جست‌وجوی استاد و پژوهشگر

Weihua Anمشاهده پروفایل

استاد

• Network Analysis
• Causal Inference
• Bayesian Analysis
• +۳ مورد دیگر

Dr. Weihua An is a Professor of Sociology and Quantitative Theory and Methods at Emory University, with affiliations at The East Asian Studies Program, Goizueta Business School, and Rollins School of Public Health. He holds a Ph.D. in Sociology and A.M. in Statistics from Harvard University, with postdoctoral training at Harvard Kennedy School. His research focuses on network analysis, causal inference, Bayesian methods, and their applications to social inequality, health, and organizations. He has authored influential statistical software packages like 'fglsnet' and 'keyplayer' and edited special issues in top journals. His work bridges methodological innovation with substantive social science topics, including peer effects, policy diffusion, and network-based interventions. He has advised over 27 Ph.D. dissertations and received the Emory Faculty Teaching Award and the Clifford Clogg Award. Education: PhD in Sociology, Harvard University, 2011 MA in Statistics, Harvard University, 2009 MA in Economics, Claremont Graduate University, 2005 MA in Political Science, Peking University, 2002 BA in Public Administration, Wuhan University, 1999 Research Interests: Network Analysis, Causal Inference, Bayesian Analysis, Social Inequality, Health Policy, Organizational Studies. His methodological work emphasizes rigorous statistical frameworks for analyzing complex social systems. Grants & Awards: Funded by NIH, NSF, and U.S. Census Bureau Editor of Sociological Methodology Recipient of Clifford Clogg Award (ASA) Labs & Collaborations: Involved in interdisciplinary projects at Emory’s Institute for Quantitative Theory and Methods and collaborations with institutions like The Carter Center and Harvard University.

Grace Yiمشاهده پروفایل

استاد

• Statistical Methodology
• Measurement Error
• Causal Inference
• +۳ مورد دیگر

Grace Yi is a Professor at the University of Western Ontario and holds a Tier I Canada Research Chair in Data Science. She is affiliated with the Departments of Statistical and Actuarial Sciences and Computer Science. Her research focuses on statistical methodology addressing challenges in measurement error, causal inference, missing data, and machine learning. Yi has authored influential works, including the monograph Statistical Analysis with Measurement Error or Misclassification and co-edited Handbook of Measurement Error Models . She has served as Co-Editor-in-Chief of The Electronic Journal of Statistics and President of the Statistical Society of Canada. Her accolades include the CRM-SSC Prize (2010), Fellowships from the IMS and ASA, and leadership roles in professional societies. Education: Ph.D. in Statistics (University of Toronto, 2000), M.A. in Statistics (York University, 1996), M.Sc. and B.Sc. in Mathematics (Sichuan University, China). Research Interests: Measurement error models, causal inference, high-dimensional data analysis, statistical machine learning. Yi’s work bridges theoretical advancements and practical applications, particularly in handling noisy data across disciplines like epidemiology and public health. Her recent studies include analysis of COVID-19 data dynamics and quarantine strategies. She has supervised numerous students and contributed to software development, including R packages like augSIMEX and swgee .

Liqun Diaoمشاهده پروفایل

دانشیار

• Statistical Methodology
• Machine Learning
• Causal Inference
• +۷ مورد دیگر

Liqun Diao is an Associate Professor (Tenured) in the Department of Statistics and Actuarial Science at the University of Waterloo. He holds affiliations with the Health Data Science Lab and the Waterloo Artificial Intelligence Institute. His research focuses on developing statistical methods and machine learning algorithms for applications in medicine, public health, and insurance. Education: B.Econ. in Statistics, Renmin University of China (2007) M.Math. in Statistics (Biostatistics), University of Waterloo (2009) Ph.D. in Statistics (Biostatistics), University of Waterloo (2013) Research Interests: Recursive partitioning and tree-based methods for survival and health data Causal inference and missing data methodologies Bayesian nonparametric models and copula dependence structures Mortality forecasting and actuarial science applications Awards: 2013 Pierre Robillard Award (best doctoral thesis in Canadian statistics) 2024 Outstanding Performance and Teaching Awards at UW Professional Activities: Led research groups in health data science and AI Recipient of multiple grants from NSERC and industry partners Advises graduate students in statistics and actuarial science Labs/Teams: Active contributor to the Health Data Science Lab and Waterloo AI Institute, focusing on applying statistical innovations to real-world health and insurance challenges.

