Shuchismita Sarkar
Shuchismita Sarkar has a Ph.D in Applied Statistics from University of Alabama. Her primary research interests are model based clustering, finite mixture model, hidden Markov models. Her research has been published in leading statistics and machine learning journals such as Annals of Applied Statistics, Computational Statistics and Data Analysis, Advances in Data Analysis and Classification, Pattern Recognition. She has also authored a R package. Prior to starting an academic career, Dr. Sarkar worked in credit risk analytics for several years.
Education:
- Doctor of Philosophy in Applied Statistics, University of Alabama, 2019
- Master of Science in Applied Statistics, Western Michigan University, 2008
- Master of Science in Applied Statistics and Informatics, Indian Institute of Technology, Bombay, 2002
- Bachelor of Science in Statistics, University of Calcutta, 2000
Teaching:
Data Mining (graduate and undergraduate level), Bowling Green State University, Bowling Green, OH (Spring 2020, Spring 2021 (grad only)) (Mode of instruction: Face-to-face, online synchronous)
Regression Analysis (graduate and undergraduate level), Bowling Green State University, Bowling Green, OH (Fall 2019, Fall 2020) (Mode of instruction: Face-to-face, online asynchronous)
Awards:
2020 - Classification Society Distinguished Dissertation Award (Honourable Mention). The Classification Society (award supported by Springer).
2018 - “Summer in Excellence Research Grant”, $5, 000 Award. University of Alabama.
2018 - “Jeff Kurkjian Teaching Award”, University of Alabama (awarded for best teaching performance among Applied Statistics PhD students).
Computational Statistics
Cluster Analysis
Finite mixture modeling
Hidden Markov models
Change point estimation
Sarkar, S, Zhu, X., (2022) Finite mixture model of hidden Markov regression with covariate dependence, Stat, 11(1), p.e469.
Sarkar, S, Zhu, X., (2022). Multiple change point clustering of count processes with application to California COVID data, Pattern Recognition Letters, 157, pp.83-89.
Zhu, X., Sarkar, S, Melnykov, V., (2022). MatTransMix: An R Package for matrix parsimonious models, Journal of Classification, 39(1), pp.147-170.
Sarkar, S., Melnykov, V. and Zhu, X., (2021). Tensor-variate finite mixture modeling for the analysis of university professor remuneration. Annals of Applied Statistics, 15(2), pp.1017-1036.
Melnykov, V., Sarkar, S. and Melnykov, Y., (2021). Finite mixture modeling of directed weighted multilayer networks, Pattern Recognition, 112, p.107641.
Sarkar, S., Melnykov, V. and Zheng, R., (2020). Gaussian mixture modeling and model-based clustering under measurement inconsistency. Advances in Data Analysis and Classification, pp.1-35.
Sarkar, S., Zhu, X., Melnykov, V. and Ingrassia, S., (2020). On parsimonious models in matrix data mixture modeling. Computational Statistics and Data Analysis, 142, p.106822.
Sarkar, S. and Melnykov, V. (2020) R-package netClust.
Updated: 05/22/2023 11:42AM