جستجوی استاد راهنمای دانشگاهی

Marina Vannucciمشاهده پروفایل

استاد

• Bayesian statistics
• Variable selection
• Graphical models
• +۵ مورد دیگر

Marina Vannucci is the Noah Harding Professor of Statistics at Rice University, with an adjunct appointment at the UT MD Anderson Cancer Center. She holds a Ph.D. and Laurea in Mathematics from the University of Florence, Italy. Her research focuses on Bayesian statistical methods for complex problems in genomics, neuroimaging, and engineering. She has supervised 31 Ph.D. students and 13 postdocs, published over 185 papers, and received prestigious awards including the Mitchell Prize, Zellner Medal, and Don Owen Award. She has served as Editor-in-Chief of Bayesian Analysis and co-Editor of the Journal of the American Statistical Association. Education: Ph.D. in Statistics (University of Florence, 1996), Laurea in Mathematics (University of Florence, 1992). Research Interests: Bayesian statistics, variable selection, graphical models, statistical computing, applications in genomics, neuroscience, and engineering. Awards: Includes Fellowships from ASA, IMS, AAAS, ISBA, and the 2020 Zellner Medal. Recent recognitions include the 2025 Don Owen Award for excellence in research and contributions to the statistical community. Grants/Advising: Over 30 Ph.D. students and 13 postdocs trained. Key roles include Department Chair (2014–2019) and President of the International Society for Bayesian Analysis (2018). Labs/Teams: Affiliated with Rice Neuroengineering, Ken Kennedy Institute, and the W.M. Keck Center for Interdisciplinary Bioscience Research.

Emese Lazarمشاهده پروفایل

استاد

• Risk Measurement
• Model Risk
• Financial Econometrics
• +۴ مورد دیگر

Professor Emese Lazar serves as Professor of Finance and Deputy School Director of Teaching and Learning at the ICMA Centre, Henley Business School, University of Reading. She joined the institution in 2005 and is an active member of the Econometrics with Data Science research cluster, focusing on quantitative finance applications. Her educational background includes: PhD in Finance from the University of Reading BSc in Finance and Banking from the University of Economic Studies, Bucharest BSc in Computer Science from the University of Bucharest Research interests span risk measurement and management , model risk , financial econometrics , derivatives pricing , green finance , climate risk in finance , and machine learning applications . Her work bridges theoretical finance with practical risk management challenges, particularly in climate-related financial risks and algorithmic risk modeling. Recent publications reveal a pronounced shift toward integrating climate risk metrics with traditional financial models and developing neural network-based forecasting for tail risk measures. Her research demonstrates consistent innovation in volatility modeling, model risk quantification, and climate finance applications across top-tier journals. Professor Lazar teaches postgraduate modules in Market Risk and Climate Change and Risk Management, supervising PhD students in her specialized research areas. She actively contributes to the Econometrics with Data Science research cluster, fostering interdisciplinary collaboration between finance, data science, and climate risk modeling.

Feng Chenمشاهده پروفایل

دانشیار

• Nonparametric and semiparametric statistics
• Point processes and self-exciting processes
• Statistical computing
• +۳ مورد دیگر

Associate Professor Feng Chen is a faculty member at the School of Mathematics & Statistics, University of New South Wales, specializing in statistical methodology development and applications. His research bridges theoretical statistics and practical implementations across financial modeling, spatiotemporal processes, and public health analysis. PhD in Statistics from University of Hong Kong (2008) MSc in Applied Probability & Statistics from Lanzhou University (2004) BSc in Mathematics from Lanzhou University (2001) Research focuses include: Nonparametric and semiparametric statistical methods Point process modeling with emphasis on Hawkes processes Statistical computing and algorithm development Applications to financial data, earthquake analysis, and public health Recent publications demonstrate methodological advances in: Hawkes process estimation with complex data structures Renewal process applications in seismology GARCH modeling with missing data Spatiotemporal clustering analysis Scientific recognition includes: UNSW Science Staff Impact Award (2023) Professional roles: Director of Research Postgraduate Studies (2023--) Associate Editor for multiple journals Statistics Honours Coordinator (2013-2018) Active participant in statistical societies

