جست‌وجوی استاد و پژوهشگر

• Cardiology
• Cardiovascular Imaging
• Health Economics
• +۳ مورد دیگر

Dr. Faraz Pathan is a Senior Lecturer at Sydney Medical School and a cardiologist specializing in cardiovascular imaging. Based at Nepean Hospital, he serves as Head of Cardiac Imaging and Cardiac Research within the Cardiology Department. University of New South Wales (2006), First Class Honours His research focuses on precision phenotyping of cardiovascular pathophysiology using Cardiac Magnetic Resonance (CMR), atrial strain as a cardiovascular outcome predictor, appropriate cardiac imaging use, heart failure readmission reduction, and health economics applications in cardiology. Key trends in his 2021-2025 publications include multimodality imaging techniques, atrial strain analysis, chemotherapy-related cardiotoxicity, and obesity-related cardiovascular imaging challenges. Collaborative projects span institutions like Goethe Institute Frankfurt and Menzies Institute for Medical Research. Scientific recognition includes the Ten of the Best research award from Menzies Institute for Medical Research. Grants highlight his work in microvascular restoration (REDUCE trial), obesity-cardiovascular interactions, and cardiac inflammation mapping. He leads a nine-researcher team in echocardiography quality improvement, atrial strain analysis, heart failure protocols, and health economics studies. International collaborations with Goethe Institute Frankfurt and Menzies Institute for Medical Research demonstrate his global research impact, particularly in experimental cardiovascular imaging and translational studies.

Laurence Likforman-Sulemمشاهده پروفایل

دانشیار

• Document Analysis
• Handwriting Recognition
• Historical Document Processing
• +۷ مورد دیگر

Laurence Likforman-Sulem is an Associate Professor at Institut Polytechnique de Paris , affiliated with the Signal, Statistics and Learning (S2A) team in the Image, Data, Signal (IDS) department . She has been at Télécom Paris since 1991, where she teaches Pattern Recognition , Signal Processing , and Document Analysis . PhD from ENST-Paris (1989) HDR from Sorbonne University (2008) Her research integrates Markovian methods (HMMs, Bayesian Networks) and deep learning (BLSTMs, CNNs) for: Handwriting recognition in historical documents Character analysis in Byzantine seals Parkinson’s disease detection through multimodal signals Biometric authentication using hand shape Recent work focuses on Byzantine seal character recognition (BHAi project) and multimodal group cohesion analysis (IEEE ICMI 2021 Best Paper). She has supervised 10 PhD students and numerous Master internships. Scientific Awards Winning system at ICDAR 05 Arabic Hand-Written Word Recognition Competition Fondation Telecom Thesis Award 2014 (2nd prize for Olivier Morillot) Best Paper Award, ICMI 2021 Active in conference leadership, she chaired ICDAR 2015 and ICPR 2022 document analysis tracks. Her 15 most recent publications span Byzantine document analysis, Parkinson’s detection, and low-energy neural architectures.

