جست‌وجوی استاد و پژوهشگر

• Biostatistics
• Clinical Trial Design
• Bayesian Hierarchical Modeling
• +۳ مورد دیگر

Dr. Song Zhang is an Assistant Professor at the University of Texas Southwestern Medical Center, Department of Clinical Sciences. He serves as Director of BERD (Biostatistics, Epidemiology, and Research Design) for the UTSW CTSA program and is a member of the NCI Central Institutional Review Board (Adult CIRB – Early Phase Emphasis). His research focuses on statistical methodology, particularly Bayesian hierarchical modeling and clinical trial design, addressing challenges like clustered data, missing values, and cost constraints. He has authored a book on sample size calculations for clustered and longitudinal outcomes and secured grants as PI from NIH, NSF, and PCORI. His work spans disease mapping, joint modeling of longitudinal/survival outcomes, grant review methodologies, and pathway analysis. He has extensively published on clinical trial design innovations, including stepped-wedge cluster trials and pragmatic trial methodologies. His research bridges statistical rigor with practical healthcare applications, emphasizing efficient trial design and evidence-based decision-making.

Cathy Ningمشاهده پروفایل

استاد

• Financial Econometrics
• Empirical Finance
• Econometrics

Cathy Ning serves as Professor in the Department of Economics at Toronto Metropolitan University, specializing in quantitative financial analysis and econometric modeling. Her educational foundation includes: Ph.D. from University of Western Ontario Professor Ning's research concentrates on Financial Econometrics, Empirical Finance, and advanced Econometrics methodologies. She investigates complex market dependencies, volatility dynamics, and risk transmission mechanisms across global financial instruments. Her work bridges theoretical econometric frameworks with practical financial applications, particularly in extreme market conditions. Publication analysis (2008-2024) reveals consistent innovation in copula modeling and regime-switching techniques applied to oil-exchange rate interactions, stock-bond correlations, and IPO market cycles. Key thematic evolution shows progression from foundational dependence structures (2008-2010) to contemporary risk spillover quantification (2021-2024), with persistent focus on asymmetric volatility and extreme event modeling. Research funding includes: SSHRC Standard Research Grant (2011-2013): $21,805 for 'Dependence in financial markets' SSHRC Research Workshops Grant (2011-2013): $20,727 for 'The econometrics of socioeconomic interactions'

Bo Friis Nielsenمشاهده پروفایل

استاد

• Queueing Theory
• Matrix Analytic Methods
• Stochastic Processes
• +۳ مورد دیگر

Bo Friis Nielsen is a Professor and Head of Section in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU). His research focuses on queueing theory, particularly matrix-analytic methods, with applications in telecommunication systems and service optimization. He currently supervises PhD students working on projects with Danske Bank (call center efficiency) and DSB S-tog (passenger counting for transport planning). His teaching includes probability theory and stochastic simulation. Education/Background: Formal academic qualifications not explicitly listed in the text. Affiliations: Head of the Mathematical Modelling Section since 2003, affiliated with DTU Compute. Research interests span theoretical advancements in queueing models and practical applications in transportation, healthcare, and industrial systems. He has collaborated with companies like Danske Bank and DSB S-tog, addressing real-world operational challenges. Recent work includes predictive maintenance strategies for multi-component systems and railway defect analysis. Publications emphasize stochastic modeling, maintenance optimization, and transportation analytics. Projects involve developing tools for public transport efficiency and reliability engineering in wind turbines and power plants.

