epidemic, racism, and pandemic responses. He emphasizes bridging mathematical rigor with practical health solutions through student mentorship. Education: PhD in Statistics, University of New Mexico Master's degrees from Michigan State University and University of Nevada, Reno Undergraduate dual major, University of Nevada, Reno Research Interests: Dr. Qeadan develops statistical frameworks to analyze complex health data, particularly in immunology and substance use disorders. His work addresses societal issues like opioid misuse, racial health disparities, and pandemic impacts. Recent studies explore telehealth efficacy during COVID-19, syndemic interactions of chronic pain and mental health, and e-cigarette use patterns among college students. Professional Contributions: His publications span epidemiological analyses, methodological advancements, and healthcare policy evaluations. While no awards are explicitly listed, his work reflects sustained engagement with high-impact public health challenges. He actively involves students in research to prepare them for careers at the intersection of biostatistics and clinical practice. Labs/Teams: No specific lab affiliations are detailed, though collaborations likely occur through Loyola's interdisciplinary health sciences network.

Alexander Aksenovمشاهده پروفایل

استادیار

• Metabolomics
• Mass Spectrometry
• Molecular Diversity
• +۴ مورد دیگر

Alexander Aksenov serves as Assistant Professor in the Department of Chemistry at the University of Connecticut, Storrs Campus, specializing in metabolomics and molecular diversity research. His work centers on advancing mass spectrometry techniques and data analysis methods to explore the molecular 'dark matter' within living systems. His academic foundation includes a Ph.D. from the University of Florida (2005), a Postdoctoral Fellowship at Carnegie Mellon University (2006-2008), and a B.S. from Moscow State University (1999). This training established his expertise in analytical chemistry and biological systems. Aksenov's research investigates metabolic processes through 'global' metabolomics approaches, studying living systems in situ to capture native complexity. He develops advanced mass spectrometry workflows and computational tools to extract chemical knowledge from complex biological samples, with particular focus on gut-bone axis interactions, plant-derived compounds like anthocyanins, and microbial metabolite networks. His recent publications demonstrate strong interdisciplinary integration of chemistry, biology, and data science. Analysis of his 2025 publications reveals dominant research themes: metabolomics methodology development (including AI-driven data analysis), gut microbiome-bone health interactions, plant-pathogen chemical ecology (particularly citrus-Huanglongbing systems), and nutritional impacts on metabolic profiles. His work consistently bridges fundamental molecular discovery with translational applications in human health and agricultural science. Contact: Email aaksenov@uconn.edu , Phone (860) 486-6722, Office CHEM A-421, Department of Chemistry, University of Connecticut, 55 N. Eagleville Rd, Storrs, CT 06269.

Russell Zaretzkiمشاهده پروفایل

پژوهشگر ارشد

• Applied Statistics
• Big Data Analytics
• Computational Statistics
• +۳ مورد دیگر

Russell Zaretzki serves as a Heath Faculty Fellow in the Department of Business Analytics & Statistics at the Haslam College of Business, University of Tennessee, Knoxville. His research bridges industry and academia through predictive analytics and business intelligence applications, with expertise spanning applied statistics, big data, and healthcare analytics. His educational foundation includes: Ph.D. in Statistics from Cornell University (2004) M.S. in Statistics from Cornell University (1999) B.S. in Physics from the University of Michigan (1995) Zaretzki's research integrates computational statistics and data mining across diverse domains. His work demonstrates exceptional versatility in applying statistical methodologies to software engineering ecosystems (e.g., developer behavior analysis), genomic evolution (e.g., codon usage modeling), financial risk assessment, and healthcare outcomes. This interdisciplinary approach emphasizes real-world problem solving through predictive modeling and large-scale data analysis. Recent publications reveal a pronounced trend toward methodological innovation in open-source software analytics and biological data science, with increasing focus on ensemble learning techniques and mutation-selection modeling. His work consistently addresses data quality challenges in version control systems while expanding into healthcare applications like oral mucositis dynamics. Recognition includes the prestigious Heath Faculty Fellowship. While specific advising activities remain undocumented, his publication record indicates active mentorship through collaborative research projects. His work on infrastructure initiatives like the World of Code project suggests leadership in developing research frameworks for software ecosystem analysis. Zaretzki maintains significant involvement with the World of Code research initiative, which provides infrastructure for mining open-source version control data at scale, indicating leadership in computational research infrastructure development.

• Statistical learning in high dimensions
• Statistical learning on networks
• Functional data analysis
• +۱۲ مورد دیگر

Charles Bouveyron is a Full Professor of Statistics at Université Côte d'Azur, Nice, France. He holds a Chair in Artificial Intelligence and serves as Director of the Institut 3IA Côte d'Azur, while also leading the Inria research team MAASAI on Statistical Learning and Artificial Intelligence. Université Côte d'Azur: Full Professor of Statistics (2019-present) Institut 3IA Côte d'Azur: Director (2019-present) Inria: MAASAI Team Leader (2019-present) His research focuses on statistical learning methodologies for high-dimensional data, network analysis, functional data modeling, and deep latent variable approaches. Key applications include medical diagnosis through radiomics, ecological monitoring, astrophysics data analysis, and humanities data science. Recent publications emphasize dynamic co-clustering techniques for count data streams, deep latent position models for network analysis, and discriminative clustering frameworks using generalized mutual information. His work integrates Bayesian sparsity, probabilistic PCA, and robust classification methods. Software development remains a core activity with tools like HDclassif, FunLBM, and Linkage.fr platform for network analysis with textual edges. He has mentored over 20 PhD/postdoc researchers in statistical learning and AI applications.

