Subhomoy Ghosh, Ph.D., is a researcher at the National Institute of Standards an Technology (NIST), working as a statistician with Kuldeep Prasaad, Ph.D., in the Fire Research Division (FRD) of the Engineering Laboratory (EL). Ghosh works on multidisciplinary areas including statistical modeling of climate data, forecasting, bayesian analysis and spatio-temporal data mining.
He works on different aspects of statistical inversion techniques for the NIST Greenhouse Gas and Climate Science Measurement Program. Statistical modeling of different covariances in bayesian inversion and developing state-space models (e.g. Kalman Filtering etc.) are few of the nano-scale topics he works on.
He received his Ph.D. in statistics from Iowa State University in 2013 and holds a master's degree in statistics from University of Calcutta, India. Ghosh was a postdoctoral researcher with Prof. Andrew Majda at New York University Abu Dhabi, where he worked on a newly introduced spatio-temporal data analytic technique (Nonlinear Laplacian Spectral analysis or NLSA) and its linear counterpart (Singular Spectrum Analysis). These tools are very useful in extracting sources of variability (e.g. trend, seasonality) from the space-time data.