Bryan S. Grahamمشاهده پروفایل

استاد

• Econometrics
• Social Networks
• Inequality Measurement
• +۴ مورد دیگر

Bryan S. Graham is a Professor in the Department of Economics at the University of California, Berkeley, where he has held faculty positions since 2005, progressing from Assistant Professor to his current full Professor rank in 2017. His research focuses on the econometrics of social interactions and networks, with particular expertise in measuring the effects of stratification on inequality. He is an active member of the Inequality: Measurement, Interpretation, and Policy (MIP) Network and contributes significantly to the field of network econometrics. Professor Graham's educational background reflects exceptional academic achievement: B.A. in Quantitative Economics from Tufts University (1997), Summa Cum Laude, Phi Beta Kappa M.Phil. in Economics from Oxford University (2000) as a Rhodes Scholar Ph.D. in Economics from Harvard University (2005) His research program bridges theoretical econometric advances with practical applications to economic policy questions. Graham has pioneered methods for analyzing network data, identifying social interactions, and measuring segregation effects. His work appears in top journals including Econometrica, Review of Economic Studies, and Journal of Econometrics. He has authored the influential Handbook of Social Economics chapter on network econometrics and co-edited the comprehensive volume 'The Econometric Analysis of Network Data' (2020). Professor Graham's publications reveal a consistent focus on developing methodological tools for analyzing social networks and peer effects, with increasing sophistication in handling complex network structures and addressing identification challenges. His recent work has expanded into practical applications in education policy and spatial inequality while maintaining theoretical rigor in econometric methodology. His scientific contributions have been recognized through prestigious awards including election as Fellow of the International Association of Applied Econometrics (2023) and multiple competitive National Science Foundation grants totaling over $1 million. Early career recognition included the Rhodes Scholarship, Fulbright Scholarship, and National Science Foundation Graduate Fellowship. As an educator, Graham teaches undergraduate and graduate econometrics courses at UC Berkeley and has developed specialized short courses internationally on econometric methods for social spillovers and network data. He has served on numerous editorial boards including as Co-Editor of the Review of Economics and Statistics (2014-2019) and has organized major conferences including the Berkeley-Stanford Econometrics Jamborees. Beyond academia, he maintains active research affiliations with the National Bureau of Economic Research, Center for Evaluation and Development at the University of Mannheim, and the Human Capital and Economic Opportunity Working Group at INET.

• Discriminant Analysis
• Inference based on Data Depth
• Characterization of Multivariate Distributions
• +۱ مورد دیگر

Subhajit Dutta is an Associate Professor in the Department of Mathematics and Statistics at the Indian Institute of Technology Kanpur. He has established himself as a notable researcher in specialized statistical methodologies with publications in top-tier statistical journals. Dr. Dutta completed his PhD in Statistics from the Indian Statistical Institute (ISI), Kolkata in 2013 under the supervision of Prof. Probal Chaudhuri. His academic journey includes an M.Sc. in Statistics from IIT Kanpur (2007) and a B.Sc. in Statistics from Presidency College, University of Calcutta (2005). He also pursued post-doctoral research at KAUST with Prof. Marc G. Genton. His research focuses on advanced statistical methodologies, particularly in Discriminant Analysis, Inference based on Data Depth, Characterization of Multivariate Distributions, and Classification of Sequence Data. His work bridges theoretical statistics with practical applications, developing robust methods for complex data analysis problems across various scientific domains. Dr. Dutta's publication record shows consistent progression from foundational properties of statistical depth functions to practical applications in classification and sequence analysis, demonstrating both theoretical depth and practical relevance in his scholarly contributions. As a faculty member at IIT Kanpur, one of India's premier technical institutions, Dr. Dutta contributes to both teaching and research in the Department of Mathematics and Statistics, helping to advance statistical science education and methodology development.

• Survival Analysis
• Nonparametric Regression
• Semiparametric Regression
• +۴ مورد دیگر

Ingrid Van Keilegom is a Full Professor at the Faculty of Economics and Business (FEB) at KU Leuven. She is a member of the LISS – KU Leuven Institute of Sports Science and serves as a program director for the POC Master in Statistics. Senior academic staff member of FEB Council and Campus Council Observer in Faculty of Science Council Member of LStat General Assembly Her research focuses on Survival Analysis and Nonparametric Regression , with expertise in: Dependent Censoring Cure Models Measurement Error Correction Quantile Regression Multivariate Statistical Inference Causal Surrogacy Metrics Recent publications emphasize copula-based methods, quantile regression for censored data, and applications to biomedical and economic forecasting. Key projects include instrumental variable approaches for endogeneity in cure models, bankruptcy prediction via machine learning, and measurement error modeling in multivariate settings. Notable affiliations: Centre for Efficiency and Productivity Analysis Collaborations in pharmaceutical statistics and actuarial science