Michael Levittمشاهده پروفایل

استاد

• Computational Biology
• Molecular Dynamics
• Protein Folding
• +۵ مورد دیگر

Michael Levitt is the Robert W. and Vivian K. Cahill Professor of Cancer Research and Professor of Structural Biology at Stanford University School of Medicine. He is also a member of Bio-X and the Wu Tsai Neurosciences Institute. Dr. Levitt served as Chair of the Department of Structural Biology from 1993 to 2004 and as Associate Chair from 2005 to 2010. A Nobel Laureate in Chemistry (2013), he is a member of the US National Academy of Sciences, the American Academy of Arts & Sciences, and a Fellow of the Royal Society. Dr. Levitt pioneered computational biology, establishing the conceptual and theoretical framework for the field. His research focuses on three interconnected areas: 1) predicting protein folding with emphasis on hydrophobic forces; 2) predicting protein structure from sequence through homology modeling and refinement of near-native structures; and 3) mesoscale modeling of large macromolecular complexes like RNA polymerase. His work employs diverse energy functions ranging from statistical potentials to quantum-mechanical force-fields. His technical expertise includes developing simulation packages, molecular graphics interfaces, and advanced scripting capabilities. Dr. Levitt's recent publications demonstrate his continued leadership across computational chemistry, molecular biology, and public health. His work spans fundamental research on molecular force fields and protein dynamics to epidemiological analyses of pandemic impacts. He has made significant contributions to understanding water ionization, neural network applications in molecular modeling, and global patterns of excess mortality during the COVID-19 pandemic. Nobel Prize in Chemistry (2013) Member, US National Academy of Sciences (2002) Fellow, The Royal Society (2001) Member, American Academy of Arts & Sciences (2010) Anniversary Prize, Federation of European Biochemical Societies (1986) Member, European Molecular Biology Organization (1981) Dr. Levitt actively mentors students through numerous independent study and research courses across computer science, biomedical informatics, biophysics, and structural biology programs. His teaching includes Advanced Reading and Research, Biomedical Informatics Teaching Methods, and various directed research opportunities. He has supervised work in computational biology, protein structure prediction, and molecular dynamics. His lab collaborates extensively with experimentalists to overcome challenges in modeling large, complex biological systems. Dr. Levitt leads a research group focused on developing advanced computational methods for molecular simulation. His team works on integrating machine learning with traditional force-field approaches to improve accuracy while maintaining computational efficiency. Current projects include neural network corrections for molecular force fields, mesoscale modeling of macromolecular complexes, and analysis of global health data related to pandemic impacts.

Cameron Brownمشاهده پروفایل

دانشیار

• Pharmaceutical Manufacturing
• Crystallization Processes
• Digital Design Tools
• +۴ مورد دیگر

Dr. Cameron Brown is a Reader (equivalent to Associate Professor) at the Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow. He specializes in developing digital design tools and strategies for pharmaceutical manufacturing. Brown joined Strathclyde in 2014 and has progressed through research associate, research fellow, and Chancellor's fellow positions. He currently coordinates the Drug Substance Manufacturing module for the Advanced Pharmaceuticals Manufacturing MSc program. Education: Brown holds a PhD in crystallization process characterization and a Chemical Engineering degree, both from Heriot-Watt University. Research Focus: His work centers on three primary areas: Hybrid modeling approaches : Integrating physics-based and data-driven models to enhance drug substance manufacturing efficiency Self-driving labs : Developing automated systems for drug substance process development with model-based experimental design Digital decision-making : Implementing coupled models through GenAI and LLMs for rapid pharmaceutical process development His research contributes to UN Sustainable Development Goals through improved medicine manufacturing sustainability. Publication Trends: Brown's recent articles focus on pharmaceutical crystallization, digital design methodologies, AI applications in manufacturing, and process optimization. His work demonstrates strong emphasis on translating computational models into industrial practice, particularly in continuous manufacturing and quality-by-design frameworks. Honors: Elected staff officer of British Association of Crystal Growth (2024) Research Leadership: Brown serves as Principal Investigator for PharmaCrystNet and co-investigator on multiple major initiatives including Digital Design and Manufacturing of Amorphous Pharmaceuticals, Future CMAC Manufacturing Hub, Accelerated Discovery and Development of New Medicines Prosperity Partnership, and ARTICULAR. He leads knowledge exchange projects with pharmaceutical companies and manages knowledge transfer partnerships. Professional Engagement: Brown is active in the Acceleration Consortium and serves on the committee of the British Association of Crystal Growth.

Erik Bekkersمشاهده پروفایل

دانشیار

• Geometric Machine Learning
• Deep Learning
• Medical Image Analysis
• +۴ مورد دیگر

Erik Bekkers is an Associate Professor at the University of Amsterdam's Informatics Institute, leading research in the Machine Learning Lab (AMLab). His work bridges geometric mathematics and machine learning, focusing on developing robust and efficient deep learning architectures grounded in symmetry, equivariance, and physical principles. Education: PhD in Biomedical Engineering (cum laude) from Eindhoven University of Technology Previous Roles: Postdoctoral researcher in applied differential geometry at TU/e Department of Applied Mathematics His research spans: Group convolutional neural networks Symmetry-preserving representation learning Generative modeling on manifolds Physics-informed neural networks Medical imaging applications Recent publications emphasize geometric latent variable models, equivariant diffusion methods, and applications to molecular generation, medical imaging, and physics-driven AI. His team actively explores structure-preserving and self-supervised learning techniques. Scientific Awards MICCAI Young Scientist Award (2018) Philips Impact Award (MIDL 2018) NWO VENI grant: Context-Aware AI in Medical Imaging (2023) NWO VIDI grant: Neural Ideograms - Geometry-Grounded AI (2024) As co-founder of the ICML'24 GRaM workshop , he promotes geometry-grounded approaches in AI. His lab actively investigates geometric regularization, manifold-based PDE forecasting, and symmetry-aware generative methods.

James Taylorمشاهده پروفایل

استاد

• Control Engineering
• Data-Driven Modelling
• Automatic Control
• +۳ مورد دیگر

Professor James Taylor is a distinguished academic at Lancaster University where he holds a Personal Chair in Control Engineering within the School of Engineering . As Impact Champion for the School of Engineering and lead for Robotics & Control, he has been instrumental in advancing control engineering research. Previously, he served as group lead for Nuclear Science & Engineering (2019-24) and held senior administrative roles including Director of Teaching and Deputy Head of Engineering (2005-2018). Professor Taylor's research spans data-driven modelling and automatic control for challenging, uncertain systems with applications in Energy systems Healthcare Robotics Environmental monitoring His work has attracted over £10m in UK research council funding as co-investigator across 10+ projects. He has made significant contributions through his research on topics such as Electricity theft detection using machine learning Nuclear fuel analysis with hyperspectral imaging Digital twins for nuclear manufacturing Adaptive medical treatment systems Robotic plant phenotyping platforms Scientific recognition includes: Fellow of the Institution of Engineering & Technology (FIET) Member of IET Academic Accreditation Committee Editorial board member for three Elsevier journals Active participation in UK Automatic Control Council Professor Taylor has supervised numerous PhD students to successful completion and co-develops the internationally used CAPTAIN Toolbox (MATLAB) for system identification and control. He has been involved in developing control systems for Hydraulically actuated dual-arm robots Wave energy converters Assisted tele-operation systems Grow-cell agricultural facilities Motion planning algorithms

Bryan E. Shepherdمشاهده پروفایل

استاد

• Biostatistics
• Causal Inference
• Measurement Error
• +۳ مورد دیگر

Bryan E. Shepherd serves as Professor of Biostatistics and Biomedical Informatics and Vice Chair of Faculty Affairs in the Department of Biostatistics at Vanderbilt University. As a primary faculty member, he leads methodological research while overseeing departmental academic operations within the Vanderbilt University Medical Center ecosystem. His foundational education includes a PhD in Biostatistics from the University of Washington, establishing his technical expertise in advanced statistical methodologies. This training underpins his dual focus on theoretical innovation and real-world health applications. Dr. Shepherd's research centers on solving complex data challenges through novel biostatistical frameworks, particularly causal inference under measurement error, two-phase sampling optimization, and longitudinal analysis of error-prone clinical data. His work bridges theoretical statistics and urgent public health needs, with HIV research forming a major application domain where he addresses disparities in treatment access, care continuum outcomes, and comorbid conditions across global populations. Recent methodological breakthroughs include R packages for semiparametric likelihood estimation and rank correlation analysis. Analysis of his 2023-2025 publications reveals a dominant trend toward methodological solutions for data imperfections (measurement error, selective sampling) combined with high-impact applications in HIV epidemiology. His Latin American and African cohort studies consistently examine sex disparities, treatment efficacy, and social determinants of health, while his statistical innovations focus on efficiency gains in causal estimation and correlation modeling for clustered data. No specific scientific awards are documented in the available institutional profile. Though student advising details are unlisted, his extensive publication record—particularly collaborative international studies—implies active mentorship of junior researchers. His leadership as Vice Chair of Faculty Affairs suggests significant administrative responsibilities alongside research. Grant activity is inferred from multi-country HIV studies involving Vanderbilt's Center for Quantitative Sciences and Data Coordinating Center. Dr. Shepherd operates within Vanderbilt's collaborative research infrastructure, notably the Center for Quantitative Sciences (CQS) and Biostatistics Data Coordinating Center (VBDCC). His work leverages these resources for large-scale analyses of HIV cohorts across Latin America and Nigeria, focusing on treatment outcomes, genetic risk factors, and health system barriers. The Vanderbilt Nigeria Biostatistics Training Program (VN-BioStat) reflects his commitment to global capacity building in biostatistics.

Anne Ryan Driscollمشاهده پروفایل

دانشیار

• Statistical Process Control
• Healthcare Surveillance
• Industrial Statistics
• +۳ مورد دیگر

Anne Ryan Driscoll is a Collegiate Associate Professor in the Department of Statistics at Virginia Tech. She holds affiliations with the College of Science and has research interests in Statistical Process Control, Healthcare Surveillance, and Industrial Statistics. Her work emphasizes practical applications of statistical methods in quality control and biomedical analysis. Education includes a B.S. in Mathematics and Physics from Emory & Henry College (2006), followed by M.S. (2007) and Ph.D. (2011) in Statistics from Virginia Tech. She has received numerous awards for teaching excellence and departmental contributions, including the Jesse C. Arnold Award and Ellis R. Ott National Scholarship. Her professional memberships include the American Statistical Association and American Society for Quality. Dr. Driscoll serves as a referee for journals like the Journal of Quality Technology and contributed to the 2011 Quality and Productivity Research Conference. Recent publications focus on control chart methodologies, biomedical applications of statistics, and experimental design. Her research bridges theoretical statistical frameworks with real-world quality assurance and healthcare applications.

Todd D. Littleمشاهده پروفایل

استاد

• Structural Equation Modeling
• Developmental Psychology
• Methodology and Statistics
• +۷ مورد دیگر

Todd D. Little is a Professor of Educational Psychology at Texas Tech University, internationally recognized for his work in structural equation modeling (SEM) and developmental psychology. His research spans applied methodology, action-control processes, motivation, coping, and self-regulation, with a focus on developmental trajectories and cross-cultural influences. Education: Ph.D., Developmental Psychology, University of California, Riverside (1988) B.A., English Literature, University of California, Riverside (1983) Little has pioneered innovations in SEM, including indicator selection, parceling, and modeling developmental processes. His substantive research explores motivational self-beliefs, school achievement, and the social-personality nexus in childhood and adolescence. He has authored over 280 publications, including the seminal Longitudinal Structural Equation Modeling (2013), and his work has received over 45,000 citations with an H-index of 95. His recent publications highlight methodological rigor in longitudinal analysis and practical applications in diverse fields. Topics include chronic wound healing, educational equity, coping during crises, and prevention science, reflecting his interdisciplinary impact. Scientific Awards and Leadership: Elected Member, Society for Multivariate Experimental Psychology (2001) President, APA Division 5 (Evaluation, Measurement, and Statistics) (2009) Little founded Stats Camps, an international training program in quantitative methods, and has served on review panels for NSF, NIH, and the Jacobs Foundation. He has led over 15 grants and consulted on 70+ projects, contributing to measurement tools like the CAMI and Multi-CAM.

