جست‌وجوی استاد و پژوهشگر

• Bayesian statistics
• Hierarchical modeling
• Image analysis for super-resolution microscopy
• +۴ مورد دیگر

Dr. Miguel Juarez is a Lecturer in Statistics at the University of Sheffield's School of Mathematical and Physical Sciences. He holds a PhD in Mathematical Sciences from Universidad de Valencia (2004), an MSc in Economics from CIDE (Mexico), and a BSc in Actuarial Sciences from ITAM (Mexico). His research focuses on Bayesian hierarchical modeling for panel/longitudinal data with applications in biology, medicine, and econometrics. Key projects include the STriTuVaD initiative for integrating computer simulations with clinical trials and developing models for super-resolution microscopy image analysis. He has contributed to advancing in silico trial methodologies through the UISS-TB simulator and works on objective Bayesian methods for non-Gaussian data. Professional activities include teaching MAS2010 Statistical Inference and Modelling. His recent work emphasizes accelerating tuberculosis vaccine development via augmented clinical trials and establishing credibility frameworks for in silico trials. He collaborates with interdisciplinary teams in systems biology and biomedical informatics. Research outputs span Bayesian statistical theory, computational epidemiology, and medical technology innovation. Notable collaborations include the Warwick Systems Biology Centre and EU-funded H2020 projects.

Scott A Guelcherمشاهده پروفایل

استاد

• Bone Tissue Engineering
• Polymeric Biomaterials
• Orthopedic Biomaterials
• +۳ مورد دیگر

Scott A. Guelcher is a Professor of Chemical and Biomolecular Engineering, Biomedical Engineering, and Pathology, Microbiology and Immunology at Vanderbilt University's School of Engineering. His research focuses on developing polymeric biomaterials for bone tissue engineering, including injectable scaffolds, biodegradable composites, and drug delivery systems. His work addresses clinical needs in orthopedics, such as bone graft alternatives and fracture fixation devices. Education: PhD in Chemical Engineering from Carnegie Mellon University (Carnegie Mellon), M.S. from University of Pittsburgh, and B.S. from Virginia Tech. Research interests include designing biomaterials with tunable mechanical/biological properties, ex vivo bone tissue engineering in perfusion bioreactors, and ROS-degradable materials for wound healing. Collaborations include Virginia Tech and allograft bone companies. Industrial experience includes Eastman Chemical Company and Bayer Corporation's Polyurethanes Division. Key contributions include injectable polyurethane foams for bone repair, biodegradable fracture fixation devices, and 3D-printed bone models for tumor-bone disease studies. His work integrates materials science, biomedical engineering, and clinical applications to advance regenerative medicine.

• Operational Research
• Simulation Methods
• Healthcare Management
• +۳ مورد دیگر

Dr. Katy Hoad is an Associate Professor of Operational Research at the University of Warwick's Warwick Business School (WBS), affiliated with the ISM-Analytics (ISMA) Group. Her work spans operational research methodologies, simulation techniques, and their application in healthcare and education. She currently teaches modules including Business Analytics, Forecasting, Advanced Mathematics, and Maths for Management across various undergraduate and postgraduate programs at WBS. Dr. Hoad's research focuses on simulation-based education strategies, discrete event simulation optimization, and healthcare management modeling. Notable contributions include studies on tuberculosis epidemiology and the integration of system dynamics with discrete event simulation in pedagogical contexts. Her work has been published in journals such as the Journal of the Operational Research Society and Health Care Management Science, alongside conference proceedings on simulation practices. Her technical expertise includes automating simulation output analysis and adapting simulation software for practical applications. Collaborations with colleagues like Currie, Robinson, and O’Brien highlight her involvement in cross-disciplinary research.

