جست‌وجوی استاد و پژوهشگر

Charmaine Deanمشاهده پروفایل

استاد

• Biostatistics
• Environmental Science
• Epidemiology
• +۷ مورد دیگر

Charmaine Dean is the Vice-President, Research and Professor in the Department of Statistics and Actuarial Science at the University of Waterloo. She holds leadership roles in research administration and academic governance, including previous service as Dean of Science at Western University and Associate Dean at Simon Fraser University. Dr. Dean earned her Ph.D. in Statistics from the University of Waterloo in 1988. Her research focuses on biostatistics, environmental science, and spatio-temporal analysis, with applications to public health, wildfire management, and ecological modeling. Notable contributions include disease mapping, clinical trial design, and statistical methods for analyzing wildfire risk and forest ecology. She has received prestigious awards such as the CRM-SSC Prize, Fellowships from leading statistical societies, and the L’Ordre des Palmes Académiques from France. Her service includes roles on advisory boards for institutions like the Pacific Institute for the Mathematical Sciences and the Banff International Research Station. Dr. Dean’s work bridges academic research and policy, with impactful contributions to environmental and public health policy through interdisciplinary collaborations. Her research group’s activities include developing statistical tools for environmental risk assessment and pandemic response.

• Supply Chain Management
• Time-Series Analysis
• Data-Mining
• +۲ مورد دیگر

Vladimir Kovtun is a Visiting Assistant Professor at the Sy Syms School of Business, Yeshiva University. He holds a PhD from NYU Stern School of Business and joined Yeshiva University as a Research Fellow in 2013. His research focuses on the intersection of time-series analysis and supply chain management, particularly the value of information-sharing in supply chains using modern forecasting models. Previously, he studied data-mining and high-dimensional data. He teaches courses in Business Statistics, Quantitative Methods, Business Intelligence, and Consumer Insights. His work has been published in journals such as Biostatistics and Naval Research Logistics . Dr. Kovtun works across two campuses: Belfer Hall (Wilf Campus) and 215 Lexington Ave (Beren Campus).

Vincent Jaddoeمشاهده پروفایل

استاد

• Pediatrics
• Epidemiology
• Developmental Biology
• +۵ مورد دیگر

Vincent Jaddoe is a Professor of Pediatric Epidemiology at Erasmus University Rotterdam and an Adjunct Professor at Harvard T.H. Chan School of Public Health . He is Chair of the Division of General Pediatrics at Erasmus MC, Sophia Children’s Hospital, and leads the Generation R Study , a major population-based cohort involving 10,000 pregnant women and their children. His work bridges epidemiology, pediatrics, and epigenomics, focusing on developmental origins of health and disease. Current Positions Chair, Division of General Pediatrics, Erasmus MC, Netherlands Principal Investigator, Generation R Research Core Facility Project Coordinator, EU LifeCycle Project Jaddoe’s research explores how genetic, epigenetic, and environmental factors during pregnancy influence lifelong health. His studies span fetal development , childhood obesity , and preconception health . He utilizes advanced epidemiological methods, imaging, and omics technologies, collaborating with over 20 departments and 150 PhD students. Key publications address fetal growth restriction , maternal smoking , and genetic heritability in child health. Articles highlight cross-disciplinary approaches integrating epidemiology , neuroscience , and genomics . Scientific Awards Lifetime member of Royal Dutch Academy of Sciences (KNAW) European Research Council (ERC) Consolidator Grant NWO VIDI and VENI grants Jaddoe has received over 900 publications with an H-index of 92 and significant citations. He coordinates the Academic Rotterdam Center for Pregnancy & Childhood , fostering collaboration between researchers, healthcare providers, and policymakers.

