11th Conference on Bayesian Nonparametrics (BNP11), June 29, 2017, Paris, France. Several interpretations of the power posterior. (slides)

ICERM Probabilistic Scientific Computing Workshop, June 8, 2017, Providence, RI. Inference using partial information. (slides)

MIT Machine Learning Colloquium, April 26, 2017, Massachusetts Institute of Technology. Robust Bayesian inference via coarsening. (slides)

9th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2016), Dec 11, 2016, Seville, Spain. Non-standard approaches to nonparametric Bayes. (slides)

FocuStat L^\eta Research Kitchen, Oct 12, 2016, University of Oslo, Norway. Robust Bayesian inference via coarsening.

Pattern Theory Seminar, Oct 5, 2016, Brown University. Robust Bayesian inference via coarsening.

Joint Statistical Meetings (JSM), August 3, 2016, Chicago, IL. Robust Bayesian inference via coarsening.

International Society for Bayesian Analysis (ISBA) World Meeting, June 14, 2016, Sardinia, Italy. Robust Bayesian inference via coarsening. (One of 2 winners of travel award)

8th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2015), Dec 14, 2015, London, UK. Robust Bayesian inference via coarsening.

Bayesian Nonparametrics: The Next Generation, NIPS 2015 Workshop, Dec 12, 2015, Montreal, CA. Non-standard approaches to nonparametric Bayes (talk), Microclustering: When the cluster sizