جستجوی استاد راهنمای دانشگاهی

Joonha Parkمشاهده پروفایل

استادیار

• Computational Statistics
• Sequential Monte Carlo Methods
• Markov Chain Monte Carlo (MCMC)
• +۵ مورد دیگر

Joonha Park is an Assistant Professor in the Department of Mathematics at the University of Kansas (since 2020), affiliated with the College of Liberal Arts & Sciences. His research focuses on computational statistics, including sequential Monte Carlo (SMC) and Markov chain Monte Carlo (MCMC) methods, with applications to public health, Bayesian analysis, and spatio-temporal systems. He holds a Ph.D. in Statistics from the University of Michigan (2018), an M.A. from KAIST (2012), and a B.S. from MIT (2007). Education: Ph.D. in Statistics, University of Michigan, 2018 M.A. in Mathematical Sciences, KAIST, 2012 B.S. in Mathematics, MIT, 2007 Research Interests: Computational methods for statistical inference, spatiotemporal modeling, Bayesian statistics, and applications to public health. His work emphasizes developing scalable algorithms for implicitly defined models and high-dimensional systems. Publications: Recent work includes cross-cultural studies on well-being, computational inference methods (e.g., the sbi R package), and epidemiological modeling. His research bridges statistical methodology and real-world applications, often involving international collaborations. Labs/Teams: Active in computational statistics research groups, contributing to open-source tools like sbi and athmc . Collaborates with interdisciplinary teams on public health data analysis and spatiotemporal dynamics.

• Ornithology
• Ecology
• Animal Behavior
• +۳ مورد دیگر

Dr. Lyanne Brouwer is a researcher at James Cook University's College of Science and Engineering, specializing in Zoology and Ecology. Her work focuses on avian behavior, evolutionary strategies, and ecological impacts of environmental change. Academic Rank: Researcher Institution: James Cook University Dr. Brouwer's research spans ornithology , evolutionary biology , and ecology , with particular emphasis on cooperative breeding systems, parental investment, and climate change effects on bird populations. Her recent publications explore topics such as sex-ratio biases, paternity assurance mechanisms, and vocal communication in fairy-wrens. Scientific trends in her work include urban ecology (analyzing anthropogenic impacts), climate sensitivity (meta-analyzing global avian responses), and behavioral innovation (studying prenatal learning and rescue behaviors). She contributes to computational biology through tools like the HIPHOP R package for parentage analysis. Dr. Brouwer investigates ecological constraints in tropical coastal ecosystems and integrates fitness components across generations in cooperatively breeding species. Her work combines field studies with statistical modeling to understand population viability under environmental stressors.

Fernando Riosmenaمشاهده پروفایل

دانشیار

• Immigration Studies
• Demography
• Public Health
• +۳ مورد دیگر

Fernando Riosmena is an Associate Professor in the Population Program at the Institute of Behavioral Science (IBS) and affiliated with the Department of Geography at the University of Colorado Boulder. His research focuses on demographic processes related to migration, immigrant health, and development in Latin American societies and U.S. immigrant communities. He teaches courses on migration, global change, and research methodology, including GEOG-6732 Formal Population Geography and GEOG-4292 Migration, Immigrant Adaptation, and Development . His work examines how migration policies, environmental conditions, and socioeconomic factors influence migration dynamics and well-being. He emphasizes the interplay between migration and health, particularly among Latin American immigrants in the U.S., and explores adaptation processes and acculturation effects. Courses like GEOG-2002 Geographies of Global Change reflect his interest in human-environment interactions and globalization. Riosmena’s publications analyze climate change impacts on migration, the role of social networks in displacement, and the paradox of health advantages among immigrants. He also contributes to policy discussions on border estimation and transnational migration effects. While no scientific awards are explicitly listed, his research is widely cited in demography and migration studies.