Jun Liuمشاهده پروفایل

استاد

• Bioinformatics
• Statistical Genetics
• Bayesian Methods
• +۳ مورد دیگر

Jun Liu is a Professor in the Department of Statistics at Harvard University, renowned for his contributions to computational statistics, bioinformatics, and Bayesian methods. He leads research in statistical genetics, genomic data analysis, and algorithm development for biological systems. His work integrates advanced statistical theory with computational tools, such as the Gibbs Motif Sampler and Bayesian Aligner, widely used in bioinformatics. Research interests include Monte Carlo methods, statistical genetics, and machine learning applications in biology. He has developed influential software tools like BPPS, MDScan, and CLIC, addressing problems in motif discovery, genomic sequence analysis, and pathway expansion. Liu’s interdisciplinary approach bridges statistics and computational biology, with applications in cancer genomics, immune repertoire analysis, and evolutionary biology. Notable recognition includes fellowships from the American Statistical Association, Institute of Mathematical Statistics, and International Society for Bayesian Analysis. He advises numerous Ph.D. students and postdoctoral researchers, many of whom hold academic and industry positions globally. His lab collaborates internationally, organizing workshops on Monte Carlo methods and statistical forums in China. Liu’s publications span statistical methodology, computational biology, and genetics, with recent work on genomic element evolution, immune cell profiling, and algorithmic advancements in high-dimensional data analysis. He emphasizes inverse modeling and Bayesian approaches to tackle complex biological questions.

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

Dr. Jessica Hargreaves is a Lecturer in Data Science at the University of York's Department of Mathematics. Previously, she held roles as a Lecturer in Statistics at Sheffield Hallam University and taught mathematics in secondary schools (2011-2014). She earned an MSci in Mathematics from Imperial College London and a PhD in Statistics from the University of York. Research Focus: Her work spans applied statistics in biology, chemistry, and sports analytics. Key areas include wavelet techniques for nonstationary time series analysis (e.g., circadian rhythms, EEG data), statistical methods in sports (attendance patterns, athlete career trajectories), and collaborations with professional sports organizations. She co-founded Quantitative Sports Analysis: York (QSAY) with Dr. Ben Powell and Dr. Rich Bingham. Roles & Affiliations: Impact Officer, EGPC Committee Member, and Vice-Chair of the Royal Statistical Society's Statistics in Sport Section. Research datasets are available via the University of York repository. Teaching & Scholarship: Specializes in integrating practical learning experiences, evidenced by publications like 'Work-related learning: benefits and challenges' (2019) and pandemic-era pedagogy studies.

• Bayesian Modeling
• Longitudinal Data Analysis
• Experimental Designs
• +۵ مورد دیگر

Elizabeth Slate is a Professor in the Department of Statistics at Florida State University. Her expertise includes longitudinal data analysis, experimental designs, and Bayesian modeling. She focuses on interdisciplinary applications in biomedical research, particularly in musculoskeletal biomechanics, clinical trial design, and public health disparities. Her work bridges statistical methodology with real-world clinical and epidemiological challenges. Research Interests: Her research spans Bayesian statistical methods, causal inference, and the analysis of complex biomedical datasets. Key areas include biomechanical modeling of joint disorders, risk factor analysis for chronic diseases, and the development of innovative statistical frameworks for clinical trials and observational studies. She has contributed to understanding the structural-functional relationships in musculoskeletal tissues and the sociocultural determinants of health behaviors in underserved populations. Publications Trends: Recent work emphasizes Bayesian borrowing of historical data in basket trials, explainable AI in medical imaging, and the biomechanical correlates of TMJ disorders. She also investigates longitudinal health outcomes in diabetes and HPV vaccination uptake among marginalized groups. Methodologically, her articles address causal mediation, sensitivity analysis, and mixed-scale data integration. Grants and Advising: While specific grant details are not listed, her publication record indicates sustained funding in interdisciplinary health research. No advising information is explicitly provided in the source text. Labs/Teams: Collaboration networks likely span biomedical engineering, public health, and clinical departments, though formal affiliations are not detailed here.

