جستجوی استاد راهنمای دانشگاهی

Benjamin Kedem is a Professor in the Department of Mathematics at the University of Maryland, College Park, with affiliations at the Institute for Systems Research (ISR). His academic career spans several decades with significant contributions to time series analysis, spatial statistics, and statistical methodology. Dr. Kedem's research focuses on time series analysis, space-time statistical problems, and combination of information from multiple sources. His work includes significant contributions to higher order crossings (HOC), contraction mapping methods in spectral analysis, Rice formula applications, threshold methods for rainfall estimation, partial likelihood approaches, Bayesian-transformed-Gaussian spatial prediction, and statistical data fusion. His research has practical applications in environmental statistics, meteorology, and public health. His recent publications demonstrate a continued focus on semiparametric methods, statistical data fusion, and computational approaches to time series and spatial analysis. The 15 most recent articles span from 2017 back to 1994, showing both contemporary relevance and foundational contributions to the field of statistics. Dr. Kedem has directed 15 PhD dissertations, with students completing between 1983 and 2007, indicating his long-standing commitment to mentoring the next generation of statisticians. His former students include George Reed, Donald E.K. Martin, Silvia R.C. Lopes, Haralabos Pavlopoulos, James Troendle, Ta-Hsin Li, John Barnett, Konstantinos Fokianos, Victor De Oliveira, Neal Jeffries, Boris Kozintsev, Richard Gagnon, Haiming Guo, Guanhua Lu, and Shihua Wen. He has authored or co-authored several books including 'Regression Models for Time Series Analysis' (2002), 'Time Series Analysis by Higher Order Crossings' (1994), and 'Statistical Data Fusion' (2017). His teaching portfolio includes graduate courses such as STAT 730 (Time Series Analysis), STAT 740 (Linear Models I), and STAT 741 (Linear Models II).

• Time Series Analysis
• Spatial Statistics
• Spectral Analysis
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Takumi Saegusa is an Associate Professor in the Department of Mathematics at the University of Maryland, College Park, specializing in Statistics and Biostatistics. His work bridges theoretical and applied statistical methods. Rank: Associate Professor Department: Department of Mathematics University: University of Maryland, College Park His research focuses on advanced statistical techniques for survival analysis, two-phase sampling, and data integration. He develops methods to handle longitudinal covariates, heterogeneous populations, and merged datasets from multiple sources. Recent publications address confidence bands for survival curves, variable selection in threshold regression, and computational approaches for statistical packages like osDesign and tpsDesign . These works span biostatistical applications, computational methods, and theoretical foundations. He contributes to statistical software development, including R packages for two-phase study design and analysis. His work has applications in epidemiology, clinical research, and precision medicine.

• Statistics
• Biostatistics
• Survival Analysis
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Tao Wang is an Assistant Professor Adjunct in the Department of Biostatistics at Yale School of Public Health and a tenure-track Associate Professor and Principal Investigator in the SJTU-Yale Joint Center for Biostatistics and Data Science at Shanghai Jiao Tong University. His work focuses on statistical methods for high-dimensional biomedical data, especially dimension reduction, microbiome analysis, and integrative genomic modeling. Education: PhD in Statistics, Hong Kong Baptist University, 2013 MS, East China Normal University, 2010 Research Interests: Dr. Wang develops statistical and computational techniques to tackle complex, high-dimensional data arising from modern biotechnologies. His methodological portfolio includes adaptive independence tests for microbiome community data, penalized prediction models for sequencing counts, and counting-process-based dimension reduction for censored survival outcomes. He also works on Bayesian tree-structured models linking dietary nutrients to gut microorganisms and joint estimation of Gaussian graphical models over spatial and temporal domains. These interests sit at the intersection of biostatistics, bioinformatics, machine learning, and epidemiology , with applications spanning the human microbiome, epigenomics, and large-scale cohort studies. Publications at a Glance: Across six major papers (2015-2019) in Biometrics and Biometrika , Wang has consistently advanced methodology for high-dimensional count, compositional, and censored data. Themes include adaptive testing, predictive modeling, dimension reduction, and network estimation, often motivated by collaborative projects in genomics and microbiome science. Honours: Elected Member, International Statistical Institute (2020) Collaboration & Advising: He maintains an active collaboration with Yale’s Dr. Hongyu Zhao, co-authoring all showcased publications. No formal advisees are listed in the provided material. Laboratories & Teams: He leads research activities within the SJTU-Yale Joint Center for Biostatistics and Data Science, integrating resources from both Shanghai Jiao Tong University and Yale to advance data-intensive biomedical science.

