جست‌وجوی استاد و پژوهشگر

Abbas Khaliliمشاهده پروفایل

استاد

• High-dimensional statistics
• Distributed learning in big data
• Neural Networks
• +۵ مورد دیگر

Abbas Khalili is a Professor in the Department of Mathematics and Statistics at McGill University, Montreal. His research focuses on statistical methodology in data science, particularly high-dimensional statistics, distributed learning in big data, latent variable models (e.g., finite mixtures, hidden Markov models), and time series analysis. He holds a PhD from the University of Waterloo and BSc/MSc degrees from Isfahan University of Technology. Education: PhD in Statistics, University of Waterloo (supervised by Jiahua Chen) MSc in Mathematical Sciences, Isfahan University of Technology BSc in Mathematical Sciences, Isfahan University of Technology His research interests include post-selection inference, neural networks, sparse network analysis, and change point detection. His work has been funded by NSERC (Canada) and Fonds de recherche du Québec-Nature et technologies. Recent articles focus on mixture models, regularization in autoregressive systems, and hub structure analysis in networks. His work bridges theoretical advancements with practical applications in drug discovery and network science. Dr. Khalili advises students in PhD, MSc, and undergraduate projects in his research areas. He can be contacted at abbas.khalili@mcgill.ca .

Byeong U. Parkمشاهده پروفایل

استاد

• Nonparametric structured models
• Semiparametric inference
• Non-Euclidean data analysis
• +۲ مورد دیگر

Byeong U. Park is a Professor in the Department of Statistics at Seoul National University, College of Natural Sciences. He has held this position since 1999 and previously served as Assistant Professor (1988–1992) and Associate Professor (1992–1999) at the same university. His research focuses on nonparametric structured models, semiparametric inference, and non-Euclidean data analysis. 1982: B.Sc., Department of Computer Science and Statistics, Seoul National University 1984: M.Sc., Department of Computer Science and Statistics, Seoul National University 1987: Ph.D., Department of Statistics, University of California at Berkeley (supervised by Peter J. Bickel) His research spans nonparametric regression, density estimation, and analysis of complex data on manifolds. Key methodologies include smooth backfitting, local likelihood estimation, and handling of errors-in-variables. He has developed techniques for additive models, varying coefficient regression, and high-dimensional data analysis. Notable scientific awards include the 2019 Incheon Award of Science and Technology, 2018 Carver Medal from IMS, 2017 Seoul National University Research Award, and fellowships from IMS, ASA, and KAST. He has served as Editor-in-Chief for multiple journals and held leadership roles in the Bernoulli Society and ISI. 2019–2023: Vice President, International Statistical Institute 2013–2017: Elected Council member, Bernoulli Society 2002–2004: Editor-in-Chief, Journal of Korean Statistical Society As head of the Nonparametric Inference Lab at Seoul National University, he leads research on infinite-dimensional statistical models and nonparametric methods, emphasizing applications to real-world problems in economics and biomedical data.

Frédéric PASCALمشاهده پروفایل

استاد

• Statistical Signal Processing
• Machine Learning
• Adaptive Radar Detection
• +۵ مورد دیگر

Frédéric Pascal is a Full Professor at CentraleSupélec, part of the University of Paris-Saclay, and a member of the Laboratoire des Signaux et Systèmes (L2S). His research focuses on statistical signal processing, machine learning, and robust estimation techniques, with applications in radar detection, covariance matrix estimation, and information geometry. He has held roles including Coordinator of AI activities at CentraleSupélec and Head of the "Signals and Statistics" group at L2S. His academic journey includes a PhD from University Paris X – Nanterre (2006) and an HDR (2012) from University Paris-Sud. His work emphasizes adaptive signal processing in non-Gaussian environments, with contributions to robust covariance estimation, M-estimators, and applications in radar systems and biomedical signal processing. He has authored over 100 journal/conference papers and serves as an Associate Editor for IEEE Transactions on Signal Processing and Elsevier Signal Processing. Current research interests include AI transparency, data-driven methods for industry 4.0, and health-related signal analysis.

