Surya Tapas Tokdar
Professor of Statistical Science
Current Appointments & Affiliations
- Professor of Statistical Science, Statistical Science, Trinity College of Arts & Sciences 2022
- Faculty Network Member of the Duke Institute for Brain Sciences, Duke Institute for Brain Sciences, University Institutes and Centers 2014
Contact Information
- 214 Old Chemistry, Box 90251, Durham, NC 27708
- Box 90251, Durham, NC 27708-0251
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tokdar@stat.duke.edu
(919) 684-2152
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Personal site
- Background
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Education, Training, & Certifications
- Ph.D., Purdue University 2006
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Previous Appointments & Affiliations
- Associate Professor of Statistical Science, Statistical Science, Trinity College of Arts & Sciences 2016 - 2022
- Associate Chair of the Department of Statistical Science, Statistical Science, Trinity College of Arts & Sciences 2020 - 2022
- 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
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NOV 29, 2022 -
JUL 18, 2018
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Awards & Honors
- Research
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Selected Grants
- Information Preservation in Neural Codes awarded by National Institutes of Health 2022 - 2027
- Analyzing Dependent Extremes via Joint Quantile Regression awarded by National Science Foundation 2020 - 2023
- Spatial Information Codes awarded by National Institutes of Health 2017 - 2023
- 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
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Academic Articles
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Jun, Na Young, Douglas A. Ruff, Lily E. Kramer, Brittany Bowes, Surya T. Tokdar, Marlene R. Cohen, and Jennifer M. Groh. “Coordinated multiplexing of information about separate objects in visual cortex.” Elife 11 (November 2022): e76452. https://doi.org/10.7554/elife.76452.Full Text Open Access Copy
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Tokdar, S. T., S. Jiang, and E. L. Cunningham. “Heavy-Tailed Density Estimation.” Journal of the American Statistical Association, January 1, 2022. https://doi.org/10.1080/01621459.2022.2104727.Full Text Open Access Copy
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Jiang, S., and S. T. Tokdar. “Variable selection consistency of Gaussian process regression.” Annals of Statistics 49, no. 5 (October 1, 2021): 2491–2505. https://doi.org/10.1214/20-AOS2043.Full Text
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Chen, X., and S. T. Tokdar. “Joint quantile regression for spatial data.” Journal of the Royal Statistical Society. Series B: Statistical Methodology 83, no. 4 (September 1, 2021): 826–52. https://doi.org/10.1111/rssb.12467.Full Text
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Glynn, Chris, Surya T. Tokdar, Azeem Zaman, Valeria C. Caruso, Jeff T. Mohl, Shawn M. Willett, and Jennifer M. Groh. “ANALYZING SECOND ORDER STOCHASTICITY OF NEURAL SPIKING UNDER STIMULI-BUNDLE EXPOSURE.” The Annals of Applied Statistics 15, no. 1 (March 2021): 41–63. https://doi.org/10.1214/20-aoas1383.Full Text
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Mohl, Jeff T., Valeria C. Caruso, Surya T. Tokdar, and Jennifer M. Groh. “Sensitivity and specificity of a Bayesian single trial analysis for time varying neural signals.” Neurons, Behavior, Data Analysis and Theory 3, no. 1 (January 2020): https://nbdt.scholasticahq.com/article/11880-sensi.
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Jun, Na Young, Douglas A. Ruff, Lily E. Kramer, Brittany Bowes, Surya T. Tokdar, Marlene R. Cohen, and Jennifer M. Groh. “Coordinated multiplexing of information about separate objects in visual cortex,” September 23, 2019. https://doi.org/10.1101/777912.Full Text
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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.Full Text
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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 2018): 2715. https://doi.org/10.1038/s41467-018-05121-8.Full Text Open Access Copy
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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.Full Text Open Access Copy Link to Item
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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.Full Text Open Access Copy Link to Item
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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.Full Text Link to Item
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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.Full Text
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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.Link to Item
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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.Full Text
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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.Full Text
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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.Full Text
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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.Full Text Open Access Copy
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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.Full Text
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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.Full Text
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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.Full Text
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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.Full Text
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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.Full Text
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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.Full Text
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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.Full Text
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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.Full Text
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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.Full Text
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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.Full Text
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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.Full Text
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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.Full Text
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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.Full Text
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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.Full Text
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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.Full Text
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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.
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Book Sections
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Cunningham, E., S. T. Tokdar, and J. S. Clark. “A vignette on model-based quantile regression: Analysing excess zero response.” In Flexible Bayesian Regression Modelling, 27–64, 2019. https://doi.org/10.1016/B978-0-12-815862-3.00008-1.Full Text
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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.Full Text
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- Teaching & Mentoring
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Recent Courses
- STA 532: Theory of Statistical Inference 2023
- STA 693: Research Independent Study 2023
- STA 901S: Statistical Science Seminar 2023
- STA 532: Theory of Statistical Inference 2022
- STA 901S: Statistical Science Seminar 2022
- STA 941: Bayesian Nonparametric Models and Methods 2022
- MATH 228L: Probability for Statistical Inference, Modeling, and Data Analysis 2021
- STA 240L: Probability for Statistical Inference, Modeling, and Data Analysis 2021
- STA 532: Theory of Statistical Inference 2021
- STA 693: Research Independent Study 2021
- STA 790-1: Special Topics in Statistics 2021
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