جست‌وجوی استاد و پژوهشگر

Partha Lahiriمشاهده پروفایل

استاد

• Survey Sampling
• Small-Area Estimation
• Bayesian Inference
• +۳ مورد دیگر

Partha Lahiri is a Professor at the University of Maryland, College Park, holding dual appointments in the Joint Program in Survey Methodology (JPSM) and the Department of Mathematics . He also serves as an Adjunct Research Professor at the Institute of Social Research, University of Michigan, Ann Arbor. Previously, he was the Milton Mohr Distinguished Professor of Statistics at the University of Nebraska-Lincoln. His expertise spans Survey Sampling , Small-Area Estimation , Bayesian and Empirical Bayes Inference , and Resampling Methods . He has advised organizations such as the United Nations Development Program, World Bank, and Gallup, and contributed to panels like the U.S. Census Advisory Committee. He is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics, and an elected member of the International Statistical Institute. Research interests include methodological advancements in small area estimation, survey data integration, and statistical linkage. His work emphasizes practical applications in policy and public health, including poverty measurement and pandemic-related studies (e.g., mask effectiveness perception during the pandemic). He has authored influential papers on topics like multi-goal prior selection, probabilistic record linkage, and resampling techniques for uncertainty quantification. His leadership roles, including directing JPSM, highlight his commitment to training future survey methodologists. Awards: ASA and IMS Fellowships, ISI Elected Membership Advisory Roles: U.S. Census Advisory Committee, National Academy of Sciences panels Publications Trends: Focus on small area estimation, Bayesian methods, and policy-relevant applications Grants/Projects: Collaborations with international organizations and academic institutions on statistical methodologies Lahiri’s research bridges theoretical statistics and practical survey challenges, with a strong emphasis on data integration and uncertainty estimation in complex systems.

Rajagopalan Balajiمشاهده پروفایل

استاد

• Hydroclimate Modeling
• Water Resources Management
• Climate Variability
• +۴ مورد دیگر

Rajagopalan Balaji is a Professor and Chair in the Department of Civil, Environmental, and Architectural Engineering at the University of Colorado Boulder, with a fellowship at the Cooperative Institute for Research in Environmental Sciences (CIRES). His work bridges climate science and water resources engineering, focusing on sustainable water management under climate variability. Education: B.Tech from NIT Kurukshetra (1989), M.Tech from Indian Statistical Institute (1991), and Ph.D. in Stochastic Hydroclimatology from Utah State University (1995). Research Themes: Climate drivers of hydrology, ensemble hydroclimate projections, decision support systems, Indian monsoon variability, big-data techniques, and Arctic sea-ice dynamics. Scientific Contributions: Over 50 peer-reviewed publications, including work on Science (2005) and Geophysical Research Letters (multiple). His Google Scholar articles (2025-2020) emphasize wildfire impacts on water quality, Bayesian forecasting models, and global lake storage trends. Awards: Devendra Lal Memorial Medal (2021), CIRES Fellow (2001-present). Students: Supervised 11 M.S. and Ph.D. students, including Katrina Grantz (Ph.D. 2005) and James Prairie (Ph.D. 2005). Applied Interests: Climate-informed reservoir operations, construction safety analytics, and paleoclimate reconstructions for modern water planning. His team develops tools like the SCHAAKE Shuffle for weather resampling.

• Statistical Genetics
• Spatial Statistics
• Machine Learning
• +۳ مورد دیگر

Arkajyoti Saha is an Assistant Professor in the Department of Statistics at the Donald Bren School of Information and Computer Sciences , University of California, Irvine. Previously, he was a UW Data Science Postdoctoral Fellow at the University of Washington, working with Drs. Daniela Witten and Jacob Bien. His academic journey includes a PhD in Biostatistics from Johns Hopkins Bloomberg School of Public Health (advised by Drs. Nilanjan Chatterjee and Abhirup Datta), and bachelor's/master's degrees in Statistics from the Indian Statistical Institute, Kolkata. Research Focus: His work bridges statistical methodology and computational tools for high-dimensional and spatially dependent data. Key areas include scalable algorithms for spatial genomics, environmental monitoring, and machine learning applications such as random forests for dependent data. He also develops R packages like RandomForestsGLS to address challenges in correlated data analysis. Publications: His recent work spans spatial variable gene identification, fuzzy clustering theory, and environmental sensor calibration. He emphasizes methodological innovation in statistical genetics and geospatial statistics. Education & Mentorship: Encourages prospective students to contact him directly. His academic background reflects a strong foundation in theoretical and applied statistics, with a focus on bridging computational efficiency and statistical rigor.

