جست‌وجوی استاد و پژوهشگر

• Pediatric HIV
• Prevention of Mother-to-Child Transmission (PMTCT)
• Public Health Surveillance
• +۷ مورد دیگر

Professor Gayle Sherman is a full-time faculty member in the Department of Paediatrics and Child Health at the University of the Witwatersrand, South Africa. She holds the academic rank of Professor and is actively engaged in research and clinical public health, focusing on pediatric HIV, prevention of mother-to-child transmission (PMTCT), and health systems resilience in resource-limited settings. Her research interests center on pediatric HIV care , particularly viral load monitoring, retention in treatment programs, and the impact of health policies and pandemics such as COVID-19 on service delivery. She investigates challenges in infant HIV diagnosis , data governance, and ethical considerations in managing patient information within public health systems. Her work bridges clinical practice and public health implementation, with strong emphasis on equity and access in South Africa. The recent publications highlight a consistent focus on HIV surveillance , PMTCT program effectiveness , and health system challenges during crises. Her studies utilize retrospective data analyses, policy reviews, and implementation research to evaluate service delivery gaps and inform national guidelines. Keywords across her work include pediatric HIV, viral load monitoring, PMTCT, health ethics, digital health, and pandemic resilience. While no specific awards are listed in the provided text, her scholarly output in high-impact journals such as JMIR Public Health and Surveillance , AIDS and Behavior , and Southern African Journal of HIV Medicine reflects significant contributions to the field. She has contributed to national guideline development and ethical frameworks in HIV care. Professor Sherman advises on public health policy through her research and has likely contributed to training and mentorship within her department, though no formal students are listed. Her work is supported through institutional and public health research frameworks, with implications for funding bodies such as South Africa’s Department of Health and international HIV programs. She is involved in analyzing real-world data to improve pediatric HIV outcomes and strengthen health systems. She is affiliated with research initiatives focused on pediatric HIV surveillance and implementation science within the School of Clinical Medicine. Her team likely includes public health researchers, data analysts, and clinicians working on retrospective studies and policy evaluations in urban and rural South African settings.

Jun Hongمشاهده پروفایل

استاد

• Artificial Intelligence
• Data Science
• Intelligent Autonomous Systems
• +۷ مورد دیگر

Professor Jun Hong holds the chair in Artificial Intelligence at the University of the West of England within the School of Computing and Creative Technologies. He is a key member of the Computer Science Research Centre (CCRC) and has led research initiatives in data science, graph mining, and intelligent autonomous systems. Research focus areas: Data extraction, integration, and linkage; graph mining; social network analysis; AI planning; BDI-based multi-agent systems; privacy preservation Major grants: Administrative Data Research Centre (ESRC, £4.9M), PACES (EPSRC, £623K), DEVELOP (EU Horizon 2020, £113K), and multiple Innovate UK projects His work spans from foundational research in probabilistic BDI agents and multi-agent planning to applied projects in social media analysis, insurance fraud detection, and healthcare communication systems. Recent publications emphasize graph neural networks, social influence prediction, and uncertainty management in autonomous systems. Supervised 12+ PhD/MSc students Acted as PC/OC Chair for IDEAS 2017 , BNCOD 2006 , and AICS 2006 Peer reviewer for top UK councils and international journals

Robynne Braunمشاهده پروفایل

دانشیار

• Stroke Rehabilitation
• Motor Control
• Kinematics
• +۷ مورد دیگر

Robynne Braun is an Associate Professor in the Department of Neurology at the University of Maryland. She is also Director of the PM&R Program and the Brain Rehab and Recovery Lab. Her research integrates kinesiology, stroke rehabilitation, and motor control. Education : PhD in Movement Sciences (University of Illinois, Chicago), MD (Loyola University Stritch School of Medicine) Her research focuses on stroke recovery , motor control , and genomic approaches to rehabilitation. She has pioneered kinematic analysis methods and gene therapy integration in neurological disorders. Recent publications highlight her work on genetic markers for stroke outcomes, robotic rehabilitation , and clinical trial design . She has received national recognition, including the 2010 ERF New Investigator Award. Scientific Awards : ERF New Investigator Award (2010) Her leadership roles include directing inpatient rehabilitation programs and collaborating on multinational stroke consortia . She bridges clinical practice with translational research to enhance neuroplasticity in stroke survivors.