Yinqiu Heمشاهده پروفایل

استادیار

• High-dimensional statistical inference
• Large-scale data analysis
• Mediation pathway analysis
• +۵ مورد دیگر

Yinqiu He is an Assistant Professor in the Department of Statistics at the University of Wisconsin-Madison. They hold affiliations with the School of Computer, Data & Information Sciences and the Data Science Institute at Columbia University (2021-2022 postdoc). Their research focuses on developing statistical methodologies for high-dimensional and complex data, with applications in genomics, metabolomics, and network analysis. Key areas include mediation pathway analysis, asymptotic theory for U-statistics, and functional connectivity modeling. Education includes a B.S. in Statistics from the University of Science and Technology of China (2016) and a Ph.D. in Statistics from the University of Michigan-Ann Arbor (2021), advised by Professors Gongjun Xu and Xuming He. They were awarded the ProQuest Distinguished Dissertation Award (2022) and received multiple travel grants from the Institute of Mathematical Statistics and ASA. Teaching includes core Ph.D. courses like STAT 849 (Regression Analysis) and applied courses like STAT 456 (Multivariate Statistics). Current mentoring includes MS student Yuhan Zheng (now pursuing UW-Madison Ph.D.) and Xiangyi Liao (Ph.D. in Educational Psychology). Research outputs include foundational work on adaptive U-statistics testing frameworks and scalable methods for large-scale genomic data analysis. Active in methodological contributions to biostatistics, their work bridges statistical theory and computational efficiency. Recent projects involve dynamic functional connectivity estimation from fMRI data and latent space modeling in heterogeneous networks. GitHub repository 'Adaptive-U-stats' hosts open-source implementations of high-dimensional testing algorithms developed in their research.

• Econometrics
• Applied Econometrics
• Nonparametric Identification
• +۳ مورد دیگر

Andrei Zeleneev is a Lecturer in Economics at University College London (UCL), Department of Economics, where he joined in 2020. He holds a PhD from Princeton University and is affiliated with CeMMAP (Centre for Microdata Methods and Practice). His research focuses on econometric methodologies, particularly addressing challenges in panel models, network models, and errors-in-variables. His work emphasizes nonparametric identification, robust estimation, and applied econometric techniques to handle unobserved heterogeneity and measurement errors. Dr. Zeleneev’s academic background includes advanced training in econometrics, and his research spans both theoretical and applied domains. He has published extensively on topics such as treatment effects in large panels, latent variable modeling, and structural econometric approaches to network analysis. His articles highlight innovations in handling complex data structures and improving the reliability of econometric inferences in settings with non-classical errors or latent factors. He maintains an active research agenda, with recent work addressing methodological advancements in semiparametric models and interactive fixed effects. Dr. Zeleneev’s professional activities include teaching at UCL and contributing to the econometrics community through publications and affiliations with leading institutions.

Sara van de Geerمشاهده پروفایل

استاد

• Mathematics for machine learning
• High-Dimensional statistics
• Empirical processes
• +۲ مورد دیگر

Sara van de Geer is a Full Professor at the Seminar for Statistics within the Department of Mathematics at ETH Zürich since 2005. She previously held academic positions at the University of Leiden, Université Paul Sabatier (Toulouse), and others. She earned a Master's (1982) and Ph.D. (1987) in Mathematics from Leiden University. Her research focuses on high-dimensional statistics, empirical processes, and mathematical foundations of machine learning. Van de Geer has received prestigious recognitions including the Van Wijngaarden Award (2016), Knight in the Order of Orange-Nassau (2015), and membership in Leopoldina (2013). She served as President of the Bernoulli Society (2015–2017) and Chair of the Seminar for Statistics at ETH Zürich. Her contributions include landmark works on statistical learning theory and high-dimensional inference, with key publications in top journals like Annals of Statistics and SIAM/ASA Journal on Uncertainty Quantification. Her academic leadership includes organizing Saint Flour Lectures, Wald Lectures (2016), and delivering plenary lectures globally. Her research bridges theoretical statistics with applied methodologies, emphasizing rigorous mathematical frameworks for modern data analysis challenges.

