Dr. Sanyal does research on Bayesian statistics, high-dimensional variable selection, nonparametric regression, statistical genetics, computational neuroscience, and survival analysis. His research has found applications in omics, epidemiology, public health, and neuroscience.

• Ph D in Statistics , University of Missouri (2012)
• MS in Statistics, University of Calcutta (2007)
• BS in Statistics, University of Calcutta (2004)

• Development and validation of a risk prediction model for second primary lung cancer JNCI: Journal of the National Cancer Institute 114 1 (2021), E. Choi, N. Sanyal, V. Y. Ding, R. M. Gardner, J. V. Aredo, J. Lee, J. T. Wu, T. P. Hickey, B. Barrett, T. L. Riley, L. R. Wilkens, A. N. Leung, L. L. Marchand, M. C. Tammemagi, R. J. Hung, C. I. Amos, N. D. Freedman, I. Cheng, H. A. Wakelee, S. S. Han.
• A robust test for additive gene-environment interaction under the trend effect of genotype using an empirical Bayes-type shrinkage estimator American Journal of Epidemiology 190 9 (2021), N. Sanyal, V. Napolioni, M. D. Rochemonteix, M. E. Belloy, N. E. Caporaso, M. T. Landi, M. D. Greicius, N. Chatterjee, S. S. Han.
• Tobacco Smoking and Risk of Second Primary Lung Cancer Journal of Thoracic Oncology 16 6 (2021), J. V. Aredo, S. J. Luo, R. M. Gardner, N. Sanyal, E. Choi, T. P. Hickey, T. L. Riley, W.Y. Huang, A. W. Kurian, A. N. Leung, L. R. Wilkens, H. A. Robbins, E. Riboli, R. Kaaks, A. Tjønneland, R. C. Vermeulen, S. Panico, L. L. Marchand, C. I. Amos, R. J. Hung, N. D. Freedman, M. Johansson, I. Cheng, H. A. Wakelee, S. S. Han.
• A likelihood ratio test for gene-environment interaction based on the trend effect of genotype under an additive risk model using the gene-environment independence assumption American journal of epidemiology 190 1 (2021), M. D. Rochemonteix, V. Napolioni, N. Sanyal, M. E. Belloy, N. E. Caporaso, M. T. Landi, M. D. Greicius, N. Chatterjee, S. S. Han.
• Identification of Genetic Heterogeneity of Alzheimer's Disease Across Age Neurobiology of Aging 84 (2019), M.-T. Lo, K. Kauppi, C.-C. Fan, N. Sanyal, E. T. Reas, V. S. Sundar, W.-C. Lee, R. S. Desikan, L. K. McEvoy, C.-H. Chen, Alzheimer's Disease Genetics Consortiu .
• Predicting antipsychotic response combining polygenic risk with protein-protein networks European Neuropsychopharmacology 29 (2019), K. Kauppi, O. Gani, N. Sanyal, F. Bettella, O. Smeland, O. Andreassen, C.-H. Chen.
• GWASinlps: non-local prior based iterative SNP selection tool for genome-wide association studies Bioinformatics 35 1 (2019), N. Sanyal, M.-T. Lo, K. Kauppi, S. Djurovic, O. A. Andreassen, V. E. Johnson, C.-H. Chen.

• “Boosting For Competing Risks Data With Cure Fraction” , 30th Joint UTEP/NMSU Workshop on Mathematics, Computer Science, and Computational Sciences, El Paso, October 2023. (2023), Nilotpal Sanyal.
• “Iterative Variable Selection for High-dimensional Data with Binary Outcomes” , VI International Conference "STATISTICS and its applications" 2022, Namangan, Uzbekistan, October 2022. (2022), Nilotpal Sanyal.
• “Boosting For Mixture Cure Models Using Survival Data Under Competing Risks: Application to Second Primary Lung Cancer Data”, Joint Statistical Meetings (JSM), August 2021. (2021), Nilotpal Sanyal.
• “A Robust Test for Additive Gene-Environment Interaction Under the Trend Effect of Genotype Using an Empirical Bayes-Type Shrinkage Estimator”, Joint Statistical Meetings (JSM), August 2020. (2020), Nilotpal Sanyal.
• “Novel methods for parsimonious SNP selection in GWAS and robust SNP-by- environment interaction tests”, Quantitative Sciences Unit (QSU), Stanford Center for Biomedical Informatics Research (BMIR), October 2019. (2019), Nilotpal Sanyal.
• “Robust statistical tests for gene-by-environment interaction using genome-wide association study data”, Stanford Center for Biomedical Informatics Research (BMIR), November 2019. (2019), Nilotpal Sanyal.

• Efficient variable selection methods for ultra-high-dimensional data based on the structured screen-and-select strategy

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