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Kerri VierlingView profile

Professor

• Avian Ecology
• Fire Ecology
• Disturbance Ecology
• +3 more

Kerri Vierling is a Professor of Wildlife Resources and Director of the College of Natural Resources (CNR) Graduate Studies at the University of Idaho. She holds a Ph.D. in Biology (1998) and M.A. in Biology (1994) from the University of Colorado, along with a B.A. in Biology from The Colorado College (1990). Her research focuses on avian ecology, disturbance ecology, and the impacts of vegetation structure on wildlife distributions. Her work integrates remote sensing techniques (e.g., LiDAR) to investigate habitat relationships and land use effects on animal populations. Dr. Vierling has received numerous awards for teaching and advising, including the U of I Advising Excellence Award (2017), the CNR External Advisory Board’s Faculty Excellence Award (2017), and multiple College of Natural Resources Outstanding Teacher awards. Her advising mentorship has supported both undergraduate and graduate students in ecological and conservation fields. Her recent research emphasizes fire ecology, climate change vulnerability assessments, and avian biodiversity in temperate forests. Collaborative studies involve analyzing bird assemblages using remote sensing data and exploring pest control services provided by functional bird diversity in agricultural systems. Publications span topics from thermal ecology of tree cavities to landscape-scale habitat modeling. She actively participates in interdisciplinary projects linking ecological data with management strategies, such as optimizing LiDAR applications for wildlife distribution studies. Grants & Funding: Not explicitly listed in text, but inferred through research focus areas. Labs/Teams: Involved with CNR’s wildlife ecology and remote sensing research groups.

Brian KellerView profile

Assistant Professor

• Missing Data Analysis
• Bayesian Statistics
• Statistical Computing
• +3 more

Brian Keller is an Assistant Professor in the Department of Educational, School & Counseling Psychology at the University of Missouri's College of Education & Human Development. His research focuses on statistical methodologies, particularly missing data analysis, Bayesian statistics, structural equation modeling, and multilevel modeling. He is the developer of Blimp statistical software, which supports advanced statistical computing methods. Key areas of expertise include missing data imputation techniques, Bayesian computational methods, and the application of statistical models to educational and psychological data. His work emphasizes methodological innovation, software development, and the rigorous analysis of complex datasets. Recent research trends in his articles highlight advancements in latent variable modeling, nonlinear effects, and the integration of computational tools like Blimp. His contributions address challenges in multilevel models, missing data mechanisms, and model comparison criteria such as DIC and WAIC. No specific scientific awards are listed for Brian Keller in the provided information. His professional affiliations include the College of Education & Human Development and the Blimp Statistical Software initiative. He advises students in the department but no specific advisee names are provided. Research grants and funding details are not explicitly mentioned in the text. Blimp Statistical Software is a key collaborative project, reflecting his commitment to advancing statistical methods and software solutions for researchers.

• Computational Social Science
• Misinformation Studies
• Social Media Algorithm Analysis
• +4 more

Professor Jana Lasser holds a professorship in Data Analysis at the University of Graz, where she leads the Complex Social & Computational Systems research group at the interdisciplinary IDea_Lab. She serves as Associate Faculty at the Complexity Science Hub Vienna and previously held positions at Graz University of Technology as a Marie Curie Fellow and interim professor at RWTH Aachen. Her educational background includes a PhD in Physics from Georg-August-University Göttingen (2019), conducted at the Max Planck Institute for Dynamics and Self-Organization, with research on salt desert pattern formation. Her academic journey reflects a transition from geophysics to computational social science. Lasser's research centers on emergent phenomena in complex social systems, employing machine learning, natural language processing, and computational modeling. Key interests include misinformation spread on social media, counterspeech effectiveness, social media recommendation algorithms' societal impact, and the fracturing of societal understanding of honesty. Her work combines theoretical frameworks with real-world applications through agent-based simulations and large-scale text analysis. Her recent publications reveal strong thematic coherence across computational social science, with significant focus on political communication dynamics, platform governance, and methodological innovation. The 15 most recent works demonstrate increasing integration of computational methods with social theory, particularly in analyzing truth-concept evolution in political discourse and developing alternative social media architectures. ERC Starting Grant 101160928 DeSiRe for designing social media recommendation algorithms FWF standalone project P 37280-N on conspiracy theory spread netidee SCIENCE prize for digital civic innovation Marie Curie Fellowship for postdoctoral research Lasser actively supervises graduate students in computational social science, with current focus on topics related to her ERC-funded project. She leads the Survey Special Interest Group within the COST Action on Researcher Mental Health, conducting Europe's largest benchmark study on academic mental health. Her lab maintains strong collaborations with the Complexity Science Hub Vienna and international institutions.

Paul GustafsonView profile

Professor

• Biostatistics
• Epidemiology
• Bayesian Analysis
• +3 more

Paul Gustafson is a Professor and Head of the Department of Statistics at the University of British Columbia's Faculty of Science. He holds a faculty position at the Vancouver Campus and can be contacted via gustaf@stat.ubc.ca. His research focuses on advanced statistical methodologies with applications in epidemiology, public health, and clinical research. Key interests include Bayesian analysis, causal inference, measurement error correction, and meta-analysis. Dr. Gustafson's work emphasizes methodological innovations in handling complex data challenges such as exposure misclassification, missing data, and observational study biases. He collaborates on projects addressing opioid use disorder treatment efficacy, infectious disease epidemiology, and health policy. Current advisees include Nathaniel Wu Dyrkton, Daniel Daly Grafstein, Seren Lee, Yuan Xia, and Mallory J Flynn. His research outputs span statistical theory, applied public health studies, and computational methods. Recent work highlights include Bayesian approaches to sample size determination, probabilistic bias analysis in diagnostic studies, and frameworks for evaluating treatment benefit predictors using observational data. Dr. Gustafson is also involved in global initiatives such as the Zika Virus Individual Participant Data Consortium. Notable methodological contributions include developing techniques for partially identified models, correcting measurement errors in health surveys, and advancing causal inference methodologies. He has contributed to R packages like rtestim for epidemic modeling and CRTpowerdist for cluster randomized trial analysis.

Wendy ChanView profile

Assistant Professor

• Educational Statistics
• Generalization of Research Findings
• Statistical Methods in Education
• +3 more

Wendy Chan is an Assistant Professor in the Human Development and Quantitative Methods Division at the University of Pennsylvania's Graduate School of Education. Her expertise lies in educational statistics, focusing on improving statistical models to enhance the generalization of research findings from small-scale studies to broader populations. She holds a Ph.D. in Statistics from Northwestern University and has experience teaching mathematics in New York City through Teach for America. Education: Ph.D. in Statistics, Northwestern University Teach for America, New York City middle school teaching experience Research Interests: Dr. Chan's work addresses challenges in scaling up educational interventions and improving generalization from localized trials. She explores statistical methodologies such as propensity score methods, robust estimation, and small-area estimation to tackle issues like model assumption violations and limited data. Her current projects include assessing generalization assumptions, evaluating robust estimation approaches, and analyzing heterogeneous treatment effects. Grants & Projects: Spencer Foundation: Investigating covariates, matching, and longitudinal population changes in generalization. IES: Conceptual replication of the HOPS program for ADHD organizational skills. PCORI: Reducing disparities in ADHD behavioral therapy access for low-income families. Labs/Teams: Collaborates on interdisciplinary projects, including the longitudinal study of novice teachers' role-identities and behavioral health interventions in pediatric care.

• Ecology
• Conservation Ecology
• Agriculture
• +5 more

Grant Hamilton is a Professor in Ecology at Queensland University of Technology (QUT), affiliated with the School of Biology & Environmental Science within the Faculty of Science. His research focuses on ecological analytics, conservation science, and the application of unmanned aerial systems (UAVs) for wildlife surveillance, invasive species management, and agricultural systems. He holds a Doctor of Philosophy from QUT and is a member of the Ecological Society of Australia and the Modelling and Simulation Society of Australia and New Zealand. Research interests include detection and surveillance technologies, spatial analytics, and the integration of artificial intelligence with ecological monitoring. Notable projects include the Yurol Ringtail State Forest Koala Baseline and Monitoring Project, Agri-Intelligence in Cotton Production Systems, and automated wildlife detection using drones. His work bridges ecological theory with practical applications in conservation, agriculture, and biosecurity. Publications span topics like UAV-based wildlife detection, statistical modelling for pest surveillance, and Bayesian networks for environmental management. He has advised multiple PhD and master’s students on projects related to pest invasions, agricultural systems, and biodiversity monitoring. Current grants include Australian Competitive Grants focusing on digital agriculture and cotton production systems.

• Tectonic Evolution
• Paleomagnetism
• Subduction Zones
• +3 more

Prof. Dr. Derya Gürer is affiliated with the Institute of Geosciences at Heidelberg University , where she focuses on tectonic processes, paleomagnetism, and geodynamic reconstructions. Her research spans from the Mediterranean to the Agulhas Plateau, integrating fieldwork and geochemical analysis. Research Themes : Tectonic evolution of Anatolia, paleomagnetic constraints on plate motions, subduction zone dynamics, and Cretaceous climate studies via oceanic plateau investigations. Methodologies : Utilizes paleomagnetic data, U-Pb geochronology, clumped isotope thermometry, and structural analysis of ophiolites and sedimentary basins. Scientific Engagement : Actively contributes to diversity initiatives in geosciences and science communication through digital platforms. Her recent work emphasizes the interplay between Arabia-Eurasia convergence and Anatolian tectonics, while advancing polar wander resolution techniques through uncertainty quantification. She participates in international collaborations like IODP Expedition 392.

Nik CunniffeView profile

Professor

• Epidemiological Modeling
• Plant Disease Ecology
• Computational Biology
• +3 more

Nik Cunniffe is a Professor in the Department of Plant Sciences at the University of Cambridge, specializing in epidemiological modeling of plant pests and pathogens. His research focuses on developing theoretical and applied models to inform policy decisions regarding disease detection, control, and evolutionary dynamics. Key Research Areas: Theoretical epidemiology (deterministic, stochastic, and spatial models) Applied plant disease management (Phytophthora ramorum, ash dieback) Computational simulation techniques at large spatial scales Projects: Approximate models for complex disease management Stakeholder behavior impact on control efficacy Optimal fungicide resistance strategies Trap/repellent companion plants for virus control Collaborations: Cambridge NERC Doctoral Training Partnership (C-CLEAR DTP) NERC-funded CREATES program Email: njc1001@cam.ac.uk

Douglas Jackson-SmithView profile

Professor

• Sociology of Agriculture
• Environmental Sociology
• Rural Sociology
• +6 more

Douglas Jackson-Smith is a Professor and the Kellogg Endowed Chair of Agroecosystem Management at The Ohio State University's School of Environment and Natural Resources. He serves as Director of the Agroecological Management Program and is based at the Ohio Agriculture Research and Development Center (OARDC) in Wooster, Ohio. His work focuses on interdisciplinary research at the intersection of social science and environmental systems, with particular emphasis on agricultural landscapes, water resources, and sustainable food systems. Dr. Jackson-Smith earned his three graduate degrees from the University of Wisconsin: a MS in Rural Sociology, an MA in Agricultural Economics, and a PhD in Sociology. His undergraduate work was completed at Deep Springs College in California and Cornell University in New York. Born in Boston and raised in Utah, he brings diverse regional perspectives to his research. His research program spans multiple scales to understand the structural and behavioral drivers of farm structural change, land use transformations, and environmentally-relevant behaviors. He employs mixed methods including surveys, interviews, focus groups, and geospatial analysis to investigate complex socio-ecological systems. His work frequently addresses questions of farmer decision-making, conservation behavior, water resource management, and the social dimensions of agricultural sustainability. Analysis of his recent publications reveals a strong focus on participatory research methods, interdisciplinary collaboration, and the integration of social science into environmental modeling. His work spans multiple domains including soil health assessment, water conservation, crop-livestock integration, organic farming systems, and stakeholder engagement in environmental management. A consistent theme across his research is the importance of understanding human behavior within complex working landscapes. Director, Agroecological Management Program Kellogg Endowed Chair of Agroecological Management Professor, School of Environment and Natural Resources Dr. Jackson-Smith has developed extensive collaborative networks across disciplinary boundaries, working with natural scientists, engineers, and policymakers to address complex environmental challenges. His research on participatory approaches to agricultural research has emphasized the importance of engaging farmers and stakeholders in the research process. He maintains active collaborations with researchers across the United States, particularly on projects related to watershed management, sustainable agriculture, and climate-resilient farming systems. His laboratory and research team are based at the Ohio Agriculture Research and Development Center in Wooster, where he leads an interdisciplinary group of SENR faculty, graduate students, and post-docs focused on social and environmental aspects of complex working landscapes. The team frequently collaborates with agricultural producers, extension specialists, and policy makers to ensure research relevance and impact.

Rainer SchnellView profile

Professor

• Survey Methodology
• Privacy-Preserving Record Linkage
• Health Data Analysis
• +2 more

Prof. Dr. Rainer Schnell is a Professor at the Institute of Sociology, University of Duisburg-Essen. His research focuses on advanced survey methodologies, privacy-preserving data linkage techniques, and statistical analysis of health and social data. He leads the Chair of Empirical Social Research and contributes to interdisciplinary studies combining sociology, computer science, and public health. Research interests span: Survey Methodology : Innovations in web surveys, non-response analysis, and data quality assurance Data Privacy : Cryptographic techniques for secure record linkage and vulnerability assessments Health Analytics : Vaccination behavior studies, health data governance, and pandemic-related research Recent publications demonstrate a strong emphasis on: Privacy-enhancing technologies for sensitive data integration Methodological critiques of survey and data linkage practices COVID-19-related behavioral research using large-scale population data

Siân L. CurtisView profile

Professor

• Statistical Demography
• Reproductive Health
• Contraceptive Use Dynamics
• +5 more

Siân L. Curtis is a Professor at the University of North Carolina , specializing in Maternal and Child Health within the School of Public Health. Her career spans decades of leadership in global health, including serving as Director of the MEASURE Evaluation Project from 2002 to 2012, overseeing 150+ monitoring and evaluation activities across 30+ countries. Key roles: Senior Evaluation Specialist (MEASURE Evaluation), former Senior Analyst (DHS Project) Research themes: contraceptive use dynamics, fertility preferences, maternal health equity, survey methodology, and measurement innovation Research Focus: Curtis pioneered contraceptive calendar data analysis using DHS surveys, revealing complexities in fertility intentions post-contraceptive discontinuation. Recent work examines maternal health disparities in Bangladesh, comparing urban/rural service equity (2016 BMMS) and validating self-reported obstetric complications. Methodological contributions include spatial linking of facility/population surveys and verbal autopsy reliability for maternal mortality estimation. Publications: Over 74 citations include groundbreaking studies on contraceptive discontinuation, maternal health metrics, and survey design challenges. Her 2023 Demography paper introduces novel statistical models for unintended pregnancy analysis. Global Impact: Projects span Zambia (gender equity in food security), Ukraine (TB/HIV integration), and multiple countries via the Data for Impact (D4I) initiative. Current work explores complexity-aware evaluation frameworks for contraceptive access portfolios.

Bryan E. ShepherdView profile

Professor

• Biostatistics
• Causal Inference
• Measurement Error
• +3 more

Bryan E. Shepherd serves as Professor of Biostatistics and Biomedical Informatics and Vice Chair of Faculty Affairs in the Department of Biostatistics at Vanderbilt University. As a primary faculty member, he leads methodological research while overseeing departmental academic operations within the Vanderbilt University Medical Center ecosystem. His foundational education includes a PhD in Biostatistics from the University of Washington, establishing his technical expertise in advanced statistical methodologies. This training underpins his dual focus on theoretical innovation and real-world health applications. Dr. Shepherd's research centers on solving complex data challenges through novel biostatistical frameworks, particularly causal inference under measurement error, two-phase sampling optimization, and longitudinal analysis of error-prone clinical data. His work bridges theoretical statistics and urgent public health needs, with HIV research forming a major application domain where he addresses disparities in treatment access, care continuum outcomes, and comorbid conditions across global populations. Recent methodological breakthroughs include R packages for semiparametric likelihood estimation and rank correlation analysis. Analysis of his 2023-2025 publications reveals a dominant trend toward methodological solutions for data imperfections (measurement error, selective sampling) combined with high-impact applications in HIV epidemiology. His Latin American and African cohort studies consistently examine sex disparities, treatment efficacy, and social determinants of health, while his statistical innovations focus on efficiency gains in causal estimation and correlation modeling for clustered data. No specific scientific awards are documented in the available institutional profile. Though student advising details are unlisted, his extensive publication record—particularly collaborative international studies—implies active mentorship of junior researchers. His leadership as Vice Chair of Faculty Affairs suggests significant administrative responsibilities alongside research. Grant activity is inferred from multi-country HIV studies involving Vanderbilt's Center for Quantitative Sciences and Data Coordinating Center. Dr. Shepherd operates within Vanderbilt's collaborative research infrastructure, notably the Center for Quantitative Sciences (CQS) and Biostatistics Data Coordinating Center (VBDCC). His work leverages these resources for large-scale analyses of HIV cohorts across Latin America and Nigeria, focusing on treatment outcomes, genetic risk factors, and health system barriers. The Vanderbilt Nigeria Biostatistics Training Program (VN-BioStat) reflects his commitment to global capacity building in biostatistics.

Wei WuView profile

Professor

• Clinical Psychology
• Quantitative Psychology
• Longitudinal Data Analysis
• +3 more

Wei Wu is a Professor in the Department of Psychology at Indiana University, specializing in quantitative methodology for psychological research. His work bridges clinical applications with advanced statistical techniques, particularly in longitudinal study designs. His academic credentials include: Ph.D. in Quantitative Psychology from Arizona State University (2008) M.S. in Personality Psychology from East China Normal University (2003) B.Ed. in Special Education from East China Normal University (1997) Professor Wu's research focuses on developing and refining statistical methods for psychological inquiry, with emphasis on missing data handling, growth curve modeling, and model fit evaluation. His clinical psychology background informs applications in educational and developmental contexts, particularly regarding grade retention effects. Methodological rigor defines his contributions, advancing best practices for longitudinal data analysis in social sciences. Analysis of his publication record reveals consistent innovation in missing data techniques and experimental design optimization. His work demonstrates increasing sophistication in planned missing data approaches, with growing emphasis on computational efficiency and real-world applicability across psychology subfields. Recent publications integrate SEM and multilevel frameworks to solve complex modeling challenges. Professor Wu has secured substantial research funding through competitive grants: NSF grant ($422,900, 2011-2016) as PI for planned missing data designs in longitudinal research NIH grant ($1.7M, 2010-2014) as Statistical Consultant for resilience determinants study PCORI grant ($1.5M, 2013-2016) as Statistical Consultant for burnout impact research New Faculty General Fund ($8,000, 2010-2013) for model fit evaluation methods NICHD grant (2004-2008) as Research Assistant for grade retention study He actively mentors graduate researchers, with documented advisees including A. I. Carroll, P-Y. Chen, F. Jia, R. Kinai, F. Gu, P. R. Miller, and P. Pornprasermanit across multiple publications. While no formal lab structure is specified, his extensive grant collaborations indicate active participation in interdisciplinary research networks focused on methodological advancement.

Wei-Yin LohView profile

Professor

• Machine Learning
• Classification and Regression Trees
• Data Analysis
• +4 more

Wei-Yin Loh is a Professor in the Department of Statistics at the University of Wisconsin–Madison, affiliated with the School of Computer, Data & Information Sciences. His primary research focuses on classification and regression trees, with applications in machine learning, healthcare, and engineering. He has contributed extensively to the development of algorithms and methodologies in tree-based modeling, including the GUIDE software package. Dr. Loh's work spans theoretical advancements and practical implementations, addressing challenges such as missing data imputation, subgroup identification in precision medicine, and variable importance assessment. His research integrates statistical rigor with real-world problems in public health, federal agency performance analysis, and environmental modeling. Key contributions include studies on tobacco use prediction, cardiovascular health modeling, and pandemic-related mortality analysis. He has also explored applications in engineering, such as pavement thickness analysis and project delivery system evaluation. His publications emphasize interpretable models with strong predictive power, balancing complexity and practical utility. Awards and grants are not explicitly listed in the provided text, though his prolific publication record underscores his academic impact. Advising activities and student mentorship are not detailed here, but his research collaborations likely involve training the next generation of statisticians and data scientists.

Emily BergView profile

Associate Professor

• Statistics
• Small Area Estimation
• Agricultural Statistics
• +3 more

Emily Berg is an Associate Professor in the Department of Statistics at Iowa State University. Her research focuses on small area estimation, Bayesian hierarchical models, and statistical applications in agriculture and environmental science. She holds a Ph.D. in Statistics from Iowa State University (2010), an M.S. in Statistics (2008), and a B.A. in Mathematics from Middlebury College (2005). Education: Ph.D. Statistics, Iowa State University, 2010 M.S. Statistics, Iowa State University, 2008 B.A. Mathematics, Middlebury College, 2005 Her research interests include developing statistical methodologies for small area estimation, analyzing agricultural and environmental data, and addressing challenges in informative sampling and missing data. She has contributed to applications in transportation safety, veterinary practices, and environmental monitoring. Dr. Berg’s work emphasizes practical solutions for data integration and estimation under complex scenarios, including the use of Bayesian models and quantile regression techniques. She collaborates on projects such as the Conservation Effects Assessment Project and has developed web applications for data visualization and quality assessment. No scientific awards have been explicitly mentioned in the provided materials. She has advised or collaborated on numerous statistical methodologies but no specific student names are listed. Her research extends to agricultural economics, environmental modeling, and public health surveillance.