1. A Course in Mathematical Statistics and Large Sample Theory (2016). (With Bhattacharya, R. and Patrangenaru, V.)    Springer Series in Statistics. Springer link to the book.

Research articles

  1. Bayesian high-dimensional semi-parametric inference beyond sub-Gaussian errors .pdf (with Lee, K. and Chae, M.) (2020).
          Journal of the Korean Statistical Society.

  2. Optimization of graph neural networks with natural gradient descent .pdf (with Izaldi, M., Fang, Y. and Stevenson, R.) (2020).
          IEEE BigData 2020.

  3. Community detection, pattern recognition, and hypergraph-based learning: approaches using metric geometry and persistent homology .pdf (with Nguyen, D. Q., and Xing, L.).
        Frontiers in Artificial Intelligence and Applications (2020).

  4. Bayesian Bandwidth Test and Selection for High-dimensional Banded Precision Matrices .pdf (with Lee, K. ) (2020).
          Bayesian Analysis (2020), Vol 15 (3), 737--758.

  5. Averages of unlabeled networks: geometric characterization and asymptotic behavior .pdf (with Kolaczyk, E., Rosenberg, S., Xu, J. and Jackson, W. ).
          Annals of Statistics (2020), vol. 48(1), 514-538.

  6. Intrinsic Gaussian processes on complex constrained domains .pdf (with Niu, M., Cheung, P., Dai, Z., Lawrence, N. and Dunson, D. B.)
          Journal of the Royal Statistical Society, Ser. B. (2019). vol. 81, 603--627.

  7. Minimax posterior convergence rates and model selection consistency in high-dimensional DAG models based on sparse Cholesky factors .pdf (with Lee, K. and Lee, J.)
          Annals of Statistics (2019), volume 47, Number 6, 3413--3437.

  8. Extrinsic Gaussian process models for regression and classification on manifolds .pdf (with Niu, M., Pokman, C. and Dunson. D.B.)
          Bayesian Analysis (2019), vol. 14, 907--926.

  9. Differential geometry for model independent analysis of images and other non-Euclidean data: recent development .pdf (with Bhattacharya, R. ) (2019).
          In: Sidoravicius V. (eds) Sojourns in Probability Theory and Statistical Physics - II. Springer Proceedings in Mathematics & Statistics, vol 299. Springer.

  10. Bayesian sparse linear regression with unknown symmetric error .pdf (with Chae, M. and Dunson, D.B.)
          Information and Inference (2019), vol 8 (3), 621--653.

  11. On posterior consistency of tail index for Bayesian kernel mixture models .pdf (with Li, C. and Dunson, D.B.)
          Bernoulli (2019), vol. 25(3), 1999–2028.

  12. Communication efficient parallel algorithms for optimization on manifolds .pdf (with Sarpavayeva, B. and Zhang, M.)
          Neural Information Processing Systems 2018.

  13. Robust and Parallel Bayesian Model Selection .pdf (with Zhang, M. and Lam, H.)
          Computational Statistics & Data Analysis (2018), Vol. 127, 229--247.

  14. Extrinsic local regression on manifold-valued data. pdf (with Thomas, B., Zhu, H. and Dunson, D. B.) .
       The Journal of the American Statistical Association (2017), vol. 112(519), 1261-1273.

  15. Omnibus CLTs for Fre'chet means and nonparametric inference on non-Euclidean spaces. pdf (with Bhattacharya, R.) (2017).
       Proceedings of the American Mathematical Society, vol. 145 , 413-428..
    Matlab codes for the planar shape example.
    See for the planar shape data set.

  16. Robust and scalable Bayes via a median of subset posterior measures .pdf (with Minsker, S., Srivastava, S. and Dunson, D. B.)
          Journal of Machine Learning Research (2017), 18(124):1--40.

  17. On estimating a mixture on graphons .pdf (with Mukherjee, S. and Sarkar, P.)
          Neural Information Processing Systems 2017.

  18. Bayesian nonparametric inference on the Stiefel manifold .pdf (with Rao, V. and Dunson, D. B.).
       Statistics Sinica(2017), 27, 535--553

  19. Scale and Curvature effects in principal geodesic analysis .pdf (with Lazar, D.) .
         Journal of Multivariate Analysis (2017), 153, 64--82..

  20. Borg, J.S., Srivastava, S. Lin, L. et al. Anterior cingulate and insula sub-networks signal intersubjective avoidance in rats.
         Brain and Behavior (2017), 7: e00710. DOI: 10.1002/brb3.710.

  21. Data augmentation for models based on rejection sampling .pdf(with Rao, V. and Dunson, D. B.).
       Biometrika (2016), 103(2): 319-335.