• Cloud Computing
• Edge Computing
• Fog Computing
• +۷ مورد دیگر

Roles and Affiliations: Sukhpal Singh Gill is a Lecturer (Assistant Professor) in Cloud Computing at Queen Mary University of London (QMUL), UK. He leads the GillNet Research Lab and is the Editor-in-Chief of the International Journal of Applied Evolutionary Computation (IJAEC) . He also serves as an Associate Editor for journals like IEEE IoT and Nature Scientific Reports. As Programme Director for MSc Advanced Computer Science and MSc Business Analytics, he contributes to curriculum development and education excellence. Research Interests: His research focuses on Cloud Computing, Edge AI, Internet of Things (IoT), and Energy Efficiency. He explores AI-driven solutions for resource management, security, and sustainable computing. Key areas include fog-edge integration, serverless computing frameworks, and healthcare applications. Publications and Impact: With over 200 peer-reviewed publications (including IEEE TCC, Elsevier JSS, and ACM TOIT), Dr. Gill has achieved 12,500+ citations and an H-index of 54 (Google Scholar). His work has been featured in IEEE Spectrum and Tech Monitor. Notable contributions include frameworks like HealthEdgeAI (healthcare systems), CloudAISim (cloud simulation), and EdgeAISim (edge computing modeling). Awards and Recognition: Recognized with the 2024 IEEE Outstanding Reviewer Award, Elsevier Editor’s Choice Award, and Queen Mary Education Excellence Award. He is a Fellow of the Higher Education Academy (FHEA). Teaching and Leadership: Teaches modules like Cloud Computing (Postgraduate) and Semi-structured Data Modeling. Leads the Networks and Systems Teaching Group (N&STG) and chairs academic misconduct panels. Advocates for inclusive curriculum design and intercultural development in higher education. Labs and Collaborations: The GillNet Lab develops next-generation systems for EdgeAI, CloudAIBus, and CloudAISim. Collaborates with institutions like Lancaster University, The University of Melbourne, and industry partners on fog-cloud IoT ecosystems.

Emma Towlsonمشاهده پروفایل

استادیار

• Network Neuroscience
• Brain Networks
• Mental Health
• +۳ مورد دیگر

Dr. Emma Towlson is an Assistant Professor in the Department of Computer Science at the University of Calgary's Faculty of Science, serving as Assistant Head of Undergraduate Curriculum. She holds a Full Member position at the Hotchkiss Brain Institute and is a Child Health & Wellness Researcher at the Alberta Children's Hospital Research Institute. Her research focuses on network neuroscience, exploring how brain networks' structure-function relationships underpin health and disease. Key areas include network control theory, mental illness vulnerability, and brain connectivity analysis across species. Dr. Towlson's work bridges computer science, physics, neuroscience, and biology. She investigates structural brain networks' topological features (e.g., rich clubs, modules) and their spatial constraints, applying these insights to disorders like schizophrenia, depression, and Alzheimer's. Her lab uses network control principles to identify therapeutic targets for brain stimulation therapies and personalized medicine approaches. Recent grants include a $250,000 New Frontiers in Research Fund (NFRF) Explorations grant (2022) to study preterm birth's impact on brain networks and mental illness risk. She teaches courses on social network analysis (CPSC 572/672) and data visualization (DATA 601). Collaborative efforts include partnerships with Harvard Medical School and Monash University's Turner Institute. Her team’s interdisciplinary projects include analyzing mouse brain connectomes, simulating connectome perturbations, and developing open-source network neuroscience tools. Ongoing work emphasizes translating network science insights into clinical applications for mental health interventions.

Yaoping Huمشاهده پروفایل

استاد

• Brain-Machine Interfaces
• Multi-sensory User Interaction in XR
• Immersive Analytics
• +۵ مورد دیگر

Yaoping Hu is a Professor in the Department of Electrical and Software Engineering at the Schulich School of Engineering, University of Calgary, and an Associate Member of the Hotchkiss Brain Institute. He holds a Ph.D. in Robotics and Neuroscience from the University of Western Ontario. His research focuses on Human-Computer Interaction within Virtual Environments, particularly in Brain-Machine Interfaces, multisensory user interaction in XR, and immersive analytics. He teaches courses such as ENSF 619.36 (Brain-Machine Interface Systems), ENSF 519.51 (Brain-Machine Interfaces), ENEL 602 (Virtual Environments), and ENSF 545 (Introduction to VR). Research interests include modeling neural mechanisms for visuomotor processes, cognitive-ergonomic interaction design, and applications in surgical planning, data visualization, and human-machine collaboration. He leads the Visualization and Interaction Laboratory, collaborating with institutions like the neuroArm Project, Fluid Dynamics Group, and international partners in France. Past and current students include Stanley Tarng, Lida Ghaemi Dizaji, and Aida Erfanian. His work spans projects like neuroSim (a surgical simulator) and BioVista (biofilm visualization tools). Collaborations with industry partners such as Nova Chemicals and Calgary Scientific Inc. support applied research in complex data analysis and virtual reality systems.