J. Richard Landisمشاهده پروفایل

استاد

• Biostatistics
• Epidemiology
• Clinical Research Informatics
• +۶ مورد دیگر

Dr. J. Richard Landis is a Professor of Biostatistics and Statistics at the University of Pennsylvania's Perelman School of Medicine and Wharton School, respectively. As Senior Vice Chair (SVC) of the DBEI Department, he oversees strategic initiatives and serves as a liaison between academic and clinical units. His roles include Director of the Biostatistics Unit at the Center for Clinical Epidemiology and Biostatistics (CCEB), and Director of the Clinical Research Computing Unit (CRCU), supporting translational research infrastructure. He holds advanced degrees from Millersville University (B.S.Ed., 1969), and the University of North Carolina at Chapel Hill (M.S. 1973, Ph.D. 1975). Prior to Penn, he was Professor of Biostatistics at the University of Michigan (1975–1988) and founded Penn State's Center for Biostatistics and Epidemiology (1988–1997). Dr. Landis' research focuses on statistical methodologies for longitudinal and categorical data, with applications to urological, renal, and chronic pain syndromes. He leads NIH-funded Data Coordinating Centers for networks like the MAPP Research Network (2008–2022) and the Chronic Renal Insufficiency Cohort (CRIC) study (2001–2023). His work emphasizes precision medicine, clinical trial design, and translational epidemiology. He has authored over 180 peer-reviewed articles and received prestigious awards, including the Mortimer Spiegelman Gold Medal and the Marvin Zelen Leadership Award. His current projects include the RURAL study (NHLBI) and the HOPE Consortium (NIDDK). Grants & Leadership : NIH-funded DCCs for MAPP, CRIC, and RURAL studies Collaborations : Co-investigator roles in CTSA-Hub cores and multidisciplinary teams Labs/Teams : CRCU, CCEB, and SDCC/SDRC centers

Therese Dolecekمشاهده پروفایل

استاد پژوهشی

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

Therese Dolecek is a Research Professor Emerita at the School of Public Health, University of Illinois at Chicago (UIC), affiliated with the Department of Epidemiology and Biostatistics. She holds the title of Research Professor Emerita, indicating a distinguished retired academic role. Her contact details include an office at 910 SPHPI, 1603 W. Taylor St., Chicago, and a phone number: (312) 996-9516. Her email is tdolecek@uic.edu. While specific research interests or projects are not explicitly detailed in the provided text, her department affiliation suggests expertise in epidemiological and biostatistical methods, public health policy, and data-driven health outcomes research. No awards, grants, or recent publications are mentioned in the available information.

Khurram Nadeemمشاهده پروفایل

دانشیار

• Predictive Modeling
• Wildland Fire Occurrence Prediction
• Spatio-Temporal Processes
• +۴ مورد دیگر

Khurram Nadeem is an Associate Professor of Statistics at the University of Guelph, specializing in predictive modeling of ecological and environmental systems. His research focuses on leveraging big data analytics to address challenges in wildland fire prediction, agricultural sustainability, and statistical bioinformatics. He leads a lab offering graduate research opportunities in these areas and collaborates with organizations like the Canadian Interagency Forest Fire Centre (CIFFC). Education: PhD in Statistics, University of Alberta (2013) MSc in Statistics, University of Karachi (2005) BSc (Honours) in Statistics, University of Karachi (2003) Research Themes: Wildland Fire Prediction: Developing spatiotemporal models to forecast fire occurrences in British Columbia, aiding fire management strategies. Agro-Environmental Science: Integrating big data from precision agriculture and genomics to optimize sustainable farming practices under climate change. Statistical Bioinformatics: Analyzing microbiome data in livestock and crops to improve disease prevention and crop resilience. Media & Partnerships: Featured in University of Guelph news for wildfire research and Food from Thought initiatives. Seeks industry partnerships in forest fire analytics, environmetrics, and quantitative ecology. Advising & Grants: Supervises graduate students in Statistics and Bioinformatics programs. Offers postdoctoral positions in applied statistics for climate-agriculture linkages and microbiome modeling. Labs & Teams: Hosts a multidisciplinary lab focusing on environmental and agricultural data science. Collaborates with the Canadian Forest Service and agricultural stakeholders.

