جست‌وجوی استاد و پژوهشگر

Jingjing Yangمشاهده پروفایل

دانشیار

• Genetic Epidemiology
• Longitudinal Analysis
• Bayesian Analysis
• +۵ مورد دیگر

Jingjing Yang is an Associate Professor in the Department of Human Genetics at Emory University School of Medicine and holds a secondary appointment in the Department of Biostatistics and Bioinformatics at the School of Public Health. Her research focuses on developing statistical and computational methods to integrate multi-omics data for studying complex diseases such as Alzheimer’s, Parkinson’s, and mobility disability. She utilizes Bayesian methods, machine learning, and high-performance computing to analyze genomic and biomedical data. Dr. Yang’s lab develops tools like BFGWAS_QUANT for functional GWAS and TWAS analyses. Education : Ph.D., Rice University (2009–2014); M.S., Clemson University; B.S., Jilin University. Research Interests : Genetic epidemiology, integrative omics analysis, transcriptome-wide association studies (TWAS), Alzheimer’s disease pathogenesis, and computational genomics. Her lab leverages programming languages like R, Python, and C++ with Linux/Unix clusters and AWS. Key Awards : Robert C. Elston Best Paper Award (2019) 3rd Place Presentation at The 61st McKusick Short Course (2018) Reviewers’ Choice Poster Award at ASHG Annual Meeting 2020 Advising & Grants : Supervises students in Emory’s GMB/PBEE and Biostatistics programs. Her lab actively recruits research assistants and has openings for postdoctoral fellows. Research is supported by NIH grants focusing on Alzheimer’s and genomic data integration. Labs/Teams : The Yang Lab at Emory (https://yanglab-emory.github.io/) focuses on statistical genomics, with ongoing projects in cell-cell communication analysis (STACCato) and risk prediction tools (FDrisk).

Martin Hirschiمشاهده پروفایل

مدرس

• Water Cycle Processes
• Climate Change Impacts
• Soil Moisture Analysis
• +۳ مورد دیگر

Dr. Martin Hirschi is a Lecturer at the Department of Environmental Systems Science (D-USYS) at ETH Zurich, Switzerland, and holds a position in the Land-Climate Dynamics group at the Institute for Atmospheric and Climate Science (IAC). He has been affiliated with ETH Zurich since 2003, holding roles including Scientific Programmer/Data Manager and Postdoc. His research focuses on water cycle processes, climate change impacts, and soil moisture dynamics, with contributions to projects like ESA CCI Soil Moisture and the SwissSMEX soil moisture network. He leads courses on climatological field work and meteorological measurement techniques. Education: Diploma in Environmental Sciences (ETH Zurich, 2002), Postgraduate Course in Applied Statistics (2009), and extensive postdoctoral training. Key projects include analyzing drought properties in the Mediterranean under climate change, developing gap-filled soil moisture datasets, and investigating climate extremes using satellite and reanalysis data. His research integrates remote sensing, field measurements, and climate models to address hydrological and climate questions. Notable contributions include studies on soil moisture-induced feedbacks in land carbon sinks and detecting anthropogenic fingerprints in extreme drought events. Collaborations span institutions like MeteoSwiss and the Swiss Federal Office for Meteorology. Labs/Teams: Active in the Land-Climate Dynamics group under Prof. Sonia Seneviratne, managing data for projects like Rietholzbach catchment studies and ESA CCI SM. His work supports climate adaptation strategies and improved understanding of land-atmosphere interactions.

Jonathan Helmمشاهده پروفایل

دانشیار

• Quantitative Psychology
• Longitudinal Data Analysis
• Statistical Modeling
• +۴ مورد دیگر

Jonathan Helm is an Associate Professor of Psychology at the Department of Psychology, San Diego State University (SDSU), within the College of Sciences. He holds a PhD in Quantitative Psychology from the University of California (2013), an MA in Quantitative Psychology (2010), and a BS in Psychology with a focus on Mathematics (2008). His research focuses on developing and refining statistical models to measure psychological constructs and analyze longitudinal data, with particular emphasis on structural equation modeling (SEM), multitrait-multimethod analyses, and methodological advancements in behavioral and social sciences. Key research areas include examining physiological synchrony in dyads, analyzing interactions in longitudinal studies, and applying SEM to complex experimental designs. Recent work addresses topics such as resilience factors in adolescent mental health, cognitive dysfunction in brain tumor patients, and the impact of prenatal exposures. Helm has advised numerous graduate students in quantitative methods and clinical research, contributing to over 30 academic publications since 2012. He has secured significant grants, including a $6.3M NIH-funded initiative to accelerate evidence-based practices in children’s mental health and a $1.8M study on prenatal alcohol exposure’s cognitive effects. His collaborative projects span neuroscience, public health, and LGBTQ+ health disparities, with a focus on translational research and statistical innovation.