Ralf Wilkeمشاهده پروفایل

استاد

• Applied Econometrics
• Microeconometrics
• Labour Economics
• +۳ مورد دیگر

Ralf Wilke is a Professor in Applied Econometrics and Microeconometrics at the Copenhagen Business School , with affiliations to ZEW as a research associate. His career spans multiple institutions including the University of Leicester, University of Nottingham, and University of York. Education: Diplom-Volkswirt (Bonn University), DEA (University of Toulouse I), Dr. rer. pol. (Dortmund University) Current Roles: Professor at CBS, Research Associate at ZEW Prior Roles: Lecturer/Associate Professor at Leicester/Nottingham, Readership at York Research Focus: Specializing in econometric methods for analyzing duration data, competing risks, panel models with group structures, and applications in industrial life modeling. His work addresses complex censoring mechanisms and spatial diffusion patterns in historical economic contexts. Recent Publication Trends: Recent articles emphasize duration modeling with dependent censoring, copula-based competing risks frameworks, and structural econometric approaches to panel data. Key disciplines include computational statistics, quality engineering, and cliometrics.

Sorawoot Srisumaمشاهده پروفایل

دانشیار

• Econometrics
• Empirical Industrial Organizations
• Dynamic Discrete Choice Models
• +۳ مورد دیگر

Sorawoot Srisuma is an Associate Professor in the Department of Economics at the National University of Singapore (NUS). His research focuses on econometrics and empirical industrial organization, with particular emphasis on structural estimation, dynamic discrete choice models, and market structure analysis. He holds a PhD from the London School of Economics. Key research areas include the analysis of subjective well-being data, nonparametric identification in economic models, and the welfare effects of market frictions. His work often combines advanced econometric techniques with applications in industrial organization and development economics. Recent publications explore topics such as search models with heterogeneous agents, spatial autoregressions with varying coefficients, and the impacts of mental health policies. He is affiliated with the Institute of Operations Research and Analytics (IORA), part of NUS’s Smart Nation Research Cluster. His teaching areas include advanced econometrics, and he has contributed to methodological advancements in structural estimation, particularly in dynamic games and Markov decision processes.

Jackson Buntingمشاهده پروفایل

استادیار

• Econometrics
• Applied Microeconomics
• Structural Econometrics
• +۳ مورد دیگر

Jackson Bunting is an Assistant Professor of Economics at the University of Washington. He holds a Ph.D. in Economics from Duke University (2021) and previously served as an Assistant Professor at Texas A&M University. His research focuses on structural econometric approaches to panel data, particularly addressing unobserved heterogeneity and learning models in dynamic settings. Bunting's work emphasizes methodological advancements in dynamic discrete choice models and hypothesis testing for panel data properties like marginal homogeneity. His research interests include applied microeconomics, structural econometrics, and computational methods in economics. Notable contributions involve semiparametric identification frameworks, permutation-based hypothesis testing for dynamic games, and improving computational efficiency in structural estimation. Bunting has collaborated with scholars like Federico Bugni and Takuya Ura on foundational econometric methodologies. Recent publications address topics such as homogeneity testing in dynamic discrete games, estimation techniques for dynamic choice models with unobserved heterogeneity, and algorithmic improvements for econometric analysis. His work bridges theoretical econometrics with applied problems in labor, industrial organization, and policy analysis.

Mei-Ling Ting Leeمشاهده پروفایل

استاد

• Time-to-event data modeling
• Genomic data analysis
• Statistical distribution theory
• +۳ مورد دیگر

Dr. Mei-Ling Ting Lee is a Professor in the Department of Epidemiology and Biostatistics at the University of Maryland, College Park. She specializes in developing statistical models, notably the first hitting-time based threshold regression (TR) for analyzing time-to-event survival data, which has been extended to machine learning applications. As the Founding Editor and Editor-in-Chief of the Lifetime Data Analysis journal, she has significantly contributed to the field of time-to-event data methodologies. Her research encompasses genomic data analysis, statistical distribution theory, nonparametric methods, and applications in epidemiology. Dr. Lee has authored over 150 peer-reviewed articles and a seminal monograph, Analysis of Microarray Gene Expression Data (2004), widely used in genomic research. She has also co-edited influential books, including Lifetime Data Models in Reliability and Survival Analysis (1995) and Risk Assessment and Evaluation of Prediction (2013). Education: BS in Mathematics (National Taiwan University), MS (National Tsing Hua University), MA and PhD in Mathematics/Statistics (University of Pittsburgh). Her work focuses on integrating statistical theory with practical applications, including clinical trials, genomic studies, and public health research. She leads initiatives in statistical software development (e.g., R packages like clusrank ) and advocates for rigorous methodological standards in survival analysis.

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.