• Statistics
• Biostatistics
• Missing Data Analysis
• +۷ مورد دیگر

Dr. Biao Zhang is a Professor in the Department of Mathematics and Statistics at the University of Toledo, College of Natural Sciences and Mathematics. His research focuses on advanced statistical methodologies, particularly in missing data analysis, empirical likelihood, and semiparametric modeling, with applications to clinical trials and biostatistics. University: University of Toledo School: College of Natural Sciences and Mathematics Email: biao.zhang@utoledo.edu Research Interests Dr. Zhang specializes in Statistics , Biostatistics , and Missing Data Analysis . His work develops innovative methods for: Empirical likelihood in clinical trials Density ratio models for ROC curve analysis Causal inference with missing responses Semiparametric estimation techniques Robust methods for treatment effect estimation Publications Overview His extensive publication record (59 papers, 1994-2024) reveals expertise in: Empirical likelihood applications ROC surface methodologies Multivariate analysis Case-control study frameworks Nonparametric and semiparametric approaches Statistical software development Co-author network includes Steven T. Haller , Christopher Cooper , and Pamela S. Brewster .

Barak Pearlmutterمشاهده پروفایل

استاد

• Adaptive systems
• Automatic differentiation
• Theoretical neurobiology
• +۸ مورد دیگر

Professor Barak Pearlmutter is affiliated with Maynooth University in the Faculty of Science & Engineering . His research spans multiple domains including automatic differentiation , neural networks , machine learning , and neuroscience . He has contributed significantly to adaptive systems , brain imaging , and programming language design . Research Interests include: Adaptive systems, automatic differentiation, theoretical neurobiology, neural networks, machine learning, acoustic source separation/localization, neuroscience, brain imaging, programming language design, and computational neuroscience. Publications focus on applying algorithmic differentiation to machine learning, developing neural ODE models for biomedical signals, advancing sparse NMF techniques, and integrating functional programming with numerical methods. Collaborations span institutions like MIT, Oxford, and IEEE societies, with work in brain-computer interfaces , MEG source localization , and neuromodulation for tinnitus treatment. Technical Contributions include the DiffSharp AD library for .NET languages and foundational work on reverse-mode automatic differentiation in functional frameworks. His 2018 Journal of Machine Learning Research survey on AD remains a seminal reference in the field. Application Areas cover biomedical signal processing , optical brain-computer interfaces , cognitive modeling , and neural code optimization . His work intersects computer science, neuroscience, and mathematical computing through sparse decomposition and probabilistic modeling .

Hanne Obermanمشاهده پروفایل

استادیار

• Statistics
• Applied Data Science
• Data Visualization
• +۴ مورد دیگر

Hanne Oberman is a Junior Assistant Professor and PhD candidate in Methodology and Statistics at Utrecht University , affiliated with the Open Science Community Utrecht and the rainbowR initiative. Their work focuses on computational evaluation, data visualization, and addressing missing data challenges within the Missing Data research group. Research interests include Applied Data Science, Human-centered AI, and open science practices. Skills span R programming, GitHub, and LaTeX for statistical analysis and scientific text editing. Active in promoting open science through initiatives like the Open Science Monitor and rainbowR, supporting LGBTQ+ representation in R communities.

William Pouliotمشاهده پروفایل

استادیار

• Econometric Theory
• Financial Econometrics
• Labour Economics
• +۶ مورد دیگر

William Pouliot is an Assistant Professor in the Department of Economics at the University of Birmingham, Birmingham Business School. His research focuses on econometric theory, financial econometrics, and labor economics, with a specialization in parameter stability testing in regression and risk models using weak convergence theory. He has also contributed to understanding uncertainty in overlapping generations (OLG) models and its impact on economic outcomes. PhD in Economics (City University) PhD in Mathematics (Carleton University) His recent publications analyze ESG investing performance, structural breaks in econometric models, and time-varying skill assessment in mutual funds. Articles address topics like country-level ESG impact, Eurozone trade balance dynamics, and statistical methods for missing data. Teaching includes Advanced Econometrics and Undergraduate Econometrics courses.

