Scholars@Duke

• Background
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  Education, Training, & Certifications

  • Ph.D., Purdue University 2006
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  Duke Appointment History

  • Director or Graduate Studies in Statistical Science, Statistical Science, Trinity College of Arts & Sciences 2016 - 2018
  • Co-Director of Graduate Studies in the Department of Statistical Science, Statistical Science, Trinity College of Arts & Sciences 2013 - 2016
  • Assistant Professor of Statistical Science, Statistical Science, Trinity College of Arts & Sciences 2009 - 2016
• Recognition
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  In the News

  • JUL 18, 2018

    Neurons Can Carry More Than One Signal at a Time
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  Awards & Honors

  • Leonard J. Savage Dissertation Award (Theory & Methods). ISBA, ASA-SBSS and NBER/NSF Seminars on Bayesian Inference in Econometrics and Statistics. 2006
• Research
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  Selected Grants

  • Analyzing Dependent Extremes via Joint Quantile Regression awarded by  National Science Foundation 2020 - 2023
  • Spatial Information Codes awarded by National Institutes of Health 2017 - 2022
  • Information in Limited-Capacity Neural Codes awarded by National Institutes of Health 2014 - 2019
  • Understanding Regression Heterogeneity Through Joint Estimation of Conditional Quantiles awarded by  National Science Foundation 2016 - 2019
  • Bayesian Methods for Assessing Gene by Environment Interactions awarded by National Institutes of Health 2009 - 2015
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  External Relationships

  • QuaEra Insights
• Publications & Artistic Works
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  Selected Publications