  22. Hultman, R., Mague, S., Li, Q., Katz, B.M., Michel, N., Lin, L. et al. Dysregulation of Prefrontal Cortex-Mediated Slow-Evolving Limbic Dynamics Drives Stress-Induced Emotional Pathology.  
       Neuron (2016), 91(2), 439-452.

  23. Flexible link functions in nonparametric binary regression with Gaussian process priors. (with Li, D. , Wang, X.,  and Dey, D.)
       Biometrics (2016), 72: 707--719

  24. Nonparametric benchmark dose estimation with continuous dose-response data .pdf ( with Piegorsch , W. & Bhattacharya, R.).
       Scandinavian Journal of Statistics (2015), Volume 42, 713–731.

  25. Bayesian monotone regression using Gaussian process projection (with Dunson, D.B.).
        Biometrika (2014), 101 (2): 303--317.

  26. Scalable and Robust Bayesian Inference via the Median Posterior (with Minsker, S., Srivastava, S. and Dunson, D.B.).
        ICML, 2014

  27. Benchmark dose analysis via nonparametric regression modeling (with Piegorsch, W., Xiong, H., & Bhattacharya, R.).
        Risk Analysis (2014), Volume 34, Issue 1, 135--151.

  28. Recent progress in the nonparametric estimation of monotone curves -with applications to bioassay and environmental risk assessment (with Bhattacharya, R.)
       Computational Statistics & Data Analysis (2013), vol 63, 63--80.

  29. Problem of ruin and survival in economics: application of the central limit theorem, large deviation and Markov processes (with  Bhattacharya, R. Majumdar, M.).
        Sankhya (2013), Volume 75-B, Part 2, pp. 145--180.

  30. Nonparametric estimation of benchmark doses in environmental risk assessment (with Piegorsch, W., Xiong, H., & Bhattacharya, R.).
        Environmetrics (2012), 23(8), 717--728.

  31. Nonparametric benchmark analysis in risk assessment: a comparative study by simulation and data analysis (with Bhattacharya, R.).
        Sankhya, ser. B (2011), volume 73, Issue 1 , 144-163.

  32. An adaptive nonparametric method in benchmark analysis for bioassay and environmental studies (with Bhattacharya, R.).
         Statistics & Probability Letters (2010), volume 80, 1947-1953.

  33. Under review

  34. Optimal Bayesian estimation of Gaussian mixtures with growing number of components .pdf (with Ohn, I.) (2020).

  35. Robust optimization and inference on manifolds .pdf (with Lazar, D., Saparbayeva, B. and Dunson, D. B.) (2020).

  36. Accelerated algorithms for convex and non-convex optimizations on manifolds .pdf (with Sarpabayeva, B., Zhang, M. and Dunson, D.B.) (2020).

  37. A spectral-based framework for hypothesis testing in populations of networks .pdf (with Chen, L., Josephs. N., Zhou, J. and Kolaczyk, E. ) (2020).

  38. Bayesian classification, anomaly detection, and survival analysis using network inputs with application to the microbiome .pdf (with Josephs. N., Rosenberg, S. and Kolaczyk, E. ) (2020).

  39. Neural time-dependent partial differential equation .pdf (with Hu, Y., Zhang, T. and Xu, Z. ) (2020).

  40. Weight prediction for variants of weighted directed networks .pdf (with Nguyen, D. Q., and Xing, L.)

  41. Asymptotically Corrected Person Fit Statistics for Multidimensional Constructs with Simple Structure and Mixed Item Types .pdf (with Hong, M. and Cheng, Y.) (2020).

  42. Hypothesis testing for populations of networks .pdf (with Li, C. and Zhou, J. (2019).

  43. Change point detection in dynamic networks via graphon estimation .pdf (with Zhao, Z. and Chen, L.) (2019).

  44. Maximum pairwise Bayes factors for covariance structure testing .pdf (with Lee, K. and Dunson, D. B.) (2018).

  45. Hierarchical network item response modeling for discovering differences between innovation and regular school systems in Korea .pdf (with Jin, I., Jeon, M., and Schweinberger, M. ) (2018).

  46. Hierarchical stochastic block model for community detection in multiplex networks .pdf (with Paez, M. and Amini, A.) (2019).

  47. Exact slice sampler for Hierarchical Dirichlet Processes .pdf (with Amini, A., Paez, M. and Razaee, Z. ) (2019).

  48. Network distance based Laplacian flow on graphs .pdf (with Bao, D. and You, K.) (2018).

  49. Bounded regression with Gaussian process projection .pdf (with Zhang, J. ) (2018).

  50. Learning subspaces of different dimensions .pdf (with Thomas, B. ST., Lim, L-H., and Mukherjee, S.) (2020).

  51. A projection approach for multiple monotone regression .pdf (with Thomas, B., Piegorsch, W., Scott, J. and Carvalho, C.) (2019)