Fabrizia Mealliمشاهده پروفایل

استاد

• Causal Inference
• Econometrics
• Statistical Methods
• +۳ مورد دیگر

Fabrizia Mealli is a Full-time Professor in the Department of Economics at the European University Institute (EUI), serving as Director of Graduate Studies. She holds a concurrent leave position as Professor of Statistics at the University of Florence's Department of Statistics, Informatics, Applications (DiSIA). Her research focuses on causal inference, econometric methods, Bayesian analysis, and missing data, with applications in social and biomedical sciences. Education & Academic Roles: Professor of Statistics, University of Florence (on leave) Full-time Professor, EUI Department of Economics (since 2023) Visiting positions at Harvard University (2001, 2015, 2017) Research Interests: Her work centers on causal inference methodologies, including principal stratification, interference, and bipartite settings. She develops statistical techniques for observational and experimental studies, Bayesian approaches, and applications in policy evaluation and network effects. Awards & Editorial Roles: Elected Fellow of the American Statistical Association (ASA) President-elect of the Society for Causal Inference (SCI) Associate Editor for Biometrika , Journal of the American Statistical Association , and Annals of Applied Statistics Grants & Collaborations: Her research has been applied to policy evaluation (e.g., hospital readmission programs, immigration policies) and public health interventions (e.g., air pollution effects). Labs & Teams: Leads initiatives on causal inference under interference and network analysis, collaborating with global institutions and researchers in statistics and econometrics.

Matthew Hoffmanمشاهده پروفایل

استاد

• Oceanic and Atmospheric Dynamics
• Plastic Pollution
• Freshwater Systems
• +۹ مورد دیگر

Matthew Hoffman is a Professor in the School of Mathematics and Statistics at Rochester Institute of Technology (RIT), within the College of Science. His work bridges theoretical and applied research across oceanic and atmospheric dynamics, plastic pollution in freshwater systems, data assimilation, remote sensing, and cardiac electrical dynamics. Education: BA, Williams College MS, PhD, University of Maryland Research Focus : He investigates plastic pollution transport and fate in freshwater ecosystems, particularly the Great Lakes, using mathematical modeling and data assimilation techniques. His work extends to hyperspectral vehicle tracking, cardiac dynamics reconstruction, and interdisciplinary climate change research. Recent Publications highlight his contributions to microplastic distribution modeling, remote sensing applications, and cardiac wave dynamics. His research team at RIT has secured nearly $10 million in funding since 2020 for plastic pollution studies. Scientific Leadership : Hoffman co-developed adaptive sensor systems for vehicle tracking and advanced data assimilation methods for environmental forecasting. He teaches multidisciplinary courses on climate change and mentors graduate students in environmental science and imaging technology. Labs & Collaborations : He leads RIT's interdisciplinary microplastics research group and collaborates with NY Sea Grant to expand marine debris studies in Great Lakes communities.

Steven Goodmanمشاهده پروفایل

استاد

• Epidemiology
• Biostatistics
• Clinical Research Methods
• +۴ مورد دیگر

Steven Goodman is a Professor of Epidemiology and Population Health, and Medicine at Stanford University, serving as Associate Dean of Clinical and Translational Research. He leads initiatives like the Stanford Program on Research Rigor and Reproducibility (SPORR) and co-directs the Meta-research Innovation Center at Stanford (METRICS). His research focuses on improving research reproducibility, evidence synthesis, and clinical trial methodology. Goodman holds academic appointments in multiple departments and has served on prestigious committees such as the National Academy of Medicine and the Patient-Centered Outcomes Research Institute (PCORI). Educationally, he earned an AB from Harvard, an MD from NYU, and advanced degrees in Biostatistics and Epidemiology from Johns Hopkins. He has authored over 200 publications, emphasizing Bayesian methods, ethical research practices, and meta-research. His awards include the Abraham Lilienfeld Award and induction into the National Academy of Medicine. Goodman teaches courses on clinical research methods, scientific inference, and diagnostic technologies. Key contributions include advancing open science practices, data-sharing policies, and critical analyses of FDA decision-making. His work bridges statistical rigor with practical applications in healthcare, aiming to enhance the reliability and relevance of biomedical research.