• Categorical Data Analysis
• Semiparametric Regression Modeling
• Environmental Health
• +۲ مورد دیگر

Dr. Brent Andrew Coull is a Professor of Biostatistics at the Harvard T.H. Chan School of Public Health, with a dual affiliation as Faculty Affiliate in the Department of Environmental Health. His research focuses on advanced statistical methodologies for public health applications, including Categorical data analysis Semiparametric regression modeling for complex data structures like cross-over and longitudinal studies. In environmental health, he investigates Air pollution health effects Microbial-pollutant interactions in New Bedford Harbor through NIH-funded projects. Recent articles highlight his work on PM2.5 mortality risks, heat-related hospitalizations, and environmental exposures in pregnancy outcomes. Dr. Coull serves as Associate Chair of the Biostatistics Department and leads NIH grants including R01ES028811 (Co-PI) R01ES012044 (PI) P01CA134294 (Co-PI) for statistical methods in environmental mixtures and cancer research.

Silvia Polettiniمشاهده پروفایل

دانشیار

• Bayesian Statistical inference
• Official Statistics
• Statistical Disclosure Risk Estimation
• +۵ مورد دیگر

Silvia Polettini is an Associate Professor in Statistics at the Department of Social and Economic Sciences, University of Rome "La Sapienza". She has been with the university since 2011, achieving the rank of Associate Professor in 2020. Prior to this, she served as a Researcher at the University of Naples Federico II from 2005 to 2011, and worked at the National Institute of Statistics (ISTAT) from 2000 to 2005. Her academic journey began with a Statistical Officer position at the Italian Ministry of Health from 1998 to 2000. Her educational background includes: 1994: Bachelor's Degree with honors in Statistical and Demographic Sciences, University of Rome "La Sapienza" 1999: Ph.D. in Methodological Statistics, University of Rome "La Sapienza" Silvia Polettini's research spans several critical areas in modern statistics. Her work in Bayesian Statistical inference has produced innovative approaches for handling complex data structures. She has made significant contributions to Official Statistics , particularly in methodologies for data protection and disclosure risk assessment. Her expertise in Statistical Disclosure Risk Estimation and Microdata Protection has positioned her as a leading researcher in data privacy. The development of Bayesian Semi-parametric Modeling for Small Area Estimation represents another major strand of her work, with applications across social and economic domains. Her research on Measurement Error Models addresses fundamental challenges in data quality, while her work on Latent Variable Models and Survival Analysis extends into demographic and health applications. An analysis of her recent publications reveals a clear trajectory focusing on addressing underreporting issues in sensitive social phenomena, particularly violence against women, using sophisticated Bayesian methods. Her work bridges theoretical statistical innovation with practical applications in social policy. The integration of small area estimation techniques with measurement error models represents another significant theme, enabling more accurate regional and demographic analyses despite data limitations. Professor Polettini has been actively involved in numerous research projects, including several PRIN (Progetti di Rilevante Interesse Nazionale) initiatives and international collaborations. Her leadership in projects like "ESSnet on Statistical Disclosure Control" funded by Eurostat and "Data Linkage and Anonymisation" supported by the Isaac Newton Institute demonstrates her standing in the international statistical community. She has served as principal investigator for multiple research projects focused on disclosure risk estimation, small area modeling, and addressing underreporting in social data. Her teaching responsibilities include Statistics for Political Science and International Relations students, as well as Statistics for Economic Applications for International Economic and Financial Relations students. Office hours are held on Thursdays from 10am to 12pm, available both in person and online by appointment.