  • Academic Articles

    • Glynn, C., S. T. Tokdar, B. Howard, and D. L. Banks. “Bayesian analysis of dynamic linear topic models.” Bayesian Analysis 14, no. 1 (January 1, 2019): 53–80. https://doi.org/10.1214/18-BA1100.
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    • Caruso, Valeria C., Jeff T. Mohl, Christopher Glynn, Jungah Lee, Shawn M. Willett, Azeem Zaman, Akinori F. Ebihara, et al. “Single neurons may encode simultaneous stimuli by switching between activity patterns.” Nature Communications 9, no. 1 (July 13, 2018): 2715. https://doi.org/10.1038/s41467-018-05121-8.
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    • Burris, Kyle, Kelly Vittetoe, Benjamin Ramger, Sunith Suresh, Surya T. Tokdar, Jerome P. Reiter, and L Gregory Appelbaum. “Sensorimotor abilities predict on-field performance in professional baseball.” Sci Rep 8, no. 1 (January 8, 2018): 116. https://doi.org/10.1038/s41598-017-18565-7.
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    • Klemish, David, Benjamin Ramger, Kelly Vittetoe, Jerome P. Reiter, Surya T. Tokdar, and Lawrence Gregory Appelbaum. “Visual abilities distinguish pitchers from hitters in professional baseball.” J Sports Sci 36, no. 2 (January 2018): 171–79. https://doi.org/10.1080/02640414.2017.1288296.
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    • Yang, Y., and S. Tokdar. “Joint estimation of quantile planes over arbitrary predictor spaces.” Journal of the American Statistical Association 112, no. 519 (September 15, 2017): 1107–20. https://doi.org/10.1080/01621459.2016.1192545.
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    • Caruso, Valeria, Jeff Mohl, Christopher Glynn, Jungah Lee, Shawn Willett, Azeem Zaman, Akinori Ebihara, et al. “Evidence for time division multiplexing: Single neurons may encode simultaneous stimuli by switching between activity patterns,” February 9, 2017. https://doi.org/10.1101/107185.
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    • Montagna, S., and S. T. Tokdar. “Computer emulation with non-stationary Gaussian processes.” Siam/Asa Journal of Uncertainty Quantification 4 (2016): 26–47.
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    • Yang, Y., and S. T. Tokdar. “Minimax-optimal nonparametric regression in high dimensions.” Annals of Statistics 43, no. 2 (2015): 652–74. https://doi.org/10.1214/14-AOS1289.
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    • Pati, Debdeep, David B. Dunson, and Surya T. Tokdar. “Posterior consistency in conditional distribution estimation.” Journal of Multivariate Analysis 116 (April 2013): 456–72. https://doi.org/10.1016/j.jmva.2013.01.011.
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    • Banerjee, A., D. B. Dunson, and S. T. Tokdar. “Efficient Gaussian process regression for large datasets.” Biometrika 100, no. 1 (2013): 75–89. https://doi.org/10.1093/biomet/ass068.
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    • Shen, W., S. T. Tokdar, and S. Ghosal. “Adaptive Bayesian multivariate density estimation with Dirichlet mixtures.” Biometrika 100, no. 3 (2013): 623–40. https://doi.org/10.1093/biomet/ast015.
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    • Montagna, Silvia, Surya T. Tokdar, Brian Neelon, and David B. Dunson. “Bayesian latent factor regression for functional and longitudinal data.” Biometrics 68, no. 4 (December 2012): 1064–73. https://doi.org/10.1111/j.1541-0420.2012.01788.x.
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    • Martin, R., and S. T. Tokdar. “A nonparametric empirical Bayes framework for large-scale multiple testing.” Biostatistics 13, no. 3 (2012): 427–39. https://doi.org/10.1093/biostatistics/kxr039.
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    • Tokdar, S. T., and J. B. Kadaney. “Simultaneous linear quantile regression: A semiparametric bayesian approach.” Bayesian Analysis 7, no. 1 (2012): 51–72. https://doi.org/10.1214/12-BA702.
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    • Tokdar, S. T., I. Grossmann, J. B. Kadane, A. S. Charest, and M. J. Small. “Impact of beliefs about atlantic tropical cyclone detection on conclusions about trends in tropical cyclone numbers.” Bayesian Analysis 6, no. 4 (December 1, 2011): 547–72. https://doi.org/10.1214/11-BA621.
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    • Bisi, A., M. Dada, and S. Tokdar. “A censored-data multiperiod inventory problem with newsvendor demand distributions.” Manufacturing and Service Operations Management 13, no. 4 (September 1, 2011): 525–33. https://doi.org/10.1287/msom.1110.0340.
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    • Martin, R., and S. T. Tokdar. “Semiparametric inference in mixture models with predictive recursion marginal likelihood.” Biometrika 98, no. 3 (September 1, 2011): 567–82. https://doi.org/10.1093/biomet/asr030.
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    • Tokdar, S. T., Y. M. Zhu, and J. K. Ghosh. “Bayesian density regression with logistic Gaussian process and subspace projection.” Bayesian Analysis 5, no. 2 (December 1, 2010): 319–44. https://doi.org/10.1214/10-BA605.
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    • Tokdar, Surya, Peiyi Xi, Ryan C. Kelly, and Robert E. Kass. “Detection of bursts in extracellular spike trains using hidden semi-Markov point process models.” Journal of Computational Neuroscience 29, no. 1–2 (August 2010): 203–12. https://doi.org/10.1007/s10827-009-0182-2.
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    • Tokdar, S. T., and R. E. Kass. “Importance sampling: A review.” Wiley Interdisciplinary Reviews: Computational Statistics 2, no. 1 (2010): 54–60. https://doi.org/10.1002/wics.56.
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    • Tokdar, S. T., M. Ryan, and J. K. Ghosh. “Consistency of a recursive estimate of mixingdistributions.” Annals of Statistics 37, no. 5 A (October 1, 2009): 2502–22. https://doi.org/10.1214/08-AOS639.
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    • Martin, R., and S. T. Tokdar. “Asymptotic properties of predictive recursion: Robustness and rate of convergence.” Electronic Journal of Statistics 3 (January 1, 2009): 1455–72. https://doi.org/10.1214/09-EJS458.
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    • Bogdan, Małgorzata, Jayanta K. Ghosh, and Surya T. Tokdar. “A comparison of the Benjamini-Hochberg procedure with some Bayesian rules for multiple testing,” 2008, 211–30. https://doi.org/10.1214/193940307000000158.
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    • Bogdan, M., J. K. Ghosh, A. Ochman, and S. T. Tokdar. “On the empirical bayes approach to the problem of multiple testing.” Quality and Reliability Engineering International 23, no. 6 (October 1, 2007): 727–39. https://doi.org/10.1002/qre.876.
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    • Tokdar, S. T. “Towards a faster implementation of density estimation with logistic gaussian process priors.” Journal of Computational and Graphical Statistics 16, no. 3 (September 1, 2007): 633–55. https://doi.org/10.1198/106186007X210206.
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    • Tokdar, S. T., and J. K. Ghosh. “Posterior consistency of logistic Gaussian process priors in density estimation.” Journal of Statistical Planning and Inference 137, no. 1 (January 1, 2007): 34–42. https://doi.org/10.1016/j.jspi.2005.09.005.
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    • Tokdar, S. T. “Posterior consistency of dirichlet location-scale mixture of normals in density estimation and regression.” Sankhya: The Indian Journal of Statistics 68, no. 1 (February 1, 2006): 90–110.

  • Book Sections

    • Ghosh, J. K., and S. T. Tokdar. “Convergence and consistency of Newton’s algorithm for estimating mixing distribution.” In Frontiers in Statistics: Dedicated to Peter John Bickel in Honor of His 65th Birthday, 429–43, 2006. https://doi.org/10.1142/9781860948886_0019.
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• Teaching & Mentoring
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  Recent Courses

  • MATH 228L: Probability for Statistical Inference, Modeling, and Data Analysis 2020
  • STA 240L: Probability for Statistical Inference, Modeling, and Data Analysis 2020
  • STA 641: Statistical Learning and Bayesian Nonparametrics 2020
  • STA 995: Internship 2020
  • STA 701S: Readings in Statistical Science 2019
  • MATH 790-71: Current Research in Applied Mathematics 2018
  • STA 941: Bayesian Nonparametric Models and Methods 2018
  • STA 961: Stochastic Processes 2018

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