Umashanger Thayasivamمشاهده پروفایل

استاد

• Data Mining
• Statistical Learning
• Robust Estimation
• +۶ مورد دیگر

Umashanger Thayasivam holds the position of Professor and Department Head in the Department of Mathematics at Rowan University's College of Science & Mathematics. He earned his Ph.D. in Statistics from the University of Georgia (2009), following an M.S. (2005) and B.S.c (Honors) from the University of Colombo (2001). His research focuses on data mining, robust statistical methods, and Bayesian approaches, with applications in biomarker discovery for neurodegenerative diseases, speaker verification in biometrics, and telemedicine analytics. He has received awards including the 2017 Best Paper Award for work on robust clustering. Key projects include biomarker studies for Alzheimer’s and Multiple Sclerosis, peak detection via maximum entropy, and robust methods for multivariate data analysis. Professional memberships include the American Statistical Association and the Institute of Mathematical Statistics. Grants include collaborations with Bristol-Myers Squibb for statistical tools in metabolomics analysis. His lab focuses on interdisciplinary applications of statistical learning.

Christopher Quinnمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Data Science
• +۳ مورد دیگر

Christopher Quinn is an Assistant Professor at Iowa State University, specializing in Artificial Intelligence, Machine Learning, and Data Science. He holds a B.S. in Engineering Physics from Cornell University (2008), an M.S. in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign (2010), and a Ph.D. in Electrical and Computer Engineering from UIUC (2014). His research focuses on optimization algorithms, neural networks, and information theory, with applications in neuroscience and distributed systems. He has contributed to advancements in submodular optimization, bandit algorithms, and federated learning. Quinn’s recent work explores topics such as combinatorial bandits, stochastic processes, and causal inference in neural data analysis. His publications highlight expertise in machine learning theory, including studies on gradient methods for online optimization, redundancy analysis in code-trained models, and unsupervised change-point detection in time series. He has also investigated neural mechanisms underlying visual experience, such as theta oscillation synchronization in the visual cortex of Fmr1 KO mice. Quinn’s work often bridges theoretical foundations with practical applications, such as federated learning frameworks and robust directed tree approximations of stochastic networks. Christopher Quinn’s research has been supported by grants including the NSF CIF: Small grant for sequential decision-making under uncertainty (2022). He actively publishes in top-tier venues and collaborates on interdisciplinary projects at the intersection of AI and neuroscience. His address is 113 Atanasoff Hall, Ames, Iowa 50010, and he can be reached at cjquinn@iastate.edu.

Elizabeth Stojanovskiمشاهده پروفایل

مدرس ارشد

• Biostatistics
• Longitudinal Modelling
• Multivariate Statistics
• +۶ مورد دیگر

Dr. Elizabeth Stojanovski is a Senior Lecturer at the University of Newcastle , affiliated with the School of Information and Physical Sciences and the Priority Research Centre for Computer-Assisted Research Mathematics and its Applications (CARMA) . She leads the Mathematics Education Research Group and manages the statistical consulting unit, serving industry, academics, and postgraduate students. With a PhD in Statistics from the University of Newcastle, she specializes in Biostatistics , Longitudinal Modelling , and Multivariate Statistics . Education : PhD (University of Newcastle), Bachelor of Mathematics (Honours), Bachelor of Mathematics Professional Roles : Senior Lecturer (2007–present), Research Academic (2004–2006) Memberships : Member of the Australian Statistics Society Her research spans health and education , focusing on longitudinal relationships between life events and physical/mental health. She has developed Bayesian frameworks , longitudinal models , and meta-analytic techniques for applications in tobacco control, cardiovascular disease, cancer treatment, and mathematics education. Her work includes collaborations with Eric Beh on Nobel Prize data analysis, Manohar Garg on lipid-lowering studies, and Suzanne Snodgrass on physiotherapy biomechanics. Recent publications highlight her expertise in correspondence analysis for educational and scientific data, multivariate assessment of diabetes risk , and reinforcement learning for optimization. She has supervised 15 PhD/Masters students and peer-reviewed for journals and grants. Her grants total $496,207 , including projects on statistical literacy and phytosterol efficacy. Scientific Awards : 2001 "Temporal association between life events and health" Elizabeth's teaching spans theoretical and applied statistics, with a focus on psychology, health, and business applications. She has also contributed to forensic science through cut mark analysis on bone and policy research on tobacco investments in pension funds. Her leadership in CARMA and statistical consulting underscores her interdisciplinary impact.