Andrew J. Pattonمشاهده پروفایل

استاد

• Financial Econometrics
• Volatility Forecasting
• High-Frequency Financial Data Analysis
• +۴ مورد دیگر

Andrew J. Patton is the Zelter Family Distinguished Professor and Professor of Economics at Duke University's Trinity College of Arts & Sciences, holding these roles since 2016 and 2013 respectively. Previously, he served as Associate Professor of Economics at Duke from 2009 to 2013. Patton holds a Ph.D. and M.A. from the University of California, San Diego (2002 and 2000), and a B.Bus. from the University of Technology Sydney (1998). His research focuses on financial econometrics, particularly volatility forecasting, dependence modeling, high-frequency financial data analysis, and hedge fund dynamics. He has published extensively in top journals such as the Journal of Finance , Journal of Financial Economics , and Econometrica . His recent work includes advancements in realized semicovariances, k-means clustering for unobserved heterogeneity, and risk price variations. Patton’s 2016 Distinguished Professorship reflects his scholarly impact. His research emphasizes practical applications, such as improving volatility forecasts and addressing market anomalies’ trading costs.

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.

Fu Ouyangمشاهده پروفایل

مدرس ارشد

• Econometrics
• Applied Economics
• Panel Data Analysis
• +۵ مورد دیگر

Dr. Fu Ouyang is a Senior Lecturer (equivalent to Assistant Professor) in the School of Economics at the University of Queensland (UQ), located in Brisbane, Australia. He holds a Ph.D. in Economics from Duke University (2017) and has been affiliated with UQ since 2018. His research focuses on theoretical and applied econometrics, with expertise in semiparametric/nonparametric methods, panel data analysis, causal inference, and discrete choice models. He applies these methodologies to fields like labor, health, and industrial organization economics. Dr. Ouyang is available for academic supervision and actively publishes in top-tier econometrics journals. Education: Ph.D. in Economics, Duke University, 2017 Research Interests: Development of robust econometric methods for causal inference and high-dimensional settings Analysis of longitudinal/panel data, binary choice models, and limited dependent variables Applications in empirical industrial organization, labor, and health economics Recent Research Trends: His work emphasizes semiparametric estimation techniques, dynamic panel models with lagged dependencies, and bundle choice analysis. Notable contributions include addressing heteroskedasticity in high-dimensional binary choice models and improving inference in multinomial response frameworks. Supervision & Grants: Dr. Ouyang is available to supervise graduate students but no specific grants are listed in the provided texts. His contact details include office room 532A in the Colin Clark Building and professional social media profiles (LinkedIn).

Brent A. Johnsonمشاهده پروفایل

استاد

• Biostatistics
• Semi-parametric methods
• Missing data analysis
• +۹ مورد دیگر

Brent A. Johnson is a Professor of Biostatistics, Ophthalmology, and Neurology at the University of Rochester Medical Center. He holds joint appointments in the School of Medicine and Dentistry and leads the Biostatistics and Computational Biology Department. His research focuses on methodological advancements in semi-parametric methods, causal inference, and adaptive treatment strategies, with substantive applications in HIV/AIDS, environmental health, and clinical epidemiology. Johnson collaborates widely across disciplines, including cardiology, neurology, and global health, particularly in Sub-Saharan Africa. He directs the Biostatistics Core for the University of Rochester's Center for AIDS Research and serves as an on-call statistician for the Flaum Eye Institute. Education: PhD in Statistics from North Carolina State University (2003), MS in Biostatistics from University of Minnesota (1997), BA in Mathematics from St. Olaf College (1995). Awards include the NSF VIGRE Fellowship and Best Student Paper Award from the American Statistical Association. Research Interests: Dynamic treatment regimes, personalized medicine, missing data methods, biomarker development, and HIV prevention strategies. His lab develops statistical tools for observational studies and clinical trials, with recent work on machine learning approaches for censored outcomes and causal inference. Grants and Funding: NIH grants for HIV/AIDS research, environmental health projects, and methodological development. Collaborations include the AIDS Clinical Trials Group (ACTG), the South African TDF study, and the El Paso Asthma Cohort. Labs/Teams: Directs the Brent Johnson Lab, focusing on statistical methodology for biomedical applications. Key lab members include postdoctoral fellows like John Rice and alumni Li Li (Eli Lilly) and Xin Lu (Sanofi).