Davide La Vecchiaمشاهده پروفایل

استاد

• Time series analysis
• Robust and semiparametric inference
• Financial econometrics
• +۳ مورد دیگر

Davide La Vecchia is a Full Professor at the Research Institute for Statistics and Information Science (RISIS) at the University of Geneva, Switzerland. He holds dual PhDs in Statistics from Bocconi University (2007) and Economics from Università della Svizzera italiana (2011). His academic journey includes roles as an Assistant Professor at the University of St. Gallen and Monash University, and he has held visiting positions at Princeton University, the University of Copenhagen, and CREST (Paris). His expertise spans time series analysis, robust and semiparametric inference, financial econometrics, and spatial statistics. Key research contributions include work on saddlepoint approximations, optimal transportation methods, and latent variable models. Davide has led multiple grants, including from the Swiss National Science Foundation and the Australian Research Council. He serves as a referee for top-tier journals in statistics and econometrics. Teaching focuses on advanced statistical methods, including probability theory, time series analysis, and multivariate inference. His recent work emphasizes high-dimensional data modeling, spatio-temporal factor models, and applications to commodities trading networks. He has been honored with editorial roles and speaker invitations at global conferences, reflecting his leadership in statistical methodology.

Yili Hongمشاهده پروفایل

استاد

• Machine Learning
• Reliability Analysis
• Survival Analysis
• +۴ مورد دیگر

Yili Hong is a Professor in the Department of Statistics at Virginia Tech's College of Science. Her research focuses on statistical methodologies in reliability analysis, machine learning, survival analysis, and spatial data analysis. She holds a Ph.D. from Iowa State University (2009), with prior degrees from the same institution and the University of Science and Technology of China. Hong has been recognized with awards such as the ASA Physical and Engineering Sciences Award (2017) and the DuPont Young Professor Award (2011). She serves on editorial boards for journals like Technometrics and Journal of Quality Technology, emphasizing contributions to statistical reliability and big data applications. Her work bridges theoretical statistics with practical challenges in engineering and public health, including studies on Lyme disease emergence in Virginia through spatial modeling. Education: Ph.D. in Statistics, Iowa State University, 2009 M.S. in Statistics, Iowa State University, 2005 B.S. in Statistics, University of Science and Technology of China, 2004 Research interests span machine learning robustness, reliability of AI systems, degradation analysis, and statistical computing. Notable achievements include pioneering work on mixture experiments for AI algorithm evaluation and statistical methods for high-performance computing variability. Her recent projects address the reliability of autonomous systems and AI safety through recurrent event data analysis. Awards and recognitions highlight her contributions to statistical methodology and industrial applications. Editorial activities reflect her leadership in advancing statistical practice through peer-reviewed journals.

• Saddlepoint-Based Bootstrap
• Time Series Modeling
• Longitudinal Modeling
• +۳ مورد دیگر

Dr. A. Alexandre Trindade is a Professor in the Department of Mathematics & Statistics at Texas Tech University. His research focuses on developing novel statistical methodologies for complex data structures, with applications spanning finance, physics, and environmental science. He teaches graduate courses in Time Series Analysis and Nonparametric Statistics, and serves as advisor for the undergraduate Actuarial Science Minor program. His primary research areas include: Saddlepoint-Based Bootstrap (SPBB) : Methods for approximate inference in complex models with intractable distributions Time Series & Volatility Modeling : Multivariate autoregressive models, GARCH variants, and asymmetric distributions Spatial & Longitudinal Analysis : State-space models for missing data and spatial regression techniques Tail Risk Quantification : Nonparametric estimation of systemic risk measures like CoVaR Nonparametric Inference : Density estimation and signal detection for high-energy physics His publications demonstrate consistent innovation in statistical theory, particularly in developing resampling-based inference and extending time series methodologies. Collaborative projects include interdisciplinary work with petroleum engineering, nuclear science, and finance. Dr. Trindade maintains active research partnerships with national laboratories and industry, including reliability studies for Boeing and medical device research. He has developed specialized software for multivariate time series analysis and maintains public repositories for statistical computing resources.