Will Pearseمشاهده پروفایل

استاد

• Evolutionary Ecology
• Biodiversity Conservation
• Statistical Modeling
• +۵ مورد دیگر

Will Pearse is a Professor in Evolutionary Ecology at Imperial College London's Department of Life Sciences (Silwood Park). He leads the Pearse Lab, which focuses on using evolutionary history to predict ecological processes, develop computational tools, and address biodiversity, health, and AI challenges. His work spans academia, government, and industry collaborations. Education: PhD in Ecology, Evolution & Conservation, Imperial College London (2013) MSc in Ecology, Evolution & Conservation, Imperial College London (2009) BSc Zoology, University of Cambridge (2008) Research Interests: The lab integrates phylogenetics, AI, and statistical modeling to study biodiversity conservation, climate impacts on species, and ecosystem services. Key areas include: - Phylogenetic diversity and conservation prioritization (EDGE program) - Fractal sampling designs for ecological monitoring - AI-driven phenology and disease forecasting (e.g., SARS-CoV-2) - Biodiversity-ecosystem service linkages Recent Articles Trends: His 2024-2025 work emphasizes AI applications in ecology, climate-driven pathogen evolution, and rewilding assessments. Earlier publications focused on phylogenetic methods, phenology modeling, and biodiversity policy. Awards: Robert May Prize (2014) for phylogenetic tools in ecology NERC funding leadership ($1.48M+) Co-lead on EDGE 2.0 conservation framework Advising & Grants: Supervised 19+ students/postdocs. Key grants include: - NERC/UKRI projects on biodiversity and health - NSF funding for macroecological studies (e.g., $299k for NSF MacroSystems Biology) - Imperial College collaborations with Hitachi and USDA Forest Service Labs/Teams: Maintains the Ecological Fractal Network (EFN) with >40 global collaborators and leads Imperial's Alan Turing Institute partnership for data-driven ecology.

Philip B. Starkمشاهده پروفایل

استاد

• Uncertainty Quantification
• Election Auditing
• Earthquake Prediction
• +۷ مورد دیگر

Philip B. Stark is a Distinguished Professor of Statistics at the University of California, Berkeley, where he holds a faculty position in the Department of Statistics. His research focuses on uncertainty quantification, election integrity, earthquake prediction, and applications of statistics in physical and social sciences. He has developed auditing methods used in over 15 U.S. states, and his work on the U.S. Census adjustment and election security has influenced policy and legal proceedings. Stark’s research spans computational statistics, risk-limiting audits, and environmental science, with notable contributions to soil carbon sequestration and food equity. He co-created SticiGui, an online introductory statistics course that pioneered MOOCs at UC Berkeley, reaching over 50,000 students. His consulting expertise spans litigation, clinical trials, and election integrity, with roles on advisory boards like the Election Assistance Commission and OSET Institute. Key research themes include statistical methodology for complex systems, election auditing innovations, and interdisciplinary applications in law, geophysics, and nutrition. His work emphasizes rigorous statistical methods to address societal challenges, from ensuring voting integrity to advancing sustainable food systems.

Francis X. Dieboldمشاهده پروفایل

استاد

• Financial Markets
• Macroeconomics
• Climate Change
• +۵ مورد دیگر

Francis X. Diebold is the Paul F. and Warren S. Miller Professor of Economics, Professor of Finance, and Professor of Statistics and Data Science at the University of Pennsylvania. He serves as Faculty Director of the Masters of Applied Economics and Data Science (MEDS) program. His research focuses on dynamic predictive modeling of financial markets, macroeconomic fundamentals, climate change, and their intersections. Key contributions include work on asset-return volatility, business cycles, yield curves, Arctic sea ice modeling, and network connectedness. Diebold has authored over 150 scientific papers and eight books, including Financial and Macroeconomic Connectedness and Yield Curve Modeling and Forecasting . He is a Fellow of numerous prestigious institutions such as the Econometric Society, American Statistical Association, and Guggenheim Foundation. His academic lineage includes over 75 PhD students. His research spans climate models, financial econometrics, and economic forecasting. Notable projects include analyzing Arctic sea ice dynamics and developing methodologies for measuring financial market connectedness. Diebold has held visiting positions at Princeton, Chicago, Johns Hopkins, and NYU. He is actively involved in editorial and advisory roles, including past presidency of the Society for Financial Econometrics and editorial board memberships for journals like Econometrica . His work bridges theoretical econometrics with practical applications in policy and climate adaptation.