• Nonparametric function and density estimation
• Shape-restricted inference
• Constrained regression models
• +۲ مورد دیگر

Mary C. Meyer is a Professor in the Department of Statistics at Colorado State University. Her research focuses on nonparametric function and density estimation, particularly under shape restrictions, with applications across ecological modeling, survey methodology, and statistical computing. Education: Ph.D. in Statistics from the University of Michigan (1996) Research Interests: Nonparametric function and density estimation Shape-restricted inference Constrained regression models Bayesian nonparametric methods Penalized isotonic regression Statistical software development Recent Publications show a focus on constrained regression splines, domain mean estimation, and applications in forest dynamics and survey methodology. She has developed several R packages including cgam and tools for cone projections. Grants: Funded by NSF grant DMS-0905656 for research on constrained optimization algorithms. Email Contacts: meyer@stat.colostate.edu and mmeyer@stat.uga.edu

Ivana Malenicaمشاهده پروفایل

استادیار

• Causal Inference
• Machine Learning
• Nonparametric Statistics
• +۳ مورد دیگر

Ivana Malenica is an Assistant Professor of Biostatistics at the University of North Carolina at Chapel Hill's Gillings School of Global Public Health. Previously, she was a HDSI Fellow at Harvard Data Science Initiative and Postdoctoral Fellow in Statistics at Harvard University. She holds a Ph.D. in Biostatistics from UC Berkeley and a B.S. in Mathematics from Arizona State University. Her research focuses on causal inference, machine learning, nonparametric statistics, efficiency theory, and precision health. She specializes in longitudinal and structured dependent settings including adaptive sequential experiments, online learning, and reinforcement learning applications in personalized health. Her recent publications demonstrate strong methodological contributions to causal inference and machine learning, with applications spanning clinical trials, public health, genomics, and reinforcement learning. Her work consistently develops novel statistical approaches for complex data structures. Awards include: Harvard Data Science Initiative Fellowship (2022) Berkeley Wellness Letter Fellowship (2020) Wellness Scholarship in Honor of Chin Long Chiang (2019) Berkeley Institute for Data Science Moore-Sloan Fellowship (2018) She teaches graduate courses including Advanced Probability and Statistical Inference I (BIOS 760). Her computational work includes contributions to the tlverse ecosystem for causal inference in R.

• Evolutionary Biology
• Phylogenetics
• Ecology
• +۷ مورد دیگر

Luke J Harmon serves as Professor in the Department of Biological Sciences within the College of Science at the University of Idaho, with additional appointments in the Initiative for Bioinformatics and Evolutionary Studies, Institute for Interdisciplinary Data Sciences, and Office of Research and Economic Development. Education: PhD in Evolution, Ecology, and Population Biology (2005) from Washington University in St. Louis BS in Zoology (1998) from Iowa State University Research Focus: Harmon's work integrates phylogenetic comparative methods with computational biology to investigate adaptive radiations , trait evolution , and biogeographic patterns . His research bridges micro-macroevolutionary scales through innovative statistical modeling, particularly in evolutionary medicine applications and speciation dynamics. As developer of key R packages ( rwty , treeducken , SSARP ), he advances methodological frameworks for analyzing evolutionary rates and character correlations. Publication Trends: Recent work (2022-2025) demonstrates increasing emphasis on software tool development for phylogenetic analysis, with significant contributions to evolutionary oncology, fire-adapted trait evolution in conifers, and niche theory applications in adaptive radiations. His publications consistently address methodological challenges in modeling rate heterogeneity, species delimitation, and cross-scale evolutionary processes. Research Infrastructure: Harmon leads the Harmon Lab, which operates within the University of Idaho's Initiative for Bioinformatics and Evolutionary Studies. The lab focuses on computational approaches to evolutionary questions, leveraging high-performance computing resources and interdisciplinary collaborations through the Institute for Interdisciplinary Data Sciences.