Soutir Bandyopadhyayمشاهده پروفایل

استاد

• Spatial and Environmental Statistics
• Time Series Analysis
• Bootstrap/Resampling Methods
• +۴ مورد دیگر

Soutir Bandyopadhyay is a Professor in the Department of Applied Mathematics and Statistics at Colorado School of Mines. He holds a PhD in Statistics from Texas A&M University, an M.Stat from the Indian Statistical Institute, and a B.Sc in Statistics from St. Xavier’s College. His research focuses on spatial and environmental statistics, time series analysis, bootstrap methods, and large sample theory, with applications in energy systems, climate science, and machine learning. Recent work includes advancements in spatial extremes modeling using neural networks, risk analysis in oil and gas flowlines, and statistical frameworks for energy load forecasting. He has organized major conferences including the 2023 IISA Annual Conference and the 2024 workshop on uncertainty in civil/environmental systems. As Director of the Data Science Program at Mines since 2025, he leads interdisciplinary initiatives. Key contributions include development of the National Climate Database (NCDB), bias-correction methods for solar radiation data, and R packages like glmfitmiss for handling missing data in GLMs. His students have achieved notable recognitions, including Fulbright awards and ASA competition honors.

Hervé Cardotمشاهده پروفایل

استاد

• Functional Data Analysis
• Stochastic Algorithms
• Survey Sampling
• +۱۰ مورد دیگر

Hervé Cardot is a Professor of Statistics and team leader of the Statistics, Probability, Optimization and Control (SPOC) group at the Institut de Mathématiques de Bourgogne (IMB) , University of Burgundy. His research spans functional data analysis, stochastic algorithms for robust estimation, survey sampling methodology, and applied statistics across diverse fields. Research Interests : Functional data analysis, online principal component analysis, zero-inflated regression, survey sampling, nonparametric estimation Key Applications : Agriculture, climatology, energy consumption, food science, remote sensing, medical imaging Recent Work includes: Developing statistical frameworks for analyzing categorical trajectories using multivariate functional PCA (2025) Advancing bias-robust estimation for functional data in survey sampling (2020) Creating online stochastic algorithms for high-dimensional robust estimation (2017) Applying semi-Markov models to sensory analysis (2018) Teaching at the University of Burgundy includes courses on statistical modeling, big data analysis, and applied statistics for economics and psychology. He supervises PhD students in statistics and co-edits Statistics and Probability Letters .

Nan Zouمشاهده پروفایل

مدرس

• Time series analysis
• Extreme value theory
• Resampling-type methods
• +۳ مورد دیگر

Nan Zou is a Lecturer in Statistics at Macquarie University's School of Mathematical and Physical Sciences. She holds a Ph.D. in Mathematics with a Specialization in Statistics from the University of California San Diego (2017) and a B.B.A. in Management Science from Renmin University of China (2011). Her research focuses on statistical methodology and theory in time series analysis, extreme value theory, resampling techniques, and dynamical systems. She has collaborated internationally and received the 2023 Faculty of Science and Engineering Award for Inter-School Collaboration. Her recent work addresses bias correction in extreme value analysis, bootstrap methods for unit root testing, and multivariate time series extremes. Key contributions include reweighted madogram estimators and seasonal unit root tests under periodic variation. Current research emphasizes statistical inference for massive datasets and dynamical systems. Nan Zou actively supervises PhD and MRes students at Macquarie University, encouraging applications from eligible candidates. She has published in journals like the Annals of Statistics and Journal of Nonparametric Statistics , with a focus on methodological advancements in statistical theory.

