Research

Statistics, data science, and machine learning research

  1. Pal, S., Dutta, S., and Maitra, R. (2024) Fast matrix-free methods for model-based personalized synthetic MR imaging. Journal of Computational and Graphical Statistics. 33(3), 1109–1117. Link arXiv
  2. Pal, S., Dutta, S., and Maitra, R. (2023) Personalized Synthetic MR Imaging with Deep Learning Enhancements. Magnetic Resonance in Medicine, 89(4), 1634–1643. Link
  3. Li, D., Dutta, S., and Roy, V. (2023). Model based screening embedded Bayesian variable selection for ultra-high dimensional settings. Journal of Computational and Graphical Statistics, 32(1), 61-73.  Link arXiv
  4. Dutta, S. and Mondal, D. (2021). On the usefulness of lattice approximations for fractional Gaussian fields. Handbook of Statistics. 44, 131-154. Link arXiv
  5. Wang, R., Dutta, S., and Roy, V. (2021). A note on marginal correlation based screening. Statistical Analysis and Data Mining, 14(1), 88-92. Link arXiv
  6. Mao, X., Dutta, S. Wong. K. W. and Nettleton, D. (2020). Adjusting for spatial effects in genomic prediction. Journal of Agricultural, Biological and Environmental Statistics. 25(4), 699–718. Link, arXiv, Code
  7. Dai, F., Dutta, S. and Maitra, R. (2020). A Matrix–free likelihood method for exploratory factor analysis of high-dimensional Gaussian data. Journal of Computational and Graphical Statistics. 29(3), 675–680. Link, arXiv
    [Winner of 2020 ASA Student Paper Competition award in the Section on Statistical Computing by Fan Dai]
  8. Laha, A. K., Dutta, S. and Roy, V. (2017). A novel sandwich algorithm for empirical Bayes analysis of rank data. Statistics and Its Interface, 10(4), 543-556. Link, arXiv
  9. Dutta, S. and Mondal, D. (2016). Variogram calculations for random fields on regular lattices using quadrature methods. Environmetrics, 27(7), 380-395. Link
  10. Dutta, S. and Mondal, D. (2016). REML estimation with intrinsic Matérn dependence in the spatial linear mixed model. Electronic Journal of Statistics, 10(2), 2856-2893. Link
  11. Biswas, A., Dutta, S., Laha, A. K. and Bakshi, P. K. (2016). Comparison of treatments in a cataract surgery with circular response. Statistical Methods in Medical Research, 25(5), 2238-2249. Link
  12. Biswas, A., Jha, J., and Dutta, S. (2016). Modeling circular random variables with a spike at zero. Statistics & Probability Letters, 109, 194-201. Link
  13. Biswas, A., Dutta, S., Laha, A. K. and Bakshi, P. K. (2015). Response-adaptive allocation for circular data. Journal of biopharmaceutical statistics, 25(4), 830-842. Link
  14. Dutta, S. and Mondal, D. (2015). An h‐likelihood method for spatial mixed linear models based on intrinsic auto‐regressions. Journal of the Royal Statistical Society: Series B , 77(3), 699-726. Link
  15. Dutta, S. and Bhattacharya, S. (2014). Markov chain Monte Carlo based on deterministic transformations. Statistical Methodology,16, 100-116. Link , arXiv, Supplement
  16. Dutta, S. (2012). Multiplicative random walk Metropolis-Hastings on the real line. Sankhya: Series B, 74(2), 315-342. link

  17. Anis, M. Z. and Dutta, S. (2010). Recent tests of exponentiality against IFR alternatives: a survey. Journal of Statistical Computation and Simulation , 80 (12), 1373-1387. Link