Owais Gilaniمشاهده پروفایل

دانشیار

• Spatial Statistics
• Spatiotemporal Statistics
• Air Pollution Modeling
• +۴ مورد دیگر

Owais Gilani is an Associate Professor in the Department of Public Health and Community Medicine at Tufts University School of Medicine. His academic journey includes a PhD in Biostatistics from Yale University and postdoctoral training at the University of Michigan School of Public Health. Prior to joining Tufts, he served as Associate Professor of Statistics at Bucknell University. Dr. Gilani's research program centers on developing spatial and spatiotemporal statistical methods with applications in environmental health. Key areas include: Air pollution modeling and exposure assessment Human mobility pattern analysis Bayesian data fusion techniques Environmental epidemiology Statistical software development for environmental data His work involves interdisciplinary collaborations across public health, clinical medicine, and biological sciences. Analysis of Dr. Gilani's recent publications reveals strong methodological focus on spatiotemporal modeling with applications spanning environmental epidemiology, ecological entomology, and health services research. The majority of his work employs Bayesian approaches and develops novel statistical techniques for analyzing complex environmental and health datasets. Recent outputs demonstrate expanding applications in infectious disease modeling and mobility data analysis. Dr. Gilani maintains an active teaching portfolio including: Principles of Epidemiology & Statistics Biostatistics Data Visualization for Public Health Introduction to Public Health Research Statistical modeling and inference courses He currently leads an NSF-funded project on 'Data Resources and Analytic Tools to Understand Population Scale Human Mobility for Applications in SBE Research' (2025).

• Biostatistics
• Clinical Trials
• Statistical Modeling
• +۷ مورد دیگر

Erich J Greene is a Research Scientist in the Department of Biostatistics at the Yale School of Public Health , where he contributes to clinical trial methodology and statistical analysis. Yale Center for Analytical Sciences (YCAS) Yale Data Coordinating Center (YDCC) Education: PhD in Psychology (2003), Princeton University MA in Psychology (1997), Princeton University MS in Physics (1995), Cornell University AB in Physics (1991), Princeton University Research Interests span Biostatistics , Clinical Trial Design , Survival Analysis , and Public Health Data Science . His work focuses on: Competing risks and clustering in time-to-event data Electronic health record data validation Sex/gender disparities in cardiovascular outcomes Pragmatic trial methodologies Bayesian approaches for complex biomedical data Publication Trends reveal expertise in: Cluster-randomized trial statistical methods Fall injury prevention modeling Dementia care comparative effectiveness ICD-10 coding algorithms Non-proportional hazard scenarios Zero-inflated recurrent event modeling Collaborations include frequent partnerships with: James Dziura (9 co-publications) Can Meng (8 co-publications) David Ganz and Denise Esserman (6 each) Additional Activities: Served on the Board of Directors for the New Haven Theater Company (2011-2022).

Aad van der Vaartمشاهده پروفایل

استاد

• High-dimensional statistics
• Nonparametric Bayesian statistics
• Statistical genetics
• +۵ مورد دیگر

Aad van der Vaart is a distinguished Professor of Statistics at Delft University of Technology (since 2021). Previously, he held Full Professorships at Leiden University (2012–2021) and Vrije Universiteit Amsterdam (1996–2012). His research focuses on foundational statistical theory and applications, including high-dimensional statistics, Bayesian methods, inverse problems, and genomics. He has made seminal contributions to nonparametric Bayesian inference, empirical processes, and semiparametric theory. Van der Vaart has authored influential textbooks such as Asymptotic Statistics (1998) and Fundamentals of Nonparametric Bayesian Inference (2017, with S. Ghosal). His work bridges theoretical rigor and practical applications, with over 34,830 citations (Google Scholar, 2023) and an H-index of 60. Key honors include the Spinoza Prize (2015, Netherlands’ highest science award), DeGroot Prize (2020), and membership in the Royal Netherlands Academy of Sciences. His academic journey includes roles such as Miller Fellow at UC Berkeley (2000), visiting positions at leading universities, and leadership in statistical societies. His research group actively explores modern challenges in statistical theory and methodology, including causal inference, adaptive estimation, and large-scale data analysis. Notable grants include an ERC Advanced Grant (2012) for Bayesian inverse problems. Collaborations span academia and industry, emphasizing interdisciplinary impact. While specific lab affiliations are not explicitly stated, his work is rooted in foundational mathematical statistics with broad applicability.