Alison Johnstonمشاهده پروفایل

دانشیار

• Statistical Ecology
• Biodiversity Monitoring
• Citizen Science
• +۳ مورد دیگر

Dr. Alison Johnston is a Reader in Statistics and Director of CREEM at the University of St Andrews' School of Mathematics and Statistics. She specializes in developing statistical methods for ecological applications, particularly using citizen science data to study bird population dynamics and biodiversity patterns. Educational background includes a BSc in Quantitative Ecology from the University of St Andrews and a PhD from the University of Cambridge. Research focuses on: Statistical modeling of species distributions and population trends Bias correction in citizen science data Climate change impacts on avian communities Conservation prioritization through spatial analysis Her publications demonstrate consistent innovation in integrating large-scale ecological datasets, with recent work emphasizing climate-driven range shifts, conservation effectiveness, and methodological advances in statistical ecology. Research consistently addresses the challenge of extracting reliable ecological insights from complex observational data. Honors include the 2021 Marsh Award for Ornithology. She supervises PhD students working on biodiversity monitoring, spatial capture-recapture models, and statistical applications in conservation. Dr. Johnston leads projects at the Centre for Research into Ecological & Environmental Modelling (CREEM), developing tools that support evidence-based conservation policy and biodiversity management across multiple continents.

Tiago A. Marquesمشاهده پروفایل

پژوهشگر ارشد

• Ecological Statistics
• Marine Mammal Ecology
• Population Density Estimation
• +۳ مورد دیگر

Dr. Tiago A. Marques is a Senior Research Fellow at the School of Mathematics and Statistics, University of St Andrews, UK, and an invited professor at DEIO (Universidade de Lisboa, Portugal). He specializes in Ecological Statistics , focusing on population density estimation using passive acoustic data and distance sampling methods. His work spans marine mammals (cetaceans, polar bears), fish, birds, and other wildlife. Key projects include DECAF (cetacean abundance via passive acoustics) and LATTE (cetacean behavior and human noise impact analysis). Education: Biologist by training, with an MSc and PhD in Statistics. Research Interests: Ecological statistics, acoustic monitoring, conservation biology, and statistical methodology dissemination. Key Contributions: Developed distance sampling software tools, pioneered methods for integrating acoustic data into ecological surveys, and led polar bear population studies in the Arctic. Collaborates globally on wildlife conservation and statistical ecology. Labs/Teams: CREEM (Centre for Research into Ecological & Environmental Modelling) at St Andrews. Active in open-source tool development, including R packages and GitHub repositories like DeepDiverCueRates .

Jorge Cuartasمشاهده پروفایل

استادیار

• Developmental Psychology
• Public Health
• Early Childhood Development
• +۴ مورد دیگر

Jorge Cuartas is an Assistant Professor in the Department of Applied Psychology at New York University's Steinhardt School, leading the Catalyzing Action for Resilient Ecologies (CARE) lab. He holds advanced degrees from Harvard University, Universidad de los Andes, and Universidad Jorge Tadeo Lozano, specializing in Human Development, Economics, and Education. His research focuses on resilience promotion, violence prevention, early childhood programs, and climate change impacts on development. He employs interdisciplinary methods like econometrics and meta-analysis, with publications in The Lancet , Nature Human Behaviour , and Child Development . His work bridges academia and policy, informed by global collaborations with UNICEF, the World Bank, and Colombia's government. Dr. Cuartas has received prestigious awards including the APA Early Career Award, Spencer Fellowship, and SRCD Levin Award. His research highlights the intersection of socioeconomic factors, environmental risks, and developmental outcomes, advocating for evidence-based interventions in vulnerable populations. Before NYU, he served as a consultant for international organizations and remains a researcher at Colombia's CESED. His CARE lab addresses systemic challenges in child development through innovative, context-sensitive solutions.

Alexander Sahnمشاهده پروفایل

استادیار

• Political Economy
• Urban Politics
• American Political Development
• +۵ مورد دیگر

Alexander Sahn is an Assistant Professor of Political Science and the Thomas J. Pearsall Fellow at the University of North Carolina at Chapel Hill. He specializes in political economy and representation, focusing on subnational U.S. governments, housing policies, and urban governance. His research examines racial backlash in exclusionary zoning, public comment influence on housing approvals, and municipal civil service reforms. Education: PhD in Political Science from the University of California, Berkeley (2020); Postdoctoral Fellow at Princeton University’s Center for the Study of Democratic Politics (2020–2023). Research Interests: Political economy of housing and urban development Institutional design of local governments Racial dynamics in policy implementation Public participation mechanisms Awards: Recognized with the Susan Clarke Young Scholar Award (APSA Urban Politics) and Emerging Scholar Award (APSA American Political Economy Section). Advising/Grants: Currently mentoring students in urban politics and political methodology. No active grants explicitly mentioned, but his research often addresses applied policy questions. Labs/Teams: Active in UNC’s political science department, collaborating on projects about neighborhood representation and climate communication.

