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Professor Mayetri Gupta specializes in the development of novel statistical methodologies, particularly within the Bayesian framework, to address scientific problems in the fields of computational biology and genetics. Her research primarily focuses on the detection of sparse signals in noisy discrete data, which is a significant challenge in genomic data analysis due to latent positional and structural constraints. Gupta's interests encompass Bayesian statistical modeling in areas such as epigenetics, genome-wide association studies (GWAS), and single-cell transcriptomics. She is actively involved in projects that develop innovative statistical methods for modeling and predicting chromatin structure and deciphering regulatory networks of transcription factors. Her expertise extends to integrating various types of genomic data to derive robust and meaningful biological inferences. Furthermore, Gupta's work involves general Bayesian modeling and employs Markov chain Monte Carlo methodology along with clustering, classification, and model selection techniques tailored for complex, high-dimensional correlated data. She also applies regression mixture models and hidden Markov models, with applications in biology, medicine, and image analysis.