Glen McGeeمشاهده پروفایل

استادیار

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

Glen McGee is an Assistant Professor in the Department of Statistics and Actuarial Science at the University of Waterloo. He holds a PhD in Biostatistics from Harvard University and a BScH in Mathematics from Queen's University. His research focuses on developing statistical tools for epidemiology, environmental health, and health policy, with a particular emphasis on environmental mixture analysis, cluster-correlated data modeling, and outcome-dependent sampling methodologies. Education : PhD in Biostatistics, Harvard University BScH in Mathematics, Queen's University Research Interests : McGee advances methodologies for analyzing complex environmental mixtures and their health impacts, including incorporation of biological knowledge into statistical frameworks. His work addresses challenges in multigenerational studies, informative cluster sizes, and measurement error correction in case-crossover designs. Key application areas include hospital profiling, exposure misclassification, and longitudinal health data analysis. Research Trends : His publications emphasize Bayesian methods for mixture modeling, innovative sampling strategies for clustered data, and causal inference techniques. Notable contributions include frameworks for integrating biological pathways into environmental health analyses and developing efficient sampling approaches for healthcare performance evaluation. Grants & Labs : McGee collaborates on projects involving CMS data applications and maintains GitHub repositories like hospODS for hospital profiling methodology implementation. His work bridges statistical theory with practical public health applications, particularly in environmental epidemiology and healthcare analytics.

• Epidemiology
• Biostatistics
• Rheumatology
• +۳ مورد دیگر

Dr Sara Muller is a Reader in Epidemiology and Statistics at the School of Medicine, Keele University. She holds a Master’s in Biometry and a PhD in epidemiology, focusing on locomotor disability measurement using Rasch Analysis. Her research emphasizes primary care rheumatology (polymyalgia rheumatica, gout, inflammatory arthritis) and methodological innovations in healthcare data analysis. She is the Academic Career Development Lead for the NIHR Applied Research Collaboration West Midlands and actively promotes researcher development. Her teaching includes leading the Research and Analytical Skills module for undergraduates. Education: MSc Biometry, PhD (Epidemiology) Awards: Fellow of the Royal Statistical Society Dr Muller’s research spans epidemiological methodology, routinely collected healthcare data, and ordinal regression models. Her work often addresses patient-reported outcomes in rheumatology, with over 200 publications in journals like *Rheumatology* and *BMJ Open*. She collaborates with the Society for Academic Primary Care and serves as a former Associate Editor for *Family Practice*. Her recent articles highlight trends in Parkinson’s disease mortality, gabapentinoid risks in arthritis patients, and healthcare utilization patterns in fibromyalgia. She also explores digital health equity and public health implications of chronic conditions. Key Projects: PMR Cohort Study, ICEPAC survey on rheumatic disease impact Grants: NIHR-funded collaborations Dr Muller’s work bridges clinical and public health perspectives, with a focus on improving patient outcomes through evidence-based practices and data-driven solutions.