Sorawoot Srisuma (Tang)مشاهده پروفایل

دانشیار

• Econometrics
• Empirical Industrial Organization
• Structural Models
• +۴ مورد دیگر

Sorawoot Srisuma (Tang) is an Associate Professor of Economics and Director of the Graduate Program at the Department of Economics, National University of Singapore. His research focuses on econometrics, particularly structural models applied to industrial organization, dynamic games, and empirical studies. PhD in Economics from the London School of Economics His research interests center on the identification and estimation of structural models, including Markov decision processes, dynamic games, auctions, and consumer search theories. These areas are explored through both theoretical econometric methods and empirical applications. Recent research trends include contributions to nonparametric estimation, dynamic discrete choice modeling, and structural analysis of market dynamics. His work bridges econometric theory with practical applications in industrial organization and behavioral economics. He has organized academic events such as the Workshop on Structural Econometrics (2023) in collaboration with Tiffany Tsai, and the Workshop on Markov Decision Processes and Reinforcement Learning (2023) with Ying Chen and Oliver Linton.

Fang Hanمشاهده پروفایل

دانشیار

• Rank-based methods
• Graph-based methods
• Statistical optimal transport
• +۱۰ مورد دیگر

Fang Han is an Associate Professor in the Department of Statistics at the University of Washington and an Adjunct Professor in Economics. He is also an Affiliated Investigator at Fred Hutchinson Cancer Research Center. His research focuses on rank-based and graph-based methods , statistical optimal transport , and nonparametric/semiparametric regression . Education: Ph.D. in Biostatistics, Johns Hopkins University (2015) - supported by Google Ph.D. Fellowship M.S. in Biostatistics, University of Minnesota B.S. in Mathematics, Peking University Research interests span rank correlation , causal inference , nonparametric regression , high-dimensional statistics , and random matrix theory . His recent work includes adaptations of Chatterjee's correlation to manifold data and rigorous analysis of matching estimators' variance. Scientific awards include Bernoulli Society New Researcher Award (2021) Google Ph.D. Fellowship (2013-2015) Margaret Merrell Award (2015) National Science Review 2015 Best Paper (2016) Teaching includes courses like Advanced Theory of Statistical Inference , Time Series Analysis , and Foundations of Machine Learning . He serves as Associate Editor for Bernoulli and editorial board member for Dependence Modeling .

Whitney Neweyمشاهده پروفایل

استاد

• Econometrics
• Machine Learning
• Causal Inference
• +۷ مورد دیگر

Whitney Newey is the Ford Professor of Economics at the Massachusetts Institute of Technology (MIT) and a Research Associate at the National Bureau of Economic Research (NBER). He holds affiliations with multiple prestigious institutions, including fellowships with the American Academy of Arts and Sciences, the Econometric Society, and the International Association of Applied Econometrics. His research focuses on advanced econometric methodologies, particularly in structural and causal parameter estimation, machine learning integration, and high-dimensional regression analysis. Dr. Newey’s academic career includes roles as co-editor of Econometrica and program co-chair for the 2005 World Congress of the Econometric Society. His work bridges theoretical econometrics and applied policy analysis, addressing topics such as taxable income elasticity, demand modeling, and semiparametric estimation techniques. Notable achievements include the 2020 American Economic Association Distinguished Fellow award and the Sloan Foundation Research Fellowship. His publications span leading journals such as Quantitative Economics , Econometrica , and Journal of Political Economy , reflecting contributions to both foundational econometric theory and practical policy evaluation. His advisory roles include mentoring through MIT’s Department of Economics, though specific advisee names are not listed in available materials. Current research emphasizes debiased machine learning applications and inference methods in complex economic models.