• Biostatistics
• Epidemiology
• Health Services Research
• +۱۲ مورد دیگر

Dr. Lin Liu is an Associate Adjunct Professor in the Department of Family Medicine and Public Health at the Herbert Wertheim School of Public Health & Human Longevity Science, UC San Diego. Her research focuses on biostatistical methodologies, including dose-response analysis, longitudinal HIV data modeling, and colorectal cancer prognostic modeling. She collaborates on studies addressing cancer, cardiovascular disease, hepatitis C, chronic pain, HIV/AIDS, and telemedicine. Dr. Liu’s work spans epidemiology, health services research, and clinical trial design. Her research interests include statistical methods for detecting minimum effective doses in toxicity studies, HIV viral load analysis, and predictive models for cancer recurrence. She has contributed to studies on opioid use, tuberculosis treatment adherence, and yoga interventions for chronic pain. Dr. Liu’s publications reflect her expertise in applying statistical techniques to public health challenges, such as improving cancer screening strategies and evaluating telehealth outcomes for mental health conditions like PTSD. Notable contributions include developing prognostic models for colorectal neoplasia recurrence and investigating the efficacy of videoconferencing psychotherapy. Her interdisciplinary collaborations span infectious disease epidemiology, psychiatric disorders, and biomedical informatics, emphasizing patient-centered care and electronic health record optimization.

• Microbiome Research
• Hepatology
• Genetic Epidemiology
• +۳ مورد دیگر

Xinlian Zhang is an Assistant Professor in Residence at the Herbert Wertheim School of Public Health & Human Longevity Science at UC San Diego. Their research focuses on microbiome dynamics, liver diseases (including NAFLD and alcoholic hepatitis), and statistical modeling in biomedical contexts. Collaborations include work with institutions like Altman Clinical and Translational Research Institute. Key research areas include the role of mycobiome and virome in liver pathology, host-microbiota interactions, and the application of advanced statistical methods to microbiome data. Zhang has contributed to studies linking microbiota composition to disease severity, drug responses, and psychological factors like loneliness. Publications emphasize interdisciplinary approaches, integrating virology, microbiology, and computational biology. Notable work includes analyzing fecal mycobiome in NAFLD patients and identifying virome signatures in alcoholic hepatitis. Co-authors include prominent researchers like B. Schnabl and R. Loomba. No specific awards or grants are listed, but their work has been cited widely (e.g., 87+ mentions for a 2020 Gastroenterology paper). Collaborations span institutions within the UC system and beyond, reflecting a network engaged in translational and computational biomedical research.

• Fluid Mechanics
• Mechanical Engineering
• Turbulence
• +۷ مورد دیگر

Romano Giampaolo is a Full Professor at the Department of Mechanics and Aeronautics, Sapienza University of Rome. His research focuses on fluid dynamics, turbulence, and energy systems, with notable contributions to experimental methods like PIV (Particle Image Velocimetry). He explores topics ranging from aeroelastic energy harvesters to granular flows and biomedical applications of fluid dynamics. His work also occasionally intersects with corporate governance and financial structures, including studies on SPACs. Key research areas include turbulence modeling, energy conversion systems (e.g., piezoelectric and wind turbines), and fluid-structure interactions. His experimental investigations often involve high-resolution flow measurements and interdisciplinary applications, such as vascular aneurysm sealing systems and microchannel heat transfer devices. Recent articles highlight advancements in understanding multiphase flow dynamics, high-pressure methane jet behavior, and the structural effects of obstacles on fluid jets. His work bridges theoretical fluid mechanics with practical engineering solutions, particularly in renewable energy and medical device development.