Debjoy Thakurمشاهده پروفایل

مدرس

• Spatial Statistics
• Resampling Method
• Copula
• +۲ مورد دیگر

Debjoy Thakur is a Postdoctoral Lecturer in Statistics & Data Science at Washington University in St. Louis. He holds a PhD from the Indian Institute of Technology Tirupati, India. His research focuses on advanced statistical methodologies including spatial statistics, resampling techniques, copulas, spatial extremes, and statistical neural networks. He can be reached at his office in Jolley Hall, 4th Floor, Room 413, or via email at debjoy@wustl.edu.

Hilary McMillanمشاهده پروفایل

استاد

• Hydrology
• Water Resources Management
• Watershed Dynamics
• +۴ مورد دیگر

Hilary McMillan is a Professor of Water Resources in the Department of Geography at San Diego State University (SDSU), serving as a Master’s Program Advisor. She holds a Ph.D. (Hydrography) from the University of Cambridge (2006), an M.Res. in Science of the Environment from Lancaster University (2002), and an M.A. in Mathematics from Cambridge (1999). Her research focuses on hydrology and water resources, particularly in understanding watershed dynamics across pristine and urban landscapes. She explores how water interacts with soils, vegetation, human activities, and landscapes to inform predictive hydrologic models. Her work spans field hydrology, hydrologic modeling, environmental isotopes, and remote sensing. Notable contributions include developing hydrologic models for large-scale predictions, flood forecasting collaborations in New Zealand, and soil moisture tracking in France. From 2015–2017, she chaired the IAHS Panta Rhei hydrological decade, addressing societal and environmental changes in hydrology. Current research includes urban watershed studies in San Diego, using methods like hydrologic process mapping and isotope tracking. She actively collaborates globally and leads the McMillan Hydrology Lab at SDSU, part of the Watershed Science Institute and Blue Gold Area of Excellence. Courses taught include Environmental Hydrology, Hydrology and Global Environmental Change, and Practical Hydrologic Modeling. Hilary’s publications emphasize global hydrologic patterns, socio-hydrology, and urban water management. Her lab welcomes graduate and undergraduate students in field hydrology, modeling, and environmental isotopes. Despite no explicitly listed awards, her extensive publications and leadership roles highlight her scholarly impact.

• Causal Machine Learning
• Labour Economics
• Health Economics
• +۳ مورد دیگر

Michael Lechner is a Professor of Economics at the University of St. Gallen (HSG) with a focus on Applied Microeconometrics. His work integrates causal machine learning with labor, health, and sports economics. He holds affiliations with institutions such as the Centre for Economic Policy Research (CEPR) and the Institute for the Study of Labor (IZA). Lechner earned his academic credentials from the University of Mannheim (1990-1998). His research explores topics like policy evaluation, treatment heterogeneity, and the socioeconomic impacts of sports and health interventions. He has published extensively on causal machine learning frameworks and their applications in public policy. His editorial roles include co-editing Empirical Economics (2014-2017) and serving on editorial boards of journals like German Economic Review and Econometric Reviews . He is a member of the German National Academy of Sciences Leopoldina, recognizing his contributions to economic research. Lechner’s recent work emphasizes causal machine learning to analyze labor market policies, sports participation effects, and pandemic-driven market shifts. His research bridges econometric rigor with modern computational techniques to address real-world policy challenges.

Dimitris N. Politisمشاهده پروفایل

استاد

• Time Series Analysis
• Nonparametric Statistics
• Bootstrap Methods
• +۶ مورد دیگر