Ping Wangمشاهده پروفایل

استاد

• Supply Chain Management
• Structural Equation Modeling
• Multivariate Analysis
• +۳ مورد دیگر

Ping Wang is a Professor in the Department of Computer Information Systems and Business Analytics at James Madison University's College of Business. He holds a Ph.D. in Management Science and Information Technology from the University of Georgia, an MBA from Appalachian State University, and a B.S. in Mechanical Engineering from Northeastern University in China. His research interests span supply chain management, structural equation modeling, pedagogy, and international education. His work often bridges quantitative methods with real-world applications in business analytics and global contexts. Key research contributions include studies on agro-food supply chains, pandemic response strategies, and cross-cultural education dynamics. He has published widely in journals like International Journal of Production Economics and Decision Sciences Journal of Innovative Education. His teaching innovations earned awards such as the Kenneth R. Bartee Endowed Award for Teaching Innovation (2006) and the Provost Award for Excellence in International Education (2006). Education: Ph.D., Management Science & IT, University of Georgia (1992) MBA, Appalachian State University (1996) B.S., Mechanical Engineering, Northeastern University (1982) Dr. Wang’s recent work addresses pandemic-related challenges, including health behavior changes during lockdowns and resilience in human systems. His research also explores predictive analytics in criminal justice and decision-making under uncertainty. He actively contributes to special journal issues and has advised on projects involving international engineering procurement practices and technology innovation metrics. Awards: 2019: Third Place Teaching & Learning Scholarship Award 2018: First Place Applied Scholarship Publication Award 2006: Bartee Teaching Innovation Award & Provost International Education Award His academic service includes editorial roles in decision science journals and collaborations with international scholars. Current research trends focus on sustainable technology innovation in China and comparative analyses of education systems between the US and China.

Megan Othusمشاهده پروفایل

استاد

• Survival Analysis
• Cure Modeling
• Change-Point Problems
• +۳ مورد دیگر

Megan Othus is a Professor in the Biostatistics Program at the Fred Hutchinson Cancer Research Center and an Affiliate Professor in the Department of Biostatistics at the University of Washington. She is also a member of the Public Health Sciences Division and the Translational Data Science Integrated Research Center (TDS IRC) at Fred Hutch. Her research focuses on refining clinical trial data analysis, particularly in cancer studies, including melanoma and leukemia trials. She develops statistical methods for correlated survival data and evaluates cost-effectiveness in cancer treatments. Education: PhD in Biostatistics, Harvard University (2009) MA in Biostatistics, Harvard University (2006) BA in Mathematics, Reed College (2004) Research Interests: Survival analysis, cure modeling, longitudinal data analysis, and statistical methods for biomedical research. Her work emphasizes improving clinical trial design and analyzing complex datasets, such as those from population-based studies and multivariate clinical trials. Affiliations: Fred Hutchinson Cancer Research Center, University of Washington, and the Seattle StatGROWS program. She collaborates widely on cancer research and cost-effectiveness analyses.

Carolyn A Bergstromمشاهده پروفایل

استاد

• Marine Biology
• Evolutionary Ecology
• Ecological Interactions
• +۳ مورد دیگر

Carolyn A Bergstrom is a Professor of Marine Biology in the School of Arts and Sciences at the University of Alaska Southeast. Her research focuses on evolutionary ecology, morphometrics, and ecological interactions in marine species. B.S. (1995) from the University of Arizona Ph.D. (2002) from the University of Victoria Her work investigates: • Evolutionary mechanisms maintaining phenotypic variation in marine species • Ecofunctional implications of bilateral asymmetry in flatfish • Impact of glacial melt-water on estuarine fish communities Her research integrates field experiments, behavioral observations, stable isotope analyses, and multivariate statistics. A key project examines the evolution of asymmetry in starry flounders, revealing ecological segregation between left- and right-eyed morphs. Another collaborative study funded by EPSCoR Alaska and Alaska Sea Grant explores glacial estuary fish community dynamics. Scientific awards include: Alberta Ingenuity Postdoctoral Fellowship (2003-2007) International Polar Year Postdoctoral Fellowship (2008-2009)