Xin Wangمشاهده پروفایل

استادیار

• Spatial Data Analysis
• Bayesian Statistics
• Survey Sampling
• +۲ مورد دیگر

Xin Wang is an Assistant Professor in the Department of Mathematics and Statistics at San Diego State University. Her research develops statistical methods for spatial data analysis, Bayesian inference, and survey sampling with applications in environmental science and public health. Dr. Wang's methodological work includes clustered coefficient regression models for small area estimation, spatial heterogeneity detection techniques, and spatio-temporal cluster analysis. Her applied research examines aquatic toxicity assessment, environmental monitoring, and warranty claim patterns using these advanced statistical approaches. She teaches graduate courses in mathematical statistics and survey methodology. Previously at Miami University, she developed curriculum linking statistical theory with computational implementation. Dr. Wang holds a PhD from Iowa State University and collaborates with researchers in epidemiology, materials science, and ecology on interdisciplinary projects requiring sophisticated data analysis.

Habib Tabatabaiمشاهده پروفایل

استاد

• Structural Engineering
• Bridge Reliability
• Infrastructure Durability
• +۲ مورد دیگر

Habib Tabatabai is a Professor and Director of the Structural Engineering Lab in the Department of Civil and Environmental Engineering at the University of Wisconsin-Milwaukee's College of Engineering & Applied Science. He holds a PhD, ME, and BCe from the University of Florida. His research focuses on bridge reliability, construction materials, structural health monitoring, and infrastructure durability. He is currently on sabbatical for Fall 2024. Research interests include: reliability analysis of bridges, utilization of industrial byproducts in construction, damage detection methods, and rehabilitation techniques for aging infrastructure. His publications primarily explore themes in structural mechanics, materials science, and infrastructure sustainability, with recent articles focusing on concrete technology, bridge safety assessments, and recycled construction materials.

Jun Yanمشاهده پروفایل

استاد

• Survival Analysis
• Spatial Statistics
• Network Analytics
• +۲ مورد دیگر

Dr. Jun Yan is a Professor in the Department of Statistics at the University of Connecticut. His research spans network analytics, spatial extremes, survival analysis, and statistical computing with applications in public health, finance, and environmental science. His core research interests include: network modeling and analysis, spatial statistics for climate extremes, survival analysis methodologies, statistical computing frameworks, and applications in interdisciplinary domains including sports analytics. Dr. Yan has developed significant statistical methodologies for network analysis, climate change detection, financial modeling, and health analytics. His recent publications demonstrate innovation in modeling complex network structures, analyzing climate extremes, developing computational approaches for massive datasets, and creating specialized statistical methods for health and finance applications. He maintains active collaborations across disciplines and contributes to open-source statistical software. Honors include: Guggenheim Fellowship, multiple Fromm Foundation commissions, and Barlow Endowment recognition.

Van Anh Huynh-Thuمشاهده پروفایل

استادیار

• Machine Learning
• Bioinformatics
• Gene Regulatory Network Inference
• +۳ مورد دیگر

Vân Anh Huynh-Thu is an Assistant Professor in the Department of Electrical Engineering and Computer Science at the University of Liège (Belgium). Her research focuses on improving machine learning techniques with an emphasis on model interpretability. She is based at B28: Systems and Modeling in Quartier Polytech, Allée de la Découverte 10, 4000 Liège, Belgium. Her primary research interests span Machine Learning , Bioinformatics , and Gene Regulatory Network Inference . Dr. Huynh-Thu has developed several influential methods including GENIE3, dynGENIE3, and Jump3 for inferring gene regulatory networks from expression data. Her work bridges the gap between machine learning theory and biological applications, particularly in understanding complex disease mechanisms through computational approaches. She has made significant contributions to interpretable machine learning models that maintain high predictive accuracy while providing insights into feature importance and model behavior. Her research demonstrates a progression from purely computational methods toward translational applications in medical research. Analysis of her recent publications reveals a clear trajectory from foundational work on gene regulatory network inference toward broader applications in medical research, particularly in Crohn's disease. Her research increasingly integrates machine learning with clinical applications, demonstrating a shift from purely computational methods to translational research with direct medical implications. The consistent emphasis across her work is on interpretability, rigorous validation, and the application of tree-based methods to complex biological systems, with a growing focus on proteomics and biomarker discovery for inflammatory bowel diseases. Dr. Huynh-Thu maintains an active GitHub presence with implementations of her methods, demonstrating her commitment to open science and reproducibility. Her software repositories have garnered significant attention from the research community, with GENIE3 alone having 88 stars and 37 forks on GitHub. She has developed multiple implementations of her algorithms in Python, MATLAB, and R, making them accessible to researchers across different computational environments. Her work has been influential in the DREAM challenges, where GENIE3 was the best performer in two network inference competitions.

• Econometrics
• Financial Econometrics
• Risk Management
• +۶ مورد دیگر

Patrick Gagliardini is a Full Professor of Econometrics at the University of Lugano (USI) within the Faculty of Economics and the Institute of Finance. He also serves as Pro-Rector at USI. His academic journey includes a PhD in Econometrics from USI (2003) and studies in Physics at ETH Zurich (1998). He has held roles such as Visiting Fellow at CREST Paris (2003) and Assistant Professor at the University of St. Gallen (2004–2006). His research focuses on econometric methods (nonparametric techniques, GMM, latent factor models) and financial applications such as credit risk, asset pricing, and risk management. Competence areas include Big Data, investment decisions, and systematic risk analysis. He teaches courses in econometrics, financial econometrics, and time series at the undergraduate, graduate, and PhD levels. Recent publications explore latent factor models, econometric testing (e.g., eigenvalue tests for factor detection), and financial decision-making in small data regimes. His work bridges theoretical econometrics with practical applications in finance and risk modeling. Notably, his research addresses challenges in dynamic latent factor models, hedge fund performance evaluation, and granularity theory in financial systems. He maintains an active academic profile with contributions to both theoretical and applied econometrics.

Karthik Srinivasanمشاهده پروفایل

استادیار

• Machine Learning Interpretability
• Explanatory Modeling
• Text Mining for Business Applications
• +۳ مورد دیگر

Karthik Srinivasan is an Assistant Professor in the Analytics, Information, and Operations Academic Area at the University of Kansas School of Business. He holds a Ph.D. in Management Information Systems from the University of Arizona, an M.Mgt. in Business Analytics from the Indian Institute of Science, and a B.E. from Mumbai University. His research focuses on interpretable machine learning, explanatory modeling, text mining for business applications, and healthcare information systems. He develops methods to enhance transparency in AI systems and applies data science to healthcare, finance, and retail contexts. Recent work includes predictive modeling for incomplete data, graph-based retail analytics, and analyzing pandemic impacts on stock markets and public health. His publications span journals like MIS Quarterly, Decision Support Systems, and Nature Digital Medicine. He also contributes open-source tools like TextRegress and MoreThanSentiments for advanced text analysis. Teaching responsibilities include undergraduate and graduate-level data management courses. His research emphasizes practical applications in business and public health, leveraging interdisciplinary approaches to address real-world challenges.

Igor Molybogمشاهده پروفایل

استادیار

• Artificial Intelligence
• Large Language Models
• Multimodal Modeling
• +۵ مورد دیگر

Igor Molybog is an Assistant Professor at the University of Hawai'i at Manoa, holding joint appointments in the Departments of Electrical and Computer Engineering and Information and Computer Sciences. His research focuses on advancing artificial intelligence, particularly through large language models (LLMs), multimodal modeling, and core machine learning optimization. He leads the HawAII research group, exploring applications like LLM alignment, efficient inference systems, and scaling properties of foundation models. Education: Ph.D. in Engineering from UC Berkeley (2022), specializing in optimization algorithms for complex systems. Previously worked at Meta AI on LLaMa model development. Research Interests: Efficient LLM development and evaluation frameworks Multimodal AI integration (video/audio + text) Scalable optimization for large models Computational efficiency in training/ inference Recent Work: Presented REAL alignment method (2024), developed long-context scaling techniques (2023), contributed to Llama 2 chat models (2023). Collaborates with organizations like Epoch AI on scaling challenges. Teaching: Offers courses in AI, machine learning, and optimization across ECE and ICS departments. Labs/Teams: Leads HawAII Initiative fostering AI collaboration at UH Manoa, organizes paper reading seminars, and hosts technical talks with industry experts.

Jeremy Gaskinsمشاهده پروفایل

دانشیار

• Bayesian Statistics
• Longitudinal Data Analysis
• Missing Data Methods
• +۳ مورد دیگر

Jeremy Gaskins, PhD, is an Associate Professor in the Department of Bioinformatics & Biostatistics at the University of Louisville's School of Public Health and Information Sciences (SPHIS). He joined UofL in 2013 as an Assistant Professor, earning tenure and promotion to his current rank in 2019. His expertise lies in Bayesian statistical methods for complex data structures, including longitudinal analysis, missing data imputation, and joint modeling of mixed data types. He collaborates with researchers across multiple departments, including OB/GYN, Radiation Oncology, and Surgery. Education: Ph.D. (2013) in Statistics, University of Florida B.S. (2007) in Mathematics and Applied Mathematics, Auburn University Research Interests: Development of Bayesian methods for longitudinal and clustered data Computational strategies for complex model inference Applications in medical and public health research Missing data mechanisms and imputation techniques Teaching: PHST 661: Probability PHST 662: Mathematical Statistics Collaborations & Labs: Active collaborations with UofL medical departments on applied health research Focus on translational statistics for biomedical and public health problems

Michelle Torresمشاهده پروفایل

استادیار

• Political Methodology
• Political Behavior
• Computer Vision
• +۵ مورد دیگر

Margaret Michelle Torres is an Assistant Professor in the Department of Political Science at the University of California, Los Angeles (UCLA). Her research focuses on political methodology, computer vision, causal inference, and survey methodology, with substantive interests in political media communication, participation, and attitude formation. She holds a Ph.D. in Political Science and an A.M. in Statistics from Washington University in St. Louis, alongside a B.A. in Political Science and International Relations from CIDE (Mexico City). Her work bridges computational methods and social science inquiry, emphasizing innovative tools for analyzing visual and textual data. Recent projects explore how ideology influences perceptions of political groups, the role of visual frames in protests, and methodological issues in causal inference. Torres has authored influential papers on topics ranging from election dynamics to the application of machine learning in political analysis. Notable contributions include frameworks for unsupervised visual analysis and critiques of posttreatment variable pitfalls in experiments. Her research frequently intersects with questions of media representation, public opinion, and institutional trust. Torres advises students in quantitative methods and political behavior, though specific advisee names are not listed here.

Mary Myerscoughمشاهده پروفایل

دانشیار

• Mathematical Biology
• Atherosclerosis Modeling
• Honeybee Behavior
• +۳ مورد دیگر

Professor Mary Myerscough is an Associate Professor in the School of Mathematics and Statistics at the University of Sydney. Her research focuses on mathematical biology, particularly modeling atherosclerosis progression, honeybee colony dynamics, and social insect behavior. She has contributed to understanding macrophage lipid dynamics, plaque regression mechanisms, and hive thermoregulation. Her work combines mathematical models with biological systems, addressing topics like cell behavior, population dynamics, and disease mechanisms. Myerscough has secured grants including an ARC Discovery Project on atherosclerosis and collaborations in mathematical biology. Her research spans interdisciplinary areas from cardiovascular science to ecological modeling, with over 55 peer-reviewed publications. Education background and affiliations: Affiliated with the University of Sydney's Faculty of Science. Research interests include mathematical modeling of biological systems, atherosclerosis, and social insect behavior. Grants include projects on lipid-cell interactions and educational initiatives like shark bite analytics. Key contributions include models of honeybee colony collapse, termite architecture, and atherosclerotic plaque development. Her work bridges applied mathematics with life sciences, addressing both theoretical and applied questions in health and ecology.