Ph.D., 2012, University of Guelph
Office: Whitney Hall, Room 128 (WH-128)
Professor Dang's primary research focus is on computational statistics with applications in bioinformatics. Most of her work focuses on clustering and classification algorithms suitable for high-dimensional data such as RNA-Seq data, microarray data, and microbiome data. Some of her work also involves methodological development for genome wide association studies.
Ph.D., 2008, New York University
Office: Whitney Hall, Room 136 (WH-136)
Professor Vladislav Kargin's main research interests are in random matrices and its applications, and in particular, statistical analysis of large data, zeroes of zeta functions, statistical mechanics of random media and free probability.
Ph.D., 2009, University of Southern California
Office: Whitney Hall, Room 129 (WH-129)
Professor Aleksey Polunchenko's main research interests are sequential analysis and quickest change-point detection. A change-point detection procedure tries to identify times when the probability distribution of a stochastic process (or time series) changes. It is often used in manufacturing (in quality control), intrusion detection, spam filtering, website tracking and medical diagnostics. Much of Polunchenko's work can be directly applied to real world applications such as cyber security.
Ph.D., 2010, University of North Carolina at Chapel Hill
Office: Whitney Hall, Room 134 (WH-134)
Professor Xingye Qiao's main research interest focuses on statistical machine learning, a rapidly growing area of research. Statistical machine learning refers to data mining, (statistical) inference and prediction. Compared to the more traditional mathematical statistics, it has an advantage when dealing with high-dimensional data, data with special structures and massive data. Many techniques in statistical machine learning have become essential in big data analytics. Qiao's recent works include novel large-margin based classification methods, the classification stability, the subsampling strategy for massive and high-dimensional data sets, and learning data with special structures.
Ph.D., 1983, Michigan State University
Office: Whitney Hall, Room 135 (WH-135)
Professor Anton Schick's main research interests involve large sample theory and semiparametric models. In particular, he has studied uses of large sample theory in statistics, the characterization and construction of efficient estimators and tests for semiparametric and nonparametric models, statistical inference for Markov chains and stochastic processes, estimation and comparison of curves, and the behavior of plug-in estimators, among many other works.
Ph.D., 2011, University of Wisconsin-Madison
Office: Whitney Hall, Room 131 (WH-131)
Professor Zuofeng Shang's research interests include nonparametric and semiparametric statistics, Bayesian methods, big data inference and spatial statistics. Shang is a member of the health science TAE (Transdisciplinary Areas of Excellence), which integrates cutting-edge biomedical research with science, technology, mathematics, and engineering.
Ph.D., 2011, Texas A&M University
Office: Whitney Hall, Room 133 (WH-133)
Professor Ganggang Xu's main research interests include post-model selection inference, model selection and model averaging, quantile regression, spatial statistics with large data sets, Gaussian and non-Gaussian random fields, etc. Often these problems are motivated by real challenges in analyzing real data from various fields, such as geostatistical data. Xu is a member of the smart energy TAE (Transdisciplinary Areas of Excellence), which advances energy-related research and scholarship at Binghamton University.
Ph.D., 1986, University of California at Los Angeles
Office: Whitney Hall, Room 132 (WH-132)
Professor Qiqing Yu's main research interests include survival analysis, statistical decision theory and statistical modeling for genomic data. Survival analysis is an important statistical tool in engineering (in quality control), actuarial science (such as in life tables), and in clinical trials and medical research. In particular, Yu has studied modeling the interval-censored data, consistency and asymptotic normality of the generalized maximum likelihood estimator of survival function, and those of the semiparametric estimator under linear regression models.
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