Dimitris N. Politis is a Distinguished Professor in the Department of Mathematics and the Halicioglu Data Science Institute at the University of California, San Diego. He holds the prestigious Halicioglu Data Science Institute Chancellor's Endowed Chair II position and has been affiliated with UCSD since 1997, progressing from Associate Professor to his current distinguished position. His educational background includes a Ph.D. in Statistics from Stanford University (1990), along with multiple master's degrees in Statistics, Mathematics, and Computer and Systems Engineering from Stanford and Rensselaer Polytechnic Institute. Professor Politis's research focuses on advanced statistical methodologies, with particular expertise in: Time Series and Random Fields analysis Computer-Intensive Methods in Statistics Resampling and Subsampling for Dependent Observations Spatial Statistics and Point Processes Nonparametric Spectral and Probability Density Estimation Model-free Prediction and Regression Information Theory and Signal Processing Econometric Analysis of Financial Time Series His scholarly output includes over 100 journal papers and several influential books, most notably "SUBSAMPLING" (1999), "MODEL-FREE PREDICTION AND REGRESSION" (2015), and "TIME SERIES: A FIRST COURSE WITH BOOTSTRAP STARTER" (2020), which has become a key educational resource in the field. Professor Politis has received numerous prestigious awards and honors: Guggenheim Fellowship (2011) Fellow of the American Statistical Association (2011) Fellow of the Institute of Mathematical Statistics (2004) Distinguished Author Award from the Journal of Time Series Analysis (2020) Econometric Theory Multa Scripsit Award (2013) Tjalling C. Koopmans Econometric Theory Prize (2012) He has been principal investigator on numerous NSF and NIH grants, including current funding for "Computer-intensive methods for dependent and complex data" (NSF DMS 24-13718, 2024). Professor Politis has held significant leadership roles, including serving as Chair of the Faculty Council of the Halicioglu Data Science Institute (2019-2023) and Associate Director (Founding) of the Institute (2018-2024). As a co-founder of the International Society for NonParametric Statistics, Professor Politis has made substantial contributions to the organization of major conferences and workshops in his field, including the First Conference of the International Society for NonParametric Statistics in 2012. His editorial work includes serving as Co-Editor of the Journal of Time Series Analysis since 2013 and Senior Editor for the ACM/IMS Journal of Data Science since 2022.

Andee Kaplanمشاهده پروفایل

دانشیار

• Bayesian Statistics
• Computational Statistics
• Record Linkage
• +۶ مورد دیگر

Andee Kaplan is an Associate Professor in the Department of Statistics at Colorado State University. She completed her Ph.D. in Statistics at Iowa State University under advisors Dan Nordman and Steve Vardeman, a postdoc at Duke University with Beka Steorts, and holds advanced degrees in Statistics (M.S.) and Mathematics (M.A.) from Iowa State and The University of Texas at Austin respectively. Ph.D. in Statistics, Iowa State University (2017) Postdoctoral Researcher, Duke University M.S. in Statistics, Iowa State University M.A. in Mathematics, The University of Texas at Austin B.S. in Mathematics with Computing Certificate, The University of Texas at Austin Her research bridges Statistics and Computing, focusing on Bayesian Statistics, Computational Statistics, Record Linkage/Entity Resolution, Markov chain Monte Carlo methods, Spatial Resampling, Statistical Machine Learning, Interactive Statistical Graphics, and Reproducible Research. Her methodological work emphasizes scalable algorithms for complex data structures in ecology, sports science, and public health. Her recent publications highlight trends in Bayesian entity resolution for streaming data, spatial modeling of riverscapes, time-varying epidemic models, and computational methods for LiDAR data analysis. Over 2014-2025, her work spans theoretical advancements in deep learning degeneracy, practical software design for statistics, and interactive tools for network detection.

Zeinab Mashreghiمشاهده پروفایل

دانشیار

• Sampling Theory
• Nonresponse Handling
• Bootstrap Methods
• +۳ مورد دیگر

Zeinab Mashreghi is an Associate Professor in the Department of Mathematics and Statistics at the University of Winnipeg, holding this position since July 2020. Previously, she served as an Assistant Professor at the same institution from January 2016 to June 2020. She is also an Adjunct Professor in the Department of Community Health Sciences at the University of Manitoba since October 2021. Her educational background includes a Ph.D. in Statistics from Université de Montréal, an M.Sc. in Pure Mathematics from Université Laval, and a B.Sc. in Applied Mathematics from the University of Kashan, Iran. Dr. Mashreghi’s research focuses on sampling methodology , particularly addressing nonresponse , resampling methods , imputation , and variance estimation . She also develops R packages and contributes to infectious disease modelling . Currently, she holds an NSERC Grant to support student researchers in these areas. Her work bridges statistical theory and practical applications, such as analyzing gonorrhea clusters in Manitoba using spatio-temporal methods. Teaching : She instructs courses including Mathematical Statistics II, Survey Sampling I & II, and Applied Regression Analysis. Her publications** span bootstrap techniques for imputed survey data, optimization algorithms, and disease surveillance. No scientific awards explicitly mentioned .