• Biostatistics
• Bioinformatics
• High-dimensional data analysis
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Jason Abrevaya serves as Associate Dean for Graduate Education and holds the Murray S. Johnson Chair in Economics at the University of Texas at Austin within the College of Liberal Arts and Department of Economics. His research spans econometric methodology, applied microeconomics, and demography, with significant contributions using U.S. birth databases to study birthweight inequality, smoking effects, and gender selection practices among ethnic groups. Dr. Abrevaya's work centers on econometrics (panel data analysis, treatment effects, missing data), health economics (birth outcomes, vaccination), labor economics, and sports economics. His methodological innovations address data challenges in causal inference and microeconomic applications, characterized by rigorous statistical approaches to real-world problems. Recent publications demonstrate continued emphasis on theoretical econometrics with empirical applications in public health and sports analytics. His article trends reveal sustained focus on treatment effects under heteroskedasticity, partial effects in nonlinear models, and applications to vaccine uptake and sports (e.g., penalty calls in hockey). Methodological contributions frequently bridge theoretical econometrics with empirical practice in health and social sciences. Dr. Abrevaya has secured research funding from the National Science Foundation and Robert Wood Johnson Foundation. He is author of the textbook Probability and Statistics for Economics and Business: An Introduction Using R . No scientific awards are explicitly mentioned in available sources. As Associate Dean for Graduate Education, he oversees graduate programs while maintaining active research and mentorship. His warning about scam impersonation attempts (via Gmail addresses posing as research opportunities) underscores the importance of verifying communications through his official university email.

• Econometrics
• Applied Microeconomics
• Demography
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Mehrdad Naderi is a Lecturer in Statistics at the Department of Mathematics, Physics, and Electrical Engineering , Northumbria University . His academic journey includes a PhD in Mathematical Statistics from Shahid Bahonar University of Kerman (2017) and postdoctoral research at National Chung Hsing University (Taiwan), Ferdowsi University of Mashhad (Iran), and University of Pretoria (South Africa). Education: PhD in Mathematical Statistics, Shahid Bahonar University of Kerman (2017) His research focuses on applied statistical inference with emphasis on classification , cluster analysis , factor analysis , finite mixture models , and EM algorithm for robust estimation. He has contributed to multivariate and matrix-variate analysis, particularly in handling outliers and asymmetrical data structures. Recent work includes three-way data clustering using matrix-variate normal distributions and robust Bayesian inference for censored mixture models. His publications demonstrate expertise in distribution theory, statistical computation, and applications to financial data, environmental modeling, and astrophysics. Current collaborations span multiple institutions, focusing on heavy-tailed distributions and computational methods for complex data structures.

• Classification
• Cluster Analysis
• Finite Mixture Models
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Qiankun Zhou is a Professor of Economics at Louisiana State University, holding the prestigious Freeport-McMoran Endowed Professorship in Economics #2. Based in the Business Education Complex South (2321) in Baton Rouge, LA, he can be contacted at qzhou@lsu.edu or 225-578-5211. Professor Zhou earned his PhD in Economics from the University of Southern California in 2015. His academic journey reflects rapid advancement to full professorship, underscored by his endowed chair position. His research program bridges theoretical and applied econometrics with exceptional focus on panel data methodologies. Key contributions include breakthroughs in treatment effects estimation with interactive fixed effects, high-dimensional inference through orthogonal projection, and specification testing for time-varying coefficient models. His work consistently addresses the tension between methodological rigor and practical applicability in economic analysis. Recent publications (2023-2025) reveal intensified exploration of causal inference frameworks and high-dimensional data challenges, with multiple papers in Journal of Econometrics and Econometric Theory . This output demonstrates sustained theoretical innovation while maintaining strong empirical relevance. His primary distinction is the Freeport-McMoran Endowed Professorship in Economics #2, recognizing exceptional research contributions at LSU. Professor Zhou maintains active research collaborations across institutions including USC, Florida State University, and international partners. His advising likely emphasizes mathematical rigor coupled with real-world application, preparing students for research careers through co-authorship opportunities on methodological advances. The absence of a named lab reflects economics' typical research structure where theoretical work occurs through individual and small-group collaboration.

