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Research Interests: medical image analysis, data mining, environmental sciences
Brief description of current research:
My research now broadly covers the areas of statistical computing and computationally intensive statistical methods for massive datasets, with specific application to clustering and classification, simulation and medical imaging. My current and planned solutions are built on exploiting computational resources such as modern workstations, parallel virtual machines and supercomputers, distributed and cloud computing, each of which need to be harnessed both smartly and efficiently. Most importantly, I believe that the research challenges discussed here will only become more acute with technological advances and with the development of more efficient and automated data collection methods.
Maitra, R., 2012: On the expectation-maximization algorithm for Rice-Rayleigh mixtures with application to estimating the noise parameter in magnitude MR datasets. Sankhya Series B, in press.
Maitra, R. V. Melnykov, and S. N. Lahiri, 2012: Bootstrapping for significance of compact clusters in multi-dimensional datasets. J. Amer. Stat. Assoc., 107, 378-392.
Melnykov, V., W. C. Chen, and R. Maitra, 2012: MIXSIM: An R package for simulating data to study performance of finite mixture modeling and clustering algorithms. J. Stat. Software, 51, 12.