George Tzougasمشاهده پروفایل

دانشیار

• Actuarial Science
• Applied Statistics
• Computational Statistics
• +۵ مورد دیگر

George Tzougas is an Associate Professor in the Department of Actuarial Mathematics & Statistics at Heriot-Watt University, Edinburgh. He serves as Academic Director of the Scottish Financial Risk Academy and Academic Chair of the IFoA’s Data Science Research working party on Statistical Learning in Actuarial Applications. Previously, he held a tenured position at the London School of Economics (LSE), where he was Programme Director for the BSc in Actuarial Science and a member of the Royal Statistical Society. His research focuses on applying statistical learning and computational methods to non-life insurance, finance, and climate change impact analysis. Key areas include insurance ratemaking, optimal reinsurance, green bond pricing, and climate-related risk modeling. He has published extensively on topics such as multivariate claim count models, machine learning for carbon price forecasting, and zero-inflated regression techniques. Education: Tenured position at LSE Department of Statistics (previously Programme Director for BSc Actuarial Science) Lecturer at University College Cork, Ireland Notable awards include multiple Excellence in Education Awards from LSE (2017–2020) and the Best Class Teacher Award (2017). His work contributes to UN Sustainable Development Goals related to climate action and sustainable finance. He advises on PhD projects involving climate change hazards in insurance and has taught courses such as Time Series and Machine Learning. Research collaborations span institutions globally, with a focus on interdisciplinary applications of actuarial science and data science.

William Weimin Yooمشاهده پروفایل

استادیار

• Uncertainty Quantification
• Statistical Computing
• Deep Learning
• +۳ مورد دیگر

William Weimin Yoo is an Assistant Professor at the School of Mathematical & Computer Sciences, Heriot-Watt University. His research focuses on Bayesian nonparametrics, uncertainty quantification, statistical computing, deep learning, and dark matter physics. He has worked internationally across the UK, Netherlands, France, US, and Canada. He teaches courses including F21BD: Big Data Management, F70TS: Time Series and Machine Learning, and F90AM: Advanced Machine Learning. Yoo is actively supervising PhD students in topics such as uncertainty quantification for clustering and accelerated posterior contraction rates. He emphasizes interdisciplinary research, combining statistical theory with applications in physics and sports analytics. His work bridges computational methods and theoretical guarantees in Bayesian frameworks. His academic contributions span methodological advancements in posterior contraction, credible sets, and spatio-temporal modeling. He frequently engages in collaborative research and peer review across top journals and conferences in statistics and machine learning.

John Hughesمشاهده پروفایل

دانشیار

• Biostatistics
• Spatial Statistics
• Bayesian Methods
• +۴ مورد دیگر

John Hughes is an Associate Professor and Chair of the Department of Biostatistics and Health Data Science at Lehigh University's College of Health. He holds a PhD in Statistics from Penn State University and has over 29 years of experience in academia, with previous appointments at institutions like the University of Minnesota and the University of Colorado. His research focuses on methodological advancements in statistical modeling for dependent data, Bayesian methods, and statistical computing. His interdisciplinary work spans environmental health, bioimaging, magnetic resonance safety, and vaccine hesitancy. He has developed numerous software packages for R and Perl, including copCAR and batchmeans. Education: PhD in Statistics, Penn State University MS in Statistics, Penn State University MS in Applied Computer Science, Frostburg State University BS in Mathematics and Computer Science, Frostburg State University Teaching: Courses include Advanced R Programming, Biostatistics, Population Health Data Science, and Computational methods. His research interests emphasize spatial and spatiotemporal data analysis, with applications to public health and medical imaging. He has consulted for organizations such as the Minnesota Center for Chemical and Mental Health and Temple University. His software contributions include packages like krippendorffsalpha for agreement measurement and copCAR for spatial regression modeling. Recent work focuses on improving statistical inference methods, analyzing vaccination refusal patterns, and developing frameworks for copula-based agreement coefficients. His articles highlight innovations in Bayesian computation, spatial epidemiology, and nonparametric statistics. Dr. Hughes leads academic initiatives in biostatistics and health data science, fostering interdisciplinary collaborations and advancing statistical methodologies for real-world applications.