• Environmental Health
• Public Health Disparities
• Health Equity
• +۵ مورد دیگر

Eun Kyung Lee is an Assistant Professor at the Department of Environmental Studies, State University of New York College of Environmental Science & Forestry (ESF). She holds academic appointments from 2023-present and has postdoctoral experience at Boston University School of Public Health (2022-2023) and Case Western Reserve University School of Medicine (2020-2022). Her research focuses on environmental health disparities, neighborhood inequality, chronic disease prevention, and food systems policy. She teaches courses like Foundations of Environmental Health and Quantitative Methods for Measuring Environmental Inequality. Dr. Lee's work examines how historical and contemporary environmental policies contribute to health inequities. Her research integrates spatial analysis, public health data, and community-level interventions. Notable projects include studies on redlined neighborhoods' health impacts and biorefinery pollution effects. She has published extensively in journals like Social Science & Medicine and Environmental Science and Technology. Her academic background includes a Ph.D. in Environmental Health Sciences from University at Albany, SUNY (2019), and an MPH in Global Health from Tufts University (2014). She actively collaborates on community-based interventions targeting asthma management and food access equity. Her research emphasizes actionable policy solutions to address systemic inequities in environmental and healthcare systems.

Philip Britz-McKibbinمشاهده پروفایل

استاد

• Analytical biochemistry
• Analytical chemistry
• Metabolomics
• +۷ مورد دیگر

Philip Britz-McKibbin is a Professor in the Department of Chemistry and Chemical Biology at McMaster University. He also holds associate memberships in the Department of Pathology and Molecular Medicine and the Centre for Medicinal Cannabis Research at McMaster. His laboratory is an affiliate member of The Metabolomics Innovation Centre (TMIC), Canada's national metabolomics laboratory. Dr. Britz-McKibbin is also recognized as a Cystic Fibrosis Canada Researcher. Dr. Britz-McKibbin's research focuses on bio-analytical chemistry with special emphasis on metabolomics. His laboratory develops innovative capillary electrophoresis-mass spectrometry technologies to identify and quantify clinically relevant biomarkers for early detection and diagnosis of genetic and chronic human diseases. His work spans multiple domains including children's health, nutritional sciences, metabolic diseases, and lipidomics. His recent publications demonstrate a strong trend toward clinical applications of metabolomics, particularly in pediatric health, vitamin D metabolism, inflammatory bowel diseases, and growth assessment in children. His research increasingly incorporates multi-omics approaches and population-level studies to identify biomarkers with clinical utility. Analytical biochemistry Analytical chemistry Metabolomics Clinical sciences Health sciences Human metabolism and disease Lipidomics Metabolic Diseases Dr. Britz-McKibbin has taught various courses in analytical chemistry, chemical biology, and bio-analytical chemistry at McMaster University, including Analytical Tools for Chemical Biology, Instrumental Analysis, and Introduction to Bio-Analytical Chemistry.

• Heart Failure
• Implementation Science
• Data Science
• +۵ مورد دیگر

Harriette Van Spall is an Associate Professor in the Department of Medicine at McMaster University and a practicing cardiologist at Hamilton Health Sciences . She holds scientist appointments at the Population Health Research Institute and Research Institute of St. Joe's, Hamilton , and serves as an Associate Member of the Department of Health Research Methods, Evidence, and Impact . Her clinical focus includes Heart Failure , Echocardiography , and Acute Care Cardiology , with active involvement in training medical students and fellows. Education : Bachelor of Science and Doctor of Medicine (University of Toronto) Master of Public Health (Harvard School of Public Health) Fellow of the Royal College of Physicians and Surgeons of Canada (Internal Medicine, Cardiology, Level III Echocardiography) Research Interests : Heart failure epidemiology and outcomes Implementation science for guideline adherence Diversity and inclusion in clinical trials Digital health technology in cardiology Sex and gender disparities in cardiovascular care Recent Publications span heart failure risk prediction, digital health equity, gender-specific outcomes, and global implementation guidelines. Her work appears in JAMA , Lancet , Circulation , and European Heart Journal . Scientific Awards : CIHR-Institute of Circulatory and Respiratory Health Mid-Career Lectureship Award in Cardiovascular Sciences American Heart Association Nanette Wenger Research Award Grants and Mentorship : She has secured over $6.0 million in research funding from CIHR , Heart and Stroke Foundation of Canada , and other agencies. She mentors postgraduate fellows, graduate students, and medical residents.