Tao Wangمشاهده پروفایل

دانشیار

• Statistical Genetics
• Genetic Epidemiology
• Biostatistics
• +۴ مورد دیگر

Tao Wang, PhD, serves as an Associate Professor in the Department of Biostatistics at the Medical College of Wisconsin, specializing in statistical genetics and transplantation outcomes research. He holds leadership roles as Statistical Director for both the Immunobiology Working Committee (IBWC) and Graft-versus-host Disease (GVHD) Working Committee at the Center for International Blood and Marrow Transplant Research (CIBMTR). His educational background includes: PhD in Statistics, North Carolina State University (2001) PhD in Mathematics, Southeast University, Nanjing (1993) MS in Mathematics, Southeast University, Nanjing (1990) BS in Mathematics, Peking University, Beijing (1983) Dr. Wang's research centers on statistical genetics methodology , including quantitative trait loci (QTL) modeling, haplotype analysis of SNPs, and mixed models for genetic association studies. His collaborative work spans transplantation biostatistics , focusing on survival analysis for bone marrow transplantation, genomic data analysis (methylation, proteomics), and medical data interpretation for conditions like diabetes and hypertension. He integrates advanced statistical techniques with clinical research to address complex questions in hematopoietic cell transplantation. Analysis of his 2024-2025 publications reveals dominant themes in donor selection factors (age, HLA matching), graft-versus-host disease mechanisms, and health disparities related to race, ethnicity, and socioeconomic status. His work increasingly incorporates machine learning approaches for predictive modeling while maintaining rigorous biostatistical foundations in genetic epidemiology and transplantation outcomes. No scientific awards were documented in the source material. Dr. Wang's advisory activities and grant funding details are not specified in available records, though his extensive CIBMTR committee leadership indicates significant collaborative engagement. His research directly impacts donor selection protocols and outcome prediction models in hematopoietic cell transplantation through the CIBMTR network. He operates within the Center for International Blood and Marrow Transplant Research infrastructure, contributing statistical expertise to multi-institutional teams focused on improving transplantation outcomes through genetic and epidemiological analyses.

Anika Buchholzمشاهده پروفایل

پژوهشگر ارشد

• Medical Biometry
• Epidemiology
• Cardiology
• +۳ مورد دیگر

Anika Buchholz is a Research Fellow at the Institute of Medical Biometry and Epidemiology, part of the Faculty of Medicine at the University of Hamburg (Universitätsklinikum Eppendorf). Her work focuses on medical biometry, epidemiology, and statistical methodology applied to clinical research, particularly in cardiology, oncology, and healthcare outcomes. She holds a 2022 certification in medical biometrics from the German Society for Medical Informatics, Biometrics, and Epidemiology (GMDS) and the International Biometric Society, German Region (IBS-DR). Key Research Areas: Biostatistics, clinical trial design, survival analysis, and risk factor modeling. Notable Projects: Contributions to the German Atrial Fibrillation Network (AFNET) registry, NORDIC ICD trial analyses, and oncology interventions (e.g., BENITA trial for ovarian cancer patients). Her research spans drug efficacy evaluations (e.g., proton-pump inhibitors in liver cirrhosis), device performance (ICD shock efficacy), and interdisciplinary studies in neurology (aneurysm recurrence) and psychiatry (auditory hallucination therapies). She has authored over 20 peer-reviewed articles since 2011, addressing methodological challenges in clustered data analysis and competing risks frameworks. Awards: Zertifikat „Biometrie in der Medizin“ (2022). Collaborations involve multicenter trials, including work with the European Liquid Biopsy Society (ELBS) and institutions such as Charité Berlin and the University Heart Center Hamburg.

• Multidimensional Scaling
• Nonlinear Optimization
• Multivariate Analyses
• +۶ مورد دیگر

Assoc. Prof. Dr. Ulaş Akküçük is affiliated with Boğaziçi University, where he teaches courses in Linear Programming, Probability and Statistics, Multivariate Analysis, Marketing Research, and Production and Operations Management. His research focuses on Multidimensional Scaling, Nonlinear Optimization applications, and Network Models in Marketing. He holds a Ph.D. from Rutgers University. Education: Ph.D. from Rutgers University His research interests include advanced statistical methodologies and their applications in marketing and operations management. He has contributed to fields such as clustering algorithms, tree-fitting models, and nonlinear dimensionality reduction techniques. His work bridges theoretical optimization with practical business analytics. No recent articles or grants are listed in the provided data. Contact him via email or visit his faculty page for further information.