Johan Henrik Koskinenمشاهده پروفایل

مدرس ارشد

• Bayesian Inference
• Statistical Network Analysis
• Social Network Modeling
• +۷ مورد دیگر

Johan Henrik Koskinen is a Senior Lecturer in Statistics at Stockholm University . He previously held academic positions at the Universities of Melbourne, Oxford, Manchester, and Linkoping . Teaching: Courses in Multivariate Methods and Generalized Linear Models (GLM). Advising: Supervises master’s and PhD students in applied statistics, including Jonathan Januar (funded by ARO Grant W911NF-21-1-0335 for research on missing data in covert networks). Research Interests: Specializes in Bayesian inference and statistical modeling for social networks, focusing on: Partially observed network data and missing data mechanisms Longitudinal and dynamic network analysis Exponential Random Graph Models (ERGM) and stochastic actor-oriented models Computational methods for complex network data Grants: Co-investigator on ARO Grant W911NF-21-1-0335 for covert network analysis Named non-US partner on NSF Proposal No 2005661 for multidimensional networks in car purchases Software Contributions: Development of RSiena (R package for longitudinal network analysis) Maintains MPNet (graphical interface for ERGM)

• Pharmacoepidemiology
• Comparative Effectiveness Research
• Real-World Evidence
• +۵ مورد دیگر

Sebastian Schneeweiss is Professor of Medicine and Epidemiology at Harvard Medical School and Harvard T.H. Chan School of Public Health, and Chief of the Division of Pharmacoepidemiology and Pharmacoeconomics at Brigham and Women's Hospital. His work focuses on the comparative effectiveness and safety of biopharmaceuticals using complex healthcare databases, with particular emphasis on methodological innovations for real-world evidence generation. Dr. Schneeweiss received his medical training at the University of Munich Medical School and his doctoral degree from Harvard. He teaches advanced courses including Database Analytics for Pharmacoepidemiology (EPI286) and Effectiveness Research in Longitudinal Healthcare Databases (EPI253) at Harvard, training the next generation of researchers in cutting-edge methods for healthcare data analysis. His research program is funded by multiple NIH, PCORI, BWF, and FDA grants, focusing on developing and applying analytic methods to improve the scientific validity of epidemiologic analyses using complex longitudinal healthcare databases. His work spans multiple therapeutic areas including cardiovascular medicine, oncology, dermatology, and respiratory diseases, with a consistent emphasis on rapid-cycle analytics for medication safety monitoring and comparative effectiveness research. Dr. Schneeweiss's recent publications (2024-2025) demonstrate strong trends toward methodological innovation in trial emulation, calibration of real-world evidence against randomized trials, and the application of advanced analytics including natural language processing to healthcare databases. His work is increasingly focused on creating standardized frameworks for using real-world evidence to support regulatory decision-making globally. Fellow of the American College of Epidemiology Fellow of the American College of Clinical Pharmacology Fellow of the International Society for Pharmacoepidemiology Past President of the International Society for Pharmacoepidemiology As Principal Investigator on multiple large-scale grants, Dr. Schneeweiss leads the FDA-funded Sentinel Innovation Center and serves as a voting consultant to the FDA Drug Safety and Risk Management Advisory Committee. As co-founder and Science Advisor of Aetion Inc., he develops rapid-cycle analytics platforms for healthcare database networks worldwide. His research group mentors numerous students and postdoctoral fellows working at the intersection of epidemiology, biostatistics, and healthcare data science. The Division of Pharmacoepidemiology and Pharmacoeconomics at Brigham and Women's Hospital, which he leads, serves as a world-leading research and training center that collaborates with the FDA, pharmaceutical companies, and international research networks to advance methods for using real-world data in regulatory and clinical decision-making.

• Machine Learning
• Supervised Learning
• Cluster Analysis
• +۷ مورد دیگر

Marcilio Pereira de Souto is a full professor at the University of Orléans , leading the Fundamental Computer Science Laboratory (LIFO) . His research focuses on machine learning, cluster analysis, and bioinformatics, with a strong emphasis on data complexity and hybrid intelligent systems. He has previously held positions at institutions including the Max Planck Institute of Molecular Genetics and the Federal University of Rio Grande do Norte (UFRN). Education: B.Sc. in Computer Science (UFRN, 1991), M.Sc. in Computer Science (UFPE, 1995), Ph.D. in Electrical Engineering (Imperial College London, 1999) Research Interests Marcilio's work spans machine learning (supervised, unsupervised, and ensemble methods), cluster analysis (multi-objective, constrained, and fuzzy clustering), data complexity (measuring classification difficulty, imbalanced datasets), and bioinformatics (gene expression analysis, cancer data clustering). He also explores hybrid intelligent systems combining multiple techniques for improved performance. Publication Trends His publications (h-index 18) emphasize clustering algorithms, data complexity metrics, and hybrid approaches applied to bioinformatics and computational biology. Recent work focuses on explainable AI for image captioning, while earlier contributions analyze missing data imputation strategies and meta-learning for algorithm selection. Labs & Teams Currently leads research at LIFO , a fundamental computer science laboratory at the University of Orléans. Previously contributed to the Department of Informatics and Applied Mathematics (DIMAp) at UFRN and the Department of Computational Biology at the Max Planck Institute.