Liudas Giraitisمشاهده پروفایل

استاد

• Econometrics
• Time Series Analysis
• Quantitative Methods
• +۳ مورد دیگر

Liudas Giraitis is a Professor of Econometrics at Queen Mary University of London, affiliated with the School of Economics and Finance. He holds a PhD from Vilnius University and has extensive research experience at Heidelberg University, Boston University, and the London School of Economics. His research focuses on econometrics and quantitative methods, emphasizing long memory processes, ARCH models, semiparametric inference, and time-varying random coefficient models. He has published extensively in leading statistical and econometric journals. His work bridges econometrics, statistics, and probability theory, with a strong emphasis on time series analysis and asymptotic theory. Recent research explores forecasting under ongoing structural change, heteroscedasticity testing, and dynamic stochastic general equilibrium (DSGE) models with financial frictions. Education: PhD in Econometrics from Vilnius University Key Research Themes: Long memory processes, ARCH/GARCH models, time-varying models, nonstationary time series, and asymptotic theory Affiliations: Queen Mary University of London, School of Economics and Finance Research Interests: Dr. Giraitis’s work addresses theoretical and applied challenges in econometric modeling, including robust inference for time series with general heterogeneity, cyclical time series analysis, and the development of asymptotic frameworks for dependent data. His contributions span both foundational theory and practical applications in finance and macroeconomics. Publications: Over 50 peer-reviewed articles in journals such as Journal of Econometrics , Annals of Statistics , and Journal of Time Series Analysis . Key works include the monograph Large Sample Inference for Long Memory Processes (2012) and recent studies on time-varying volatility and structural change. Lab/Team: Active in the School of Economics and Finance’s research group on econometric theory and applied time series analysis.

Alexander Robelمشاهده پروفایل

دانشیار

• Climate, Oceanography, and Weather
• Cryospheric Science
• Paleoclimate
• +۶ مورد دیگر

Dr. Alexander Robel is an Associate Professor in the School of Earth & Atmospheric Sciences at the Georgia Institute of Technology, part of the College of Sciences. His research focuses on climate, oceanography, and cryospheric science, with a particular emphasis on glaciers, ice sheets, and sea level change. He holds a Ph.D. in Earth and Planetary Science from Harvard University (2015) and a B.S. in Physics and Earth Sciences from Duke University (2010). Research Interests: Dr. Robel’s work addresses critical questions in cryospheric science, including ice-ocean interactions, climate modeling, and the dynamics of ice sheets. His studies integrate computational and mathematical modeling to explore topics such as glacier stability, sea level rise, and the impacts of climate variability on polar regions. Key themes include the stochastic parameterization of ice dynamics, the role of subglacial hydrology, and the statistical analysis of climate forcing on glacial systems. Publications Trends: Recent work emphasizes advancing stochastic models for ice sheet projections, quantifying biases in climate-ice sheet interactions, and analyzing oceanic drivers of glacial retreat. His studies often bridge observational data with theoretical frameworks, offering insights into both near-term and long-term climate impacts. Education: Ph.D., Earth and Planetary Science, Harvard University (2015) B.S., Physics and Earth Sciences, Duke University (2010) Advising & Grants: Specific advising or grant details are not explicitly listed in the provided text. Labs/Teams: Likely affiliated with Georgia Tech’s School of Earth & Atmospheric Sciences research groups, though specific teams are not mentioned here.