• Economic Growth
• Health Economics
• Macroeconomics
• +۳ مورد دیگر

Giovanni Trovato is a Professor of Economic Statistics at the University of Rome Tor Vergata. His research focuses on economic growth, health economics, macroeconomics, and semiparametric models. He has published extensively on topics such as the impact of organized crime on economic development, macroprudential policies, and healthcare system efficiency. Trovato’s work often combines theoretical frameworks with empirical analyses, addressing issues like fiscal consolidation effects, human capital accumulation, and institutional quality measurement. His recent studies include investigations into the relationship between taxation and growth, life expectancy disparities in Europe, and the role of health amenable deaths in social value assessments. He has contributed to understanding corruption’s economic consequences and the dynamics of credit volatility. His research has been disseminated through leading journals and conferences, reflecting his interdisciplinary approach to economics and statistics. Education details are not explicitly listed, but his academic career suggests advanced training in econometrics and economic theory. His articles span from 2001 to 2025, demonstrating sustained contributions to multiple subfields. Notable works include Organized Crime, Corruption, and Economic Growth (2025) and Human capital-based growth with depopulation and class-size effects (2024). Trovato’s work emphasizes empirical rigor and policy relevance, often employing innovative statistical methodologies like finite mixture models and frontier analysis.

• Environmental Science
• Climate Science
• Biostatistics
• +۳ مورد دیگر

Jessica Matthews is an Adjunct Professor at North Carolina State University. Her research focuses on environmental science, climate modeling, public health, and remote sensing. She specializes in spatial analysis of greenspace impacts on mental health, climate data interpretation, and uncertainty quantification in Earth observation systems. Her work spans interdisciplinary topics including Arctic sea ice dynamics, meteorological influences on disease transmission (e.g., COVID-19), and applications of Bayesian statistics in satellite calibration. Notable contributions include studies on racial disparities in pregnancy complications linked to greenspace access and long-term analyses of atmospheric temperature profiles. Matthews has published extensively on topics such as intersatellite calibration, growing season determination using temperature metrics, and the sensitivity of climate models to observational data thresholds. Her research bridges statistical methodologies with real-world environmental and health challenges, emphasizing practical solutions through advanced data analysis techniques.

Justin Petrovichمشاهده پروفایل

استادیار

• Applied Statistics
• Functional and Longitudinal Data Analysis
• Multiple Imputation
• +۳ مورد دیگر

Dr. Justin Petrovich is an Assistant Professor of Statistics and Business Analytics at Saint Vincent College, where he also serves as Chair of the Marketing, Analytics, and Global Commerce Department since 2022. He holds a B.S. in Finance and Mathematics from Saint Vincent College (2014) and a Ph.D. in Statistics from Pennsylvania State University (2018). His research focuses on functional and longitudinal data analysis, multiple imputation, and statistics education, with applications in mental health, pediatrics, marketing, and economics. Education: B.S., Saint Vincent College (2014); Ph.D., Pennsylvania State University (2018) Teaching: Courses include Business Statistics, Econometrics, and Data Science. His research interests emphasize applied statistical methodologies, particularly in handling complex data structures such as longitudinal and functional datasets. He has collaborated across disciplines, including health sciences and economics, and has contributed to studies on suicide risk prediction in college students and labor market analysis. Dr. Petrovich has published in high-impact journals like the Journal of the Royal Statistical Society and Journal of Counseling Psychology. His work bridges theoretical statistical advancements with practical applications, aiming to make complex analyses accessible to applied scientists. Outside academia, he is an active member of Saint Vincent Basilica Parish and maintains ties to his alma mater through teaching and community involvement. He resides in Latrobe with his family and remains an avid runner.