Applications in interdisciplinary research

  1. Cook, T. M., Biswas, E., Aboobucker, S. I., Dutta, S., and Lübberstedt, T. (2024) A Cell-Based Fluorescent System and Statistical Framework to Detect Meiosis-Like Induction in Plants. Frontiers in Plant Science, 15:1386274. Link
  2. Foster, T.L., Kloiber-Maitz, M., Gilles, L., Frei, U.K., Pfeffer, S., Chen, Y.R., Dutta, S., Seetharam, A.S., Hufford, M.B. and Lübberstedt, T. (2024) Fine mapping of major QTL qshgd1 for spontaneous haploid genome doubling in maize (Zea mays L.). Theoretical and Applied Genetics, 137(5), p.117. Link
  3. Tirone, E., Pal, S., Gallus, W. A., Dutta, S., Maitra, R., Newman, J., Weber, E., Jirak, I. (2024) A Machine Learning Approach to Improve the Usability of Severe Thunderstorm Wind Reports. Bulletin of the American Meteorological Society. 105(3), E623-E638. Link
  4. Cook, T. M., Biswas, E., Dutta, S., Aboobucker, S. I., Hazinia, S., and Lübberstedt, T. (2024) Assessing data analysis techniques in a high‐throughput meiosis‐like induction detection system. Plant Methods. 20, 7. Link
  5. Cook, T. M., Daniel Isenegger, D., Dutta, S., Sahab, S., Kay, P., Aboobucker, S. I., Biswas, E., Heerschap, S., Nikolau, B. J., Dong, L., and Lübberstedt, T. (2023) Overcoming roadblocks for in vitro nurseries in plants: induction of meiosis. Frontiers in Plant Science. 14, 1204813. Link
  6. Zheng, Z., Guo, B., Dutta, S., Roy, V., Liu, H., and Patrick S. S. (2023) The 2020 derecho revealed limited overlap between maize genes associated with root lodging and root system architecture. Plant Physiology. 192(3), 2394–2403. Link
  7. Trentin, H. U., Yavuz, R., Dermail, A., Frei, U. K., Dutta, S., and Lübberstedt, T. (2023) A comparison between inbred and hybrid maize haploid inducers. Plants, 12, 1095. Link
  8. Chiteri, K. O., Chiranjeevi, S., Jubery, T. Z., Rairdin, A., Dutta, S., Ganapathysubramanian, B., and Singh, A. (2023) Dissecting the genetic architecture of leaf morphology traits in Mungbean (Vigna radiata (L) Wizcek) using genome-wide association study. The Plant Phenome Journal, 6(1), e20062. Link
  9. Carley, C., Zubrod, M. J., Dutta, S., and Singh, A. (2023). Using machine learning enabled phenotyping to characterize nodulation in three early vegetative stages in soybean. Crop Science, 63(1), 204–226. Link
  10. Rairdin, A., Fotouhi, F., Zhang, J., Mueller, D. S., Ganapathysubramanian, B., Singh, A. K., Dutta, S., Sarkar, S., and Singh, A. (2022). Deep learning-based phenotyping for genome-wide association studies of sudden death syndrome in soybean.  Frontiers in Plant Science. 13, 966244. Link
  11. Trentin, H.U., Batîru, G., Frei, U. K., Dutta, S., and Lübberstedt, T. (2022) Investigating the effect of the interaction of maize inducer and donor backgrounds on haploid induction rates. Plants, 11(12), 1527. Link
  12. Yu, W., Hall, S. J., Hu, H., Dutta, S., Miao, Q., Wang, J., Kang, H. (2022) Chronic nitrogen deposition drives microbial community change and disrupts bacterial-fungal interactions along a subtropical urbanization gradient. Soil Biology and Biochemistry, 169, 108676. Link
  13. Musimwa, T. R., Molnar, T. L., Dutta, S., Dhliwayo, T., Trachsel, S., and Lee, M. (2023) Phenotypic Assessment of Genetic Gain from Selection for Improved Drought Tolerance in Semi-Tropical Maize Populations. Journal of Agronomy and Crop Science, 209(1), 71–82. Link
  14. Chiteri, K. O., Jubery, T. Z., Dutta, S., Ganapathysubramanian, B., Cannon, S., and Singh, A. (2022) Dissecting the root phenotypic and genotypic variability of the Iowa mung bean diversity panel. Frontiers in Plant Science, 12, 808001. Link
  15. Ghaisas, S., Harischandra, D.S., Palanisamy, B., Proctor, A., Jin, H., Dutta, S., Sarkar, S., Langley, M., Zenitsky, G., Anantharam, V., Kanthasamy, A., Kanthasamy, A., (2021) Chronic Manganese Exposure and the Enteric Nervous System: An in Vitro and Mouse in Vivo Study. Environmental Health Perspectives, 129(8), p. 087005. Link
  16. Leonard, S. M., Xin, H., Brown-Brandl, T., Ramirez, B. C.,Johnson, A. K., Dutta, S. & Rohrer, G. A. (2021) Effects of farrowing stall layout and number of heat lamps on sow and piglet behavior. Applied Animal Behavior Science, 239, 105334. Link
  17. Rahman, M. M., Dutta, S., and Lamshal, B. P. (2021) High-power sonication assisted extraction of soy protein from defatted soy meals: Influence of important process parameters. Journal of Food Process Engineering. 44(7), e13720. Link
  18. Leonard, S. M., Xin, H., Ramirez, B. C., Stinn, J. P., Dutta, S., Liu, K., and Brown-Brandl, T. M. (2021). Static and dynamic space usage of late-gestation sows. Transactions of the ASABE, 64(1), 151-159. Link
  19. Yu, W., Kang, H., Dutta, S., and Gao, H. (2021). Soil microbial community composition and function are closely associated with soil organic matter chemistry along a latitudinal gradient. Geoderma, 383, 114744. Link.
  20. Mondal, S., Dutta, S., Crespo-Herrera, L., Huerta- Espino, J., Braun, H. J. and Singh, R. P. (2020). Fifty years of semi-dwarf spring wheat breeding at CIMMYT: Grain yield progress in optimum, drought and heat stress environments. Field Crops Research, 250, 107757. Link
  21. Leonard, S., Xin, H., Brown-Brandl, T., Ramirez, B., Dutta, S., and Rohrer, G. (2020). Effects of farrowing stall layout and number of heat lamps on sow and piglet production performance. Animals, 10 (2), 348. Link
  22. Ghaisas,S., Langley, M. R., Palanisamy, B. N., Dutta, S., Narayanaswamy, K., Plummer, P. J., Sarkar, S., Ay, M., Huajun, J., Anantharam, V., Kanthasamy, A., Kanthasamy, A. G. (2019). MitoPark Transgenic Mouse Model Recapitulates the Gastrointestinal Dysfunction and Gut- Microbiome Changes of Parkinson’s Disease. Neurotoxicology, 75, 186-199. Link
  23. Manne, S., Kondru, N., Nichols, T., Lehmkuhl, A., Thomsen, b., Main., R., Halbur, P., Dutta, S., Kanthasamy, A., and Kanthasamy, A. (2017). Ante-mortem Detection of Chronic Wasting Disease Using Recto- anal Mucosa- Associated Lymphoid Tissues from Elk (Cervus elaphus nelson) Using Real-Time Quaking Induced Conversion Assay. Prion, 11(6), 415-430. Link

 

Preprints

  1. Wang, R., Dutta, S., and Roy, V. (2021+) Bayesian iterative screening in ultra-high dimensional settings. arXiv
  2. Dai, F., Dorman, K., Dutta, S., and Maitra, R. (2021+) Exploratory Factor Analysis of Data on a Sphere. arXiv