• Computational Neuroscience
• Neuroscience
• Biophysics
• +۳ مورد دیگر

Gustavo Deco is a Research Professor at the Institució Catalana de Recerca i Estudis Avançats (ICREA) and holds a Professorship (Catedrático) at Pompeu Fabra University (UPF). He leads the Computational Neuroscience Group and directs the Center of Brain and Cognition at UPF. His research focuses on computational models of brain dynamics, integrating biophysics, neuroimaging, and complex systems principles. Deco’s academic journey includes a PhD in Physics (1987, thesis on Relativistic Atomic Collisions), postdoctoral work at the University of Bordeaux (France) and University of Giessen (Germany), and a Habilitation in Computer Science (1997, Technical University of Munich). He has led computational neuroscience research at Siemens Corporate Research Center (1990–2003) and pioneered whole-brain modeling frameworks like The Virtual Brain (TVB). His research interests span critical brain dynamics, non-equilibrium thermodynamics in neural systems, psychedelics’ effects on brain hierarchy, and clinical applications of computational models in disorders such as Alzheimer’s and depression. Recent work emphasizes turbulence-like dynamics in healthy and diseased brains, and biomarker discovery using AI-driven simulations. Deco’s articles (2024–2025) explore topics like entropy production in brain networks, psychedelics-induced flattening of functional hierarchies, sleep-like dynamics post-stroke, and the role of long-range connections in global brain communication. His work bridges theoretical models with clinical insights, aiming to advance personalized neurology and digital brain research.

• Biomechanics
• Cardiovascular System Modeling
• Uncertainty Quantification
• +۵ مورد دیگر

Leif Rune Hellevik is a Professor of Biomechanics at the Department of Structural Engineering, Faculty of Engineering at NTNU. He also serves as Vice Dean for Master Programs since 2017. His research focuses on computational biomechanics, particularly in cardiovascular systems, emphasizing uncertainty quantification and sensitivity analysis. Key projects include STARFiSh (Stochastic Arterial Flow Simulations) and MyMDT (Medical Digital Twin for hypertension management). He has extensive experience in fluid-structure interaction and numerical methods, contributing to clinical decision support systems and translational hypertension research. His teaching includes courses on numerical methods and biomechanics. Research Interests: His work spans biomechanical modeling of arterial systems, cardiovascular hemodynamics, and translational applications of computational models for personalized medicine. Recent efforts integrate machine learning and digital twins to improve hypertension treatment and prevention. Publications: Over 30 peer-reviewed articles since 2000, with recent focus on sensitivity analysis, model validation, and clinical applications in coronary artery disease. Collaborations include institutions like Linköping University, University of British Columbia, and Erasmus MC. Labs/Teams: Leads projects in computational biomechanics and vascular modeling, collaborating with multidisciplinary teams in engineering, medicine, and data science.

Cathy McLellanمشاهده پروفایل

دانشیار

• Interventional Cardiology
• Cardiac Program Management
• Medical Education
• +۳ مورد دیگر

Cathy McLellan is an Associate Professor and Interim Division Chair of Cardiology at Queen’s University, a role she has held since December 2024. She joined the Queen’s Cardiology Division in 2003 and served as Chair from 2017 to 2022. Dr. McLellan is also an interventional cardiologist at Kingston General Hospital, where she leads clinical roles in Percutaneous Coronary Interventions and Cardiac Program Medical Direction (2017–2020). Her work focuses on expanding regional cardiac care programs, such as the Southeastern Ontario Regional MI program, and overseeing postgraduate training in cardiology. Her expertise spans clinical cardiology, healthcare leadership, and medical education. She has held key administrative roles, including directing the Cardiology residency program and contributing to institutional governance through divisional leadership positions. While no specific awards are listed, her contributions to clinical practice and education are central to her professional profile. Dr. McLellan’s current focus includes advancing cardiac care protocols and maintaining operational excellence in hospital-based cardiology services. No formal research grants or student advisement details are provided in the text, though her administrative roles suggest involvement in training future clinicians.

