Research
Faculty Research Interests
The Departmental faculty engage in theoretical, methodological, and applied statistical research. This work is often motivated by their collaborations with researchers at Roswell Park Cancer Institute and the New York State Center of Excellence in Bioinformatics and Life Sciences as well as other health science researchers. There is ongoing involvement in medical informatics and bioinformatics, cancer research, maternal and child health, research on addictions, and epidemiology. Projects span a wide range of topics such as biosurveillance, metabonomics, microarray data analysis, pattern recognition and classification, proteonomics, statistical genetics, clinical trials to assess the efficacy of cancer treatments, epidemiologic studies of environmental risk factors, and outcomes research.
| Name | Research Interests |
|---|---|
| Andrews, Christopher | Semi and nonparametric inference,metabomics, Bayesian statistics, statistics education, and statistics in sports |
| Brady, Mark | Clinical Trials, drug development, time to failure analyses, screening trials |
| Carter, Randy | Longitudinal data methods, measurement error models, risk assessment, biostatistics, epidemiological modeling, maternal and child health epidemiology, radiation effects |
| Desu, M.M. | Nonparametric statistical methods and sample size methodology |
| Gaile, Daniel | QTL mapping, analysis of microarray and aCGH data, RH mapping, mixture models, EM algorithm, bootstrap, jackknife and permutation based inference |
| Hutson, Alan | Bioinformatics, clinical trials, computational methods and order statistics |
| Liang, Yulan | Statistical genetics/genomics (including population genetics, QTL and microrarray-gene expression) and bioinformatics; statistical learning theory, statistical pattern recognition; neural networks, data mining and machine learning; multivariate analysis, bayesian risk analysis, statistical computing and simulation, time series analysis; decision-making and optimization for data intensive mathematical modeling, reasoning and meta analysis |
| Ma, Changxing | Statistical genetics and experimental design |
| Manly, Kenneth | Genetics, genetic mapping, complex trait analysis, bioinformatics, software development |
| Miecznikowski, Jeffrey | bio-technical image analysis, array comparative genomic hybridization (aCGH) analysis, microarray analysis, nonparametric statistics, bootstrap methods, software development |
| Priore, Roger | Statistical methods for study design, modeling, and failure time analysis; applications in neurology, oncology, geriatrics, and epidemiology |
| Schmidt, Richard | Statistical computing |
| Sill, Michael W. | Adaptive designs and inference, phase I and II clinical trial development, exact methods for small sample sizes, translational research, differences between Bayesian and frequentist methods |
| Sucheston, Lara | Development of novel methods for finding genes associated with complex disease as well as the molecular and genetic causes of compex diseases. Current research interests include study design for an anallysis of gene-environment interaction, methods for correlated data and risk analysis. |
| Tian, Lili | Design of Clinical Trials, Survival Analysis, statistical genetics, skewed data analysis, analysis of medical expenditure data, cancer research, behavioral studies, health policy issues |
| Vexler, Albert | Optimal testing; Bayes factor; Optimal designs; Segmented Regression models; Censored data; Change point problems; Sequential analysis; Receiver operating characteristic curves analysis; Measurement error; Statistical epidemiology; Biostatistics; Asymptotic methods of statistics; Forecasting. Secondary: Renewal theory; Tauberian theorems; Time series; Categorical analysis; Multivariate analysis; Multivariate testing of complex hypotheses; factor and principal component analysis |
| Wilding, Gregory | Resampling techniques, goodness-of-fit tests, distributional characteristics, permutation tests, copulas, tests of independence, biostatistics |
| Yu, Jihnhee | Stochastic population models and saddlepoint approximation |
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