Research

I am primarily interested in methodological research of high dimensional statistical inference, mostly in the domain of multiple hypotheses testing, with applications in genomics, microbiome analyses, neuroimaging, etc. I also actively collaborate on inter-disciplinary projects that involve applications of innovative statistical methods to analyze medical data obtained from national registries and Electronic Health Records (EHR).

Publications

Nandi S., Sarkar S.K. (2024), Further results on controlling the false discovery rate under some complex grouping structure of hypotheses, Journal of Statistical Planning and Inference, 229:106094.
Do, H., Nandi, S., Putzel, P., Smyth, P., Zhong, H. (2022), A joint fairness model with applications to risk predictions for underrepresented populations, Biometrics, 79, 826– 840.

Sarkar, S.K., Nandi, S., (2021), On the development of a Local FDR based approach to testing two-Way classified hypotheses. Sankhya B, 83, 1–11.
Nandi, S., Sarkar S.K., Chen X., (2021), Adapting to one- and two-way classified structures of hypotheses while controlling the false discovery rate, Journal of Statistical Planning and Inference, 215, 95-108.

Mukhopadhyay, S., Nandi, S., (2017), LPiTrack: Eye Movement Pattern Recognition Algorithm and Application to Biometric Identification. Machine Learning, 107(2), 313-331.

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