Kevin Floresمشاهده پروفایل

دانشیار

• Mathematical Biology
• Precision Medicine
• Environmental Toxicology
• +۴ مورد دیگر

Kevin Flores is an Associate Professor in the Department of Mathematics at North Carolina State University (NC State), and Director of the Biomathematics Graduate Program. He leads the Flores Lab, focusing on developing mathematical and statistical methods for parameter estimation, uncertainty quantification, and forecasting in Precision Medicine, Environmental Toxicology, and Synthetic Biology. His work bridges computational approaches with biological systems analysis. Dr. Flores earned his PhD in 2009 from Arizona State University. His research groups include the Mathematical Biology cluster within the Department of Mathematics. His affiliations include Cox Hall 406D and the College of Sciences at NC State. Research interests emphasize interdisciplinary applications: (1) Mathematical Biology involving tumor heterogeneity, viral dynamics, and angiogenesis modeling; (2) Biostatistics focusing on parameter estimation in complex systems; and (3) Computational Tools for biomedical image analysis and machine learning in healthcare. His lab pioneered methods like biologically-informed neural networks and topological data analysis for biological systems. Recent work highlights include: (1) tumor spheroid modeling predicting clinical variability; (2) BK virus infection dynamics in transplant patients; (3) EEG-based brain-computer interface improvements using GANs; and (4) few-shot learning for plant phenotyping. His methodologies address challenges in sparse data scenarios and integrate mechanistic understanding with data-driven approaches. Awards and recognition : None explicitly listed in provided texts. Advising and grants: No specific advisees or grant details provided in texts. His lab's software tools support image segmentation and population modeling. Labs/teams: Directs the Flores Lab for Mathematical Biology at NC State, specializing in hybrid computational-experimental approaches. Collaborates across departments in biomathematics and engineering.

Mette S. Olufsenمشاهده پروفایل

استاد

• Mathematical Biology
• Cardiovascular Physiology
• Inverse Problems
• +۵ مورد دیگر

Mette S. Olufsen is a Professor in the Department of Mathematics at North Carolina State University (NC State), affiliated with the College of Sciences. She leads the Cardiovascular Dynamics Research Group and directs the DRUMS REU Program. Her research focuses on mathematical biology, cardiovascular physiology, and inverse problems, with expertise in differential equations and fluid mechanics applied to biomedical systems. Olufsen holds a PhD in Applied and Industrial Mathematics from Roskilde University (1998) and an MS in Mathematics and Computer Science from the same institution. Her work integrates computational models with experimental data to study cardiovascular, respiratory, and inflammatory systems. Notable projects include modeling pulmonary hypertension, autonomic cardiovascular control during orthostatic stress, and parameter optimization in single-ventricle patient models. She collaborates extensively on interdisciplinary projects, publishing in journals like Mathematical Biosciences and Biomechanics and Modeling in Mechanobiology . Her research groups span Control, Optimization, and Modeling; Mathematical Biology; and Numerical Analysis. She also contributes to educational initiatives through the REU program, fostering undergraduate research in biomathematics. Olufsen’s labs and teams focus on translating mathematical models into clinical applications, particularly in understanding cardiovascular dynamics under stress and disease. Her work bridges applied mathematics with biomedical engineering, emphasizing practical clinical relevance and parameter identifiability in complex biological systems.

• Global Health
• Public Health Policy
• Urban Health
• +۷ مورد دیگر

Jo Ivey Elizabeth Boufford, M.D., is a Clinical Professor of Global Health at the New York University School of Global Public Health and Clinical Professor of Pediatrics at the NYU School of Medicine. She holds leadership roles as President Emeritus of The New York Academy of Medicine and Immediate Past President of the International Society for Urban Health. Previously, she served as Dean of the Robert F. Wagner Graduate School of Public Service (1997–2002), Principal Deputy Assistant Secretary for Health in the U.S. Department of Health and Human Services (1993–1997), and President of the New York City Health and Hospitals Corporation (1985–1989). Education: BA in Psychology (Magna Cum Laude), University of Michigan MD (with distinction), University of Michigan Medical School Research Focus: Boufford’s work centers on urban health systems, global health equity, aging populations, and health policy. She has pioneered initiatives like the Better Hearts Better Cities program and contributed to the Lancet Commission on Pollution and Health. Key Awards: Member, US National Academy of Medicine (1992) Member, National Academy of Public Administration (2015) Multiple honorary doctorates from institutions including Pace University and SUNY Advisory Roles: Currently serves on the Boards of the United Hospital Fund, National Hispanic Health Foundation, and Health Effects Institute. She chairs the Public Health Committee of the NYS Public Health and Health Planning Council. Labs/Teams: Collaborates with global health networks like the International Society for Urban Health and the World Health Organization (WHO), where she represented the U.S. from 1994–1997.