Julien Chiquetمشاهده پروفایل

مدرس پاره‌وقت

• Statistics
• Machine Learning
• Sparse Methods
• +۱۰ مورد دیگر

Julien Chiquet is a researcher and part-time lecturer at École Polytechnique (2020-2024), and the head of the UMR MIA Paris-Saclay research unit. He is affiliated with AgroParisTech on the Paris-Saclay campus and leads the SOLsTIS team. He serves as the chief editor of the journal Computo , focusing on computational statistics and data science. Education: Habilitation (2015) Research Interests: His work bridges statistical methodology and life sciences, with a focus on developing and applying advanced statistical methods such as sparse models, multivariate analysis, and optimization techniques. Key areas include dimension reduction in high-dimensional count data, probabilistic graphical models, and applications in ecology, genomics, and metagenomics. He emphasizes reproducible research practices, recently developing a Quarto template for HCERES reports integrating LaTeX and Pandoc. Recent Contributions: Recent work includes advancements in the Poisson lognormal model, stability selection via the sharp R package, and Python tools like pyPLNmodels for analyzing multivariate count data. His research often addresses challenges in ecological network inference, scRNA-seq analysis, and genomic prediction. Labs & Teams: Based at UMR MIA Paris-Saclay, he contributes to the SOLsTIS team, advancing computational methods for biological data analysis. The lab relocated to the new Palaiseau campus in 2022, fostering interdisciplinary collaborations.

Xiaojing Dongمشاهده پروفایل

دانشیار

• Quantitative Marketing
• Econometrics
• Bayesian Statistics
• +۳ مورد دیگر

Xiaojing Dong is an Associate Professor of Marketing and Business Analytics at the Leavey School of Business, Santa Clara University. She directs the MS in Marketing program and teaches core courses like 'Principles of Marketing' and electives such as 'Marketing Analytics.' Her research focuses on understanding customer responses to marketing strategies, leveraging quantitative methods like econometrics and Bayesian statistics, with applications in pharmaceutical, high-tech, and retail industries. Dr. Dong holds a PhD from Northwestern University (Alden G. Clayton Award recipient), an MS from MIT, and a BS from Tsinghua University. She serves as a reviewer for Marketing Science and International Journal of Research in Marketing . Her research explores preference heterogeneity, user-generated content motivation, and satisfaction spillovers. Notable work includes analyzing bid-pulsing in search engines and physicians' sample-dispensing decisions. She has contributed to over 20 peer-reviewed articles and book chapters, emphasizing empirical rigor and practical insights. Awards: Alden G. Clayton Doctoral Dissertation Proposal Award (2014). Teaching and Grants: Develops data-driven curricula and collaborates on projects involving marketing analytics and consumer behavior modeling. Labs/Teams: Engaged with the AIMES Program and interdisciplinary research in marketing science.

• Biostatistics
• Public Health
• Nutrition Science
• +۴ مورد دیگر

Jaakko Nevalainen is a Professor of Biostatistics and Head of the Health Sciences Unit at the University of Tampere , Faculty of Social Sciences. His work integrates computational methods and statistical theory to address health-related research questions in nutrition, diabetes, prostate cancer, and cardiovascular disease. He leads the LoCard research consortium, developing computational methods to study food consumption and health sustainability. His research has received significant funding from the Academy of Finland and Horizon Europe, resulting in over 160 peer-reviewed publications in top journals like Statistics in Medicine , Diabetologia , and JAMA . Key research areas include biostatistical method development, health economics, and applied epidemiology. His teams collaborate with institutions like the University of Helsinki and participate in population studies such as ProScreen (prostate cancer screening) and DIPP (type 1 diabetes). Major projects focus on food purchasing patterns, obesity-related health outcomes, and sustainable dietary transitions. He has secured major grants including Academy of Finland funding for the ProScreen trial and EU Horizon grants for LoCard. His interdisciplinary approach bridges statistical innovation with real-world health challenges, emphasizing translational research and data-driven policy recommendations.