Simon Cameronمشاهده پروفایل

پژوهشگر

• Mass Spectrometry
• Microbiome Research
• Diagnostic Microbiology
• +۳ مورد دیگر

Dr. Simon Cameron is a researcher at the School of Biological Sciences, Queen's University Belfast, within the Faculty of Medicine, Health and Life Sciences. His work bridges microbiology and mass spectrometry with applications in health, disease, and infant nutrition. Key affiliations: School of Biological Sciences, Queen's University Belfast Public outreach: Featured in Channel 4’s Dispatches program 'Breastfeeding Uncovered' and cited in BBC, Daily Mail, and Science magazine Research Focus : Applying mass spectrometry to health and disease, with a primary emphasis on human milk profiling and systems biology approaches to infant microbiome development. Maintains active programs in novel diagnostic microbiology methods to reduce time-to-diagnosis. Scientific Contributions : Recent work explores REIMS for plant infection biomarkers, donor milk preservation, microbiome analysis in extreme environments, and metabolomic stability in extended breastfeeding. His group develops automated preprocessing tools for LC-MS data and investigates microbial-host interactions in diverse contexts. Rapid Evaporative Ionization Mass Spectrometry (REIMS) applications Human milk microbiome and metabolome Diagnostic microbiology innovations Systems biology approaches Scientific Recognition : Recipient of a 2024 Royal Society of Chemistry award for contributions to mass spectrometry methodologies.

Violaine Saint-Andréمشاهده پروفایل

پژوهشگر

• Computational Biology
• Immunology
• Epigenetics
• +۳ مورد دیگر

Violaine Saint-André is a Researcher at the Institut Pasteur's Bioinformatics and Biostatistics Hub, where she leads computational biology research focused on immune response determinants through the Milieu Intérieur project. She holds a PhD in Molecular Biology/Genetics from Université Paris VI and an MSc in Bioinformatics from Université Paris VII. Her research investigates transcriptional regulatory networks, epigenetic mechanisms, and multivariate analysis of immune variability. Key interests include: Gene expression regulation and epigenetic memory of environmental exposures Determinants of human immune response heterogeneity Computational methods for multi-omics data integration Impact of smoking on long-term immune adaptation Her publications demonstrate strong focus on immunology and computational biology, with recent work uncovering smoking's persistent epigenetic effects on immunity and cytokine regulation. Research frequently involves high-dimensional data analysis across tuberculosis, COVID-19, and HIV contexts. She contributes significantly to academic training through courses at Institut Pasteur, Centrale-Supelec, and École Centrale de Nantes, covering ChIP-seq analysis, multivariate statistics, and health applications of AI. Actively participates in conference organization and student evaluation committees.

• Relational Machine Learning
• Deep Learning
• Complex Network Science
• +۴ مورد دیگر

Dr. Rebekka Burkholz is a tenured faculty member at the CISPA Helmholtz Center for Information Security in Saarbrücken, Germany, leading the Relational Machine Learning Group . Her research bridges machine learning and complex network science to develop robust, data-efficient models with applications in molecular biology. Previously, she held positions at Harvard T.H. Chan School of Public Health and ETH Zurich. PhD in Systems Design (2016) from ETH Risk Center Mathematics and Physics BSc/MSc from TU Darmstadt Her work focuses on sparse training methods and theoretical deep learning , addressing challenges like computational efficiency and adversarial robustness. Recent publications explore: Sparse training via implicit sparsification GNN optimization through rescaling and rewiring Integration of domain knowledge in biomedical modeling Theoretical guarantees for batch normalization and lottery tickets Scientific awards include: Zurich Dissertation Prize (2016) CSF Best Contribution Award (2016) She actively advises PhD students and collaborates with interdisciplinary teams in biostatistics and systems biology.