• Soft Materials
• Biological Materials
• Neutron Scattering
• +۴ مورد دیگر

Jan Swenson is a researcher at Chalmers University of Technology, where she earned her PhD in Physics in 1996. Her research focuses on soft materials, particularly the role of water in biological systems and supercooled water dynamics. She employs neutron scattering and molecular dynamics simulations to study protein aggregation (e.g., Alzheimer's and Huntington's diseases), lipid nanoparticles for RNA delivery, and sugar-protein interactions. Her work bridges physics, chemistry, and biomedicine, aiming to develop novel materials for medical applications and energy storage. Education: PhD in Physics, Chalmers University of Technology (1996) Postdoctoral Research, University College London, UK (structure/stability of clay gels) Research Interests: Soft materials, biological materials' hydration, neutron scattering analysis, supercooled water properties, and protein stabilization via sugars. Her recent projects include modeling material dynamics using computer simulations and optimizing lipid nanoparticles for therapeutic RNA delivery. Grants & Collaborations: Works with colleagues to develop new data analysis methods for neutron scattering. Active in interdisciplinary projects involving drug delivery systems and materials science. Labs/Teams: Part of Chalmers' research groups focused on biomaterials and energy materials, contributing to structural battery electrolyte development.

Alejandro Laguna Sanzمشاهده پروفایل

استادیار

• Diabetes Technology
• Glucose Monitoring
• Artificial Pancreas Systems
• +۷ مورد دیگر

Alejandro Laguna Sanz is an Assistant Professor in the Department of Electronic Engineering at the School of Engineering, Universitat de València. His work bridges biomedical engineering and clinical diabetes care, focusing on technological solutions for glucose regulation. His research interests include diabetes technology, artificial pancreas systems, glucose monitoring accuracy, and the impact of exercise on glycemic control. He is particularly active in developing and evaluating predictive algorithms for glycemic management, especially in dynamic conditions such as physical activity and post-bariatric hypoglycemia. His work often combines control theory with clinical validation. The recent publications reveal a strong focus on real-world performance of continuous glucose monitors (CGMs), sex-specific responses in women with type 1 diabetes, and innovative interventions like elastic band resistance training and dual-hormone closed-loop systems. His research increasingly integrates population modeling, in silico studies, and virtual education platforms for artificial pancreas training. He is a member of the CLIDET research group (Clinimetry and Technological Development in Therapeutic Exercise), where he contributes to the design and clinical evaluation of advanced monitoring and control systems for metabolic diseases. Alejandro Laguna Sanz earned his PhD from Universitat Politècnica de València with a thesis on uncertainty in postprandial glucose modeling in type 1 diabetes, supervised by Dr. Paolo Rosseti and Dr. Jorge Bondía Company. He has not received any explicitly listed scientific awards in the provided text. He advises students within his research group, though no specific names are listed. His work is supported by collaborative projects in diabetes technology and engineering, particularly in the development of adaptive control systems and clinical validation of glucose monitoring devices. He is involved in both experimental and simulation-based studies.

Elizabeth Chinمشاهده پروفایل

استادیار

• Causal Inference
• Algorithmic Fairness
• Machine Learning
• +۱۲ مورد دیگر

Elizabeth Chin is an Assistant Professor in the Department of Biostatistics at the Bloomberg School of Public Health, Johns Hopkins University. Her work bridges biostatistics, causal inference, and public health, with a strong focus on equity and ethical decision-making systems. PhD in Biomedical Data Science, Stanford University (2022) BS in Applied Mathematics, UCLA (2017) Her research interests include causal inference, algorithmic fairness, machine learning, and their application to public health challenges such as health inequities, environmental justice, and policy evaluation. She develops statistical methodologies to improve the robustness and fairness of data-driven decisions, particularly in vulnerable populations including incarcerated individuals. Elizabeth Chin's recent publications span high-impact areas such as mental health care disparities, forensic statistics, environmental lead exposure, and pandemic response. Her work often appears in top journals like Nature Machine Intelligence , NEJM , JAMA Pediatrics , and PNAS . A key trend across her articles is the use of advanced statistical methods to address structural injustices and inform equitable policy. National Science Foundation Graduate Fellowship Stanford Graduate Fellowship She has advised and collaborated with leading researchers in biostatistics and public health. Her work has received significant media attention and has been referenced in policy discussions, indicating real-world impact. Elizabeth Chin is actively contributing to national conversations on data ethics, health equity, and scientific rigor in public health.