Anne-Béatrice Dufourمشاهده پروفایل

پژوهشگر

• Ecological Statistics
• Paleontology
• Multivariate Analysis
• +۳ مورد دیگر

Anne-Béatrice Dufour is a prominent researcher in ecological statistics and paleontology at Université Claude Bernard Lyon 1. Her work focuses on multivariate analysis methods, particularly through the development of the ade4 software package, which is widely used in ecological data analysis. She has contributed to studies in paleontology (e.g., conodont morphology, Devonian climate), forensic science (drug analysis, DNA mixtures), and biodiversity conservation (human-wildlife interactions). Key roles: Leading author on ecological statistical methodologies Co-developer of the ade4 R package Expert in conodont paleontology and morphometrics Education contributions: Advocacy for statistical education in secondary curricula (Baccalauréat 2021) Teaching resources for biostatistics in French universities Her research spans disciplines including ecology, genetics, and forensic science, with over 60 publications since 2003. Recent work emphasizes anthropogenic impacts on biodiversity and statistical validation strategies in forensic toxicology.

Zhengyang Zhouمشاهده پروفایل

دانشیار

• Aging
• Alzheimer's disease
• Biostatistics
• +۵ مورد دیگر

Dr. Zhengyang Zhou is an Associate Professor in the College of Public Health at the University of North Texas Health Science Center, Department of Population and Community Health. His research focuses on statistical methodology development and collaborative interdisciplinary studies in aging/Alzheimer’s disease, health behaviors/substance use, and statistical genetics. He holds a PhD in Biostatistics from Southern Methodist University and has been actively funded since 2017 with 16 sponsored projects including NIH grants. His work includes 56 peer-reviewed publications with an h-index of 14 and 523 citations. Education: BS in Mathematics, Beijing Institute of Technology MS in Statistics, Southern Methodist University PhD in Biostatistics, Southern Methodist University Research Interests: Statistical method development for real-world challenges Evaluation of existing statistical methods Large-scale interdisciplinary collaborations in Alzheimer’s disease, substance use, and genetic epidemiology Awards: 2021 Junior Investigator Award, Research Society on Alcoholism Grants & Projects: PI/Co-I on 16 projects including NIH/NIA grants (e.g., Health & Aging Brain Study-HD) Focus areas: computational tools, genomic screens, and population health disparities Labs/Teams: Leads a research group studying Alzheimer’s disparities in Mexican/African American populations using advanced ML/AI and statistical techniques.

Dipak Deyمشاهده پروفایل

استاد

• Bayesian analysis
• Bioinformatics
• Biostatistics
• +۸ مورد دیگر

Dipak Dey is a Professor in the Department of Statistics at the University of Connecticut . His work bridges theoretical and applied statistics, with a focus on Bayesian methodologies and computational statistics. Affiliations : University of Connecticut, Department of Statistics Contact : dipak.dey@uconn.edu , Office: AUST 327, Phone: (860) 486-4755 His research interests span Bayesian analysis , Biostatistics , Computational statistics , Statistical genetics , and Spatio-temporal modeling . He has pioneered techniques in spatial curvature processes and scalable Bayesian algorithms for large datasets. Recent publications highlight his contributions to Bayesian spatial modeling (blockNNGP, curvature processes), survival analysis (skew-t distributions, cure rate models), and computational statistics (variable selection in Gaussian processes, fast inference algorithms). Applications include insurance data , epidemiology , and environmental statistics . Scientific Awards : Board of Trustees Distinguished Professor (University of Connecticut) He actively collaborates on interdisciplinary projects and mentors researchers in advanced statistical methodologies for complex data structures.