Giuseppe Vignaliمشاهده پروفایل

دانشیار

• Food Industry Systems
• Sustainable Packaging
• Life Cycle Assessment (LCA)
• +۴ مورد دیگر

Giuseppe Vignali is an Associate Professor at the Department of Engineering and Architecture, University of Parma, Italy, specializing in Mechanical Industrial Systems (ING-IND/17). He has held this position since 2017, previously serving the Department of Industrial Engineering from 2007 to 2016. His research focuses on sustainable food industry systems, packaging technologies, and Industry 4.0 applications. Ph.D. in Industrial Engineering (2009, University of Parma) Passed State Exam for Engineering Profession (2005) Recipient of Emilia Romagna Region Scholarships (2005, 2006) His work integrates Life Cycle Assessment (LCA) , smart agriculture technologies , and supply chain resilience , with recent studies on biochar concrete , IoT irrigation systems , and digital twins for food process optimization. Publications span environmental impact analysis , mechanical properties of sustainable materials , and behavior-based safety protocols in industrial settings. Scientific achievements include national qualifications for Associate (2012) and Full Professor (2016) roles. Key awards highlight his contributions to food industry sustainability and technology innovation . He serves as Master's Course Tutor and Curriculum Advisor for Engineering for the Food Industry programs.

• Asymptotic Analysis
• Mixed Models
• Finite Gaussian Mixture Models
• +۳ مورد دیگر

Dr. Ziyang Lyu is a Lecturer (tenure track assistant professor) in Statistics at the School of Mathematics and Statistics, UNSW Sydney. He completed his Ph.D. in Statistics at the Australian National University in 2020, followed by postdoctoral research at the University of Queensland (2020-2021) and University of New South Wales (2021-2024). His research spans asymptotic analysis, mixed models with crossed random effects, finite Gaussian mixture models, and machine learning from a statistical perspective with emphasis on semi-supervised learning. Education: Ph.D. in Statistics, Australian National University (2020) Previous Positions: Postdoctoral Research Fellow at University of Queensland (2020-2021), University of New South Wales (2021-2024) His scholarly contributions focus on: Asymptotic theory for mixed effects models Crossed random effects modeling Finite mixture model analysis Statistical approaches to semi-supervised learning Missing data mechanisms in Gaussian mixture models Research trends from his 12 publications (2018-2025) show sustained focus on asymptotic theory, mixed effects modeling, and statistical machine learning with applications to high-dimensional data analysis. His methodological work bridges theoretical statistics with practical implementation in R software. Teaching responsibilities include: 2025 Course Convenor for Data Management for Statistical Analysis and Probability/Stochastic Processes 2024 Course Convenor for Statistics Fundamentals and Applied Regression Analysis

Dong Zhangمشاهده پروفایل

دانشیار

• Receiver Operating Characteristic (ROC) analysis
• Empirical likelihood and semi-parametric inference
• Clinical trial study
• +۵ مورد دیگر

Dong Zhang is an Associate Professor in the Department of Mathematics, Computer Science, and Digital Forensics at Bloomsburg University, Pennsylvania, where he has taught since joining the institution in 2012. His academic role encompasses instruction in mathematics, statistics, and computer science disciplines. His educational qualifications include: B.S. in Mathematics (2004) from Nankai University M.Sc. in Statistics (2007) from Nankai University Ph.D. in Statistics from the University of Toledo Dr. Zhang's research program integrates theoretical and applied statistics with computational methodologies. His primary focus areas include ROC analysis for diagnostic testing, semi-parametric inference techniques, clinical trial design, handling incomplete datasets, experimental methodology, and modern data-intensive approaches including machine learning applications. His clinical trial work at the University of Toledo Medical Campus (2009-2011) demonstrates practical implementation of these statistical frameworks in medical research contexts. He maintains an active virtual research environment and teaches diverse courses spanning foundational mathematics to advanced computational statistics. His technical proficiency across multiple programming languages supports both research innovation and pedagogical effectiveness in data science education.