• Theoretical Econometrics
• Applied Econometrics
• Panel Data Econometrics
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Denis Vitalievich Belomestny is a Leading Researcher at the Faculty of Computer Science of the National Research University Higher School of Economics (HSE) in Moscow. He is affiliated with the Institute of Artificial Intelligence and Digital Sciences and the International Laboratory of Stochastic Algorithms and Multidimensional Data Analysis. Belomestny joined HSE in 2014 and has accumulated 11 years of scientific and teaching experience. His academic background includes a Candidate of Physical and Mathematical Sciences degree from Lomonosov Moscow State University (2002) and a specialty in Applied Mathematics from the same university (1998). Belomestny's research focuses on nonparametric statistics , statistics of random processes , numerical methods of stochastics , and financial mathematics . His work demonstrates a strong interdisciplinary approach bridging theoretical mathematics with practical applications in machine learning and artificial intelligence. He has made significant contributions to the analysis of stochastic differential equations, particularly McKean-Vlasov type models, and has developed innovative methods combining deep learning with traditional statistical techniques. His recent publication record shows a clear trend toward integrating deep neural networks with classical stochastic analysis. The 15 most recent articles reveal a growing emphasis on applying machine learning techniques to solve complex problems in statistical inference, particularly in areas like variance reduction, optimal stopping, and density estimation. His research spans both theoretical foundations and practical applications, with increasing focus on the intersection of probability theory and modern AI methodologies. Academic Work Allowance (2020–2021, 2019–2020) Bonus for an article in a foreign peer-reviewed scientific publication (2016–2018) Best Teacher of 2019 Belomestny has been actively involved in advising students and junior researchers, with numerous co-authored publications indicating his mentorship role. He is a key member of the International Laboratory of Stochastic Algorithms and Multidimensional Data Analysis, which received a significant grant from the Russian Science Foundation in 2018 (project No. 18-11-00132) led by Alexey Naumov, with Belomestny as a principal investigator. The laboratory focuses on high-dimensional data analysis and modern stochastic algorithms, positioning Belomestny at the forefront of research in these rapidly developing fields.

• nonparametric statistics
• Statistics of random processes
• numerical methods of stochastics
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Professor Vladimir Spokoiny is a research faculty member at the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) in Berlin, Germany, holding the academic rank of Professor. He maintains active leadership in multiple German Research Foundation (DFG)-funded projects including principal investigator roles and sub-project management positions within Collaborative Research Centers. His institutional footprint extends to the MATH+ Berlin Mathematics Research Center Cluster of Excellence. His research expertise spans foundational and applied statistics with particular emphasis on: Adaptive smoothing methodologies for imaging and time series Semiparametric regression and structural analysis High-dimensional non-stationary time series modeling Bayesian frameworks for stochastic differential equations Sparse approximation techniques in reinforcement learning Hypothesis evaluation in complex ecological systems Professor Spokoiny demonstrates sustained research productivity through continuous DFG funding across multiple mechanisms including individual grants, priority programs, and collaborative research centers. His participation in Graduate Schools GRK 251 and GRK 1792 indicates active engagement in doctoral training. Current projects reveal strategic focus on computational statistics for machine learning and stochastic systems, while historical projects show consistent contributions to adaptive data analysis methodologies with cross-domain applications.

• Statistics
• Machine Learning
• Stochastic Processes
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