Nancy Dennisمشاهده پروفایل

استاد

• Cognitive Aging
• Neuroimaging (fMRI)
• Episodic Memory
• +۵ مورد دیگر

Nancy Dennis is a Professor of Psychology and Associate Department Head for Instruction at Pennsylvania State University's Department of Psychology. She also serves as Director of Undergraduate Studies. Her research focuses on cognitive aging, particularly the effects of aging on episodic memory using behavioral and neuroimaging methods (fMRI). Key interests include false memories, associative memory processes, neural compensation mechanisms, and the application of multivariate analysis techniques such as representational similarity analyses and functional connectivity studies. Dr. Dennis earned her Ph.D. from The Catholic University of America in 2004. She leads the Cognitive Aging & Neuroimaging Lab (CANLab), which explores how cognitive and neural processes change with age. Her work integrates advanced neuroimaging approaches to understand memory specificity, neural markers of cognitive decline, and interventions to mitigate memory deficits in older adults. Her research has been published in high-impact journals such as Neuropsychologia , Neurobiology of Aging , and Journal of Neuroscience . Current projects include investigating neurovascular coupling in metabolic syndrome, enhancing creative thinking in older adults, and developing interventions to improve everyday functioning through memory training. She maintains an active role in advancing methodological approaches in cognitive neuroscience, particularly in multivariate pattern analysis and connectome studies.

Arpana Agrawalمشاهده پروفایل

استاد

• Genetic Epidemiology
• Psychiatric Genetics
• Addiction Science
• +۷ مورد دیگر

Arpana Agrawal is the James and Juanita Wittmer Professor and Professor of Psychiatry at Washington University in St. Louis. She is also a Lecturer in Psychology and affiliated with the Roy and Diana Vagelos Division of Biology & Biomedical Sciences (DBBS), the Department of Psychological & Brain Sciences, and the Institute of Clinical and Translational Sciences (ICTS). Her work focuses on the interplay of genetic and environmental factors in addictions, with a lab dedicated to advancing this research. Specializes in Human and Statistical Genetics Active in Addiction Science, Prevention, and Treatment Research Recent research explores genetic risk markers for C-reactive protein, prenatal caffeine impacts, and suicide attempts in alcohol use disorders. Her publications span high-impact journals in neuroscience, psychiatry, and developmental biology. Scientific Awards : James and Juanita Wittmer Professor She mentors PhD/MSTP students and collaborates on translational studies, including the Adolescent Brain Cognitive Development (ABCD) Study and multivariate genomic analyses of addiction traits.

• Biostatistics
• Pregnancy Complications
• Mathematical Modelling
• +۷ مورد دیگر

Dr Shalem Leemaqz is an Adjunct Researcher in the College of Medicine and Public Health at Flinders University, where he contributes to the Pregnancy Health and Beyond (PHaB) Lab. He is also a Statistician at the South Australian Health and Medical Research Institute (SAHMRI), holding a PhD from the University of Adelaide and advanced degrees in statistics and engineering. PhD, Predicting Risk for Pregnancy Complications, University of Adelaide (2010–2015) Master of Applied Statistics, Macquarie University (2009) Bachelor of Science, Mathematics/Statistics & Electronic Systems Engineering, University of South Australia (2006–2008) His research focuses on biostatistics and mathematical modelling of complex health data, particularly in pregnancy complications and maternal-fetal health. He applies advanced statistical and machine learning techniques to high-dimensional datasets, integrating clinical and genomic information. His work spans predictive modelling for preeclampsia, preterm birth, gestational diabetes, and fetal growth restriction. More recently, his research extends into transgender health and cancer epidemiology, particularly Indigenous health disparities. His recent publications (2023–2025) reflect a strong trend in applying rigorous statistical and machine learning methods to clinical problems, especially in endocrinology and obstetrics. Key themes include risk prediction models, hormone therapy safety, cancer survival analysis, and data integration from omics and electronic health records. His work often involves secondary analyses of randomized trials and large cohort studies. Dr Leemaqz is committed to advancing statistical rigour in medical research and collaborates widely with clinicians and bioinformaticians. His contributions support multiple UN Sustainable Development Goals, particularly those related to good health and well-being. He has not received any specific scientific awards mentioned in the provided text. Dr Leemaqz advises no known students but actively contributes to research teams and collaborative projects. He does not hold any named grants in the information provided but is involved in externally funded research through SAHMRI and Flinders University. His technical expertise includes R programming, machine learning, and high-dimensional data analysis. He is a member of the PHaB Lab at Flinders University and contributes to research teams at SAHMRI, focusing on biostatistical support for large-scale health studies.