• Statistics
• Statistical Modeling
• Structural Equation Modeling
• +۱۰ مورد دیگر

Sharon Christ is an Associate Professor in the Department of Human Development and Family Science at Purdue University, with joint appointments in the Department of Statistics, College of Science, and courtesy appointments in the Department of Speech, Language, and Hearing Sciences. She serves as Co-Director of The Methodology Center at Purdue (MCAP) and is actively involved in research focusing on statistics, statistical modeling, adolescent development, and aging. Her work spans multiple research areas including Health and Well-Being, Interpersonal Relations, and Military Families. Dr. Christ received her PhD in Sociology from the University of North Carolina in 2008, an MS in Statistics from the same institution in 2004, an MA in Sociology in 2002, and a BA in Sociology & Political Science from the University of Minnesota in 1994. Her research primarily focuses on applications in the social, behavioral, and health sciences, with expertise in structural equation modeling (SEM) with latent variables, multilevel (mixed effects) models, longitudinal modeling, and analysis of complex sample data. She investigates measurement of social constructs, missing data, and selection problems, often using large nationally representative datasets as well as data from observation and experimental designs. A significant portion of her recent work examines child maltreatment experiences in adolescence and their impacts on well-being outcomes including academic achievement, aggressive behaviors, and mental health. Analysis of Dr. Christ's recent publications reveals a strong focus on developmental language disorders, military family research, and statistical methodology. Her work increasingly integrates retrieval practice techniques for language learning in children with developmental disorders, examines long-term trajectories of mental health outcomes in military families, and investigates the relationship between physical activity, stress, and health outcomes in diverse populations. There's a clear interdisciplinary trend across her publications, bridging statistics, psychology, speech-language pathology, and military family research. Dr. Christ has received numerous awards and honors including: University Faculty Scholar, Purdue University (2022-present) Christine M. Ladisch Faculty Leadership Award, Purdue University (2022) Faculty Leadership Academy for Interdisciplinary Research (FLAIR) Fellow, Purdue University (2020) Honorable Mention for Disruption Paper Competition, National Foundation to End Child Abuse and Neglect (2019) College of Science Team Award for Statistics Living Learning Community, Purdue University (2015) Center for Aging and the Life Course Outstanding Professor Award for Exceptional Teaching and Mentoring of Emerging Scholars in Aging, Purdue University (2014) Dr. Christ actively mentors graduate students and is accepting applications for the December 1, 2024 deadline. She has secured significant research funding, including as Co-Principal Investigator for the 'Interdisciplinary Summer Institute on the Analysis of Complex, Large-Scale Longitudinal Data' from the National Institute on Drug Abuse ($365,129, 2024-2027) and as Co-Investigator on multiple substantial grants from agencies including the U.S. Army Medical Research & Development Command, National Institute on Deafness and other Communication Disorders, and Eunice Kennedy Shriver National Institute of Child Health and Human Development. Dr. Christ is affiliated with several research centers at Purdue including the Center for Families, The Military Family Research Institute, The Methodology Center at Purdue (MCAP), and the Center on Aging and the Life Course. These affiliations support her interdisciplinary research on family dynamics, military families, statistical methodology, and aging processes.

• Medical Image Analysis
• Deep Learning in Healthcare
• Vascular Segmentation
• +۳ مورد دیگر

Baochang Zhang is an Assistant Professor in the Department of Informatics at the Technical University of Munich's School of Computation, Information and Technology. He is affiliated with the Chair of Computer Applications in Medicine (Prof. Navab) at the Garching campus, specializing in medical image analysis and AI-driven healthcare solutions. His research focuses on Medical Image Analysis with emphasis on vascular structures, including: Deep learning for low-dose CT denoising and X-ray angiography processing Multi-modal fusion techniques for cognitive impairment prediction Real-time surgical guidance systems for endovascular procedures Self-supervised learning frameworks for vessel segmentation Analysis of his 15 most recent publications (2019-2025) reveals a strong trajectory in solving clinical imaging challenges through innovative AI methods. His work consistently bridges computer vision and clinical applications , with increasing focus on zero-shot learning, domain adaptation, and surgical robotics integration since 2022. Key trends include replacing traditional segmentation with diffusion models and addressing missing data in multi-modal clinical datasets. No scientific awards were documented in the provided materials. While no formal advisees are listed in the scraped data, his lab appears to focus on translational medical AI projects with strong industry and clinical partnerships. The publications suggest active grant funding in EU medical technology initiatives, particularly for intraoperative imaging systems and neurodegenerative disease prediction tools. Zhang leads research within TUM's medical imaging group under Prof. Navab, likely contributing to the CAMP (Computer Aided Medical Procedures) Lab ecosystem. His team develops clinical decision support systems with emphasis on real-time vascular analysis during interventions.