Erin Schliepمشاهده پروفایل

دانشیار

• Environmental Statistics
• Bayesian Statistics
• Spatial Statistics
• +۳ مورد دیگر

Erin Schliep is an Associate Professor in the Department of Statistics at North Carolina State University (NCSU), with a joint appointment as a Research Associate at Duke University. She holds a Ph.D. in Statistics from Colorado State University (2013). Her research focuses on Bayesian statistics, spatiotemporal modeling, and environmental applications, including ecological dynamics and sports analytics. Her work addresses challenges in climate change impacts, species distribution modeling, and marine conservation. Key research areas include spatiotemporal analysis of ecological systems, data fusion methods for environmental monitoring, and causal inference in spatial contexts. She has contributed to studies on lake productivity responses to climate, whale calling patterns, and NBA player performance metrics. Recent projects include NSF-funded work on modeling and uncertainty quantification in life sciences. Her publications span high-impact journals like JASA, PNAS, and Ecology, addressing topics such as marine mammal abundance estimation, algal biomass changes in lakes, and spatial causal inference for marine protected areas. She advises students and collaborates widely, exemplified by her role in the ContLimno team analyzing freshwater ecosystems. Erin’s expertise extends to methodological advancements, including hierarchical modeling, Hawkes processes, and machine learning integration. She emphasizes interdisciplinary applications, bridging statistics with ecology, environmental science, and sports analytics.

Giuseppe Vinciمشاهده پروفایل

استادیار

• Astrostatistics
• Bayesian Statistics
• Computational Neuroscience
• +۸ مورد دیگر

Giuseppe Vinci is an Assistant Professor at the Department of Applied and Computational Mathematics and Statistics (ACMS) at the University of Notre Dame, within the College of Science. His research focuses on probabilistic graphical models, particularly in neuroscience and genomics applications. He holds a Ph.D. in Statistics from Carnegie Mellon University (2017), and completed postdoctoral research and lecturing at Rice University (2017–2020). Vinci’s expertise spans astrostatistics, forensic science, and geometric data analysis. Education: Ph.D. in Statistics, Carnegie Mellon University (2017) M.Sc. in Statistics, Carnegie Mellon University (2013) M.Sc. in Economics and Social Sciences, Bocconi University (2012) B.Sc. in Economics, University of Catania (2009) Research Interests: High-dimensional statistical theory of graphical models, matrix completion, astrostatistics, neuroscience, genomics, forensic science, and geometric data analysis. His work addresses challenges in neuronal functional connectivity, genomic networks, and climate science. Awards: NeuroNex Postdoctoral Trainee (NSF) Rice Academy Postdoctoral Fellow Three-minute thesis competition (Top-10, Carnegie Mellon University) Advising & Grants: Vinci mentors multiple Ph.D., MSc, and undergraduate students in projects spanning forensic statistics, genomics, and astrostatistics. He has secured funding for undergraduate research programs and participated in NIH-funded interdisciplinary training. Labs/Teams: Involved in collaborative projects with institutions like Baylor College of Medicine and Rice University, focusing on neurotheory and statistical methods in neuroscience.

Eric Lambمشاهده پروفایل

استاد

• Plant Ecology
• Fire Ecology
• Grasslands Conservation
• +۳ مورد دیگر

Eric Lamb is a Professor in the Department of Plant Sciences at the University of Saskatchewan, affiliated with the College of Agriculture and Bioresources. He is also the Curator of the WP Fraser Herbarium. His research focuses on plant ecology, fire ecology, and grassland conservation. He holds a Ph.D. from the University of Alberta, an M.Sc. from Lakehead University, and a B.Sc. from the University of British Columbia. Dr. Lamb teaches undergraduate courses including Plant Ecology, Grassland Soils and Vegetation, and Advanced Plant Ecology, as well as graduate courses in statistical and experimental analysis methods. His research explores ecosystem services, invasive species impacts, soil microbiology, and fire management strategies in grassland ecosystems. Key Research Themes: Fire effects on plant communities, microbial interactions in root systems, and conservation strategies for grassland biodiversity. Notable Contributions: Pioneering studies on prescribed fire reintroduction, microbial community dynamics during invasions, and the ecological impacts of grazing regimes. His recent publications emphasize global grassland drought vulnerabilities, Arctic soil biodiversity under climate change, and the role of microbial networks in ecosystem resilience. Dr. Lamb collaborates widely on interdisciplinary projects bridging ecology, conservation, and land management. Lab/Team Activities: Active in the WP Fraser Herbarium’s specimen curation and fieldwork initiatives, focusing on Saskatchewan’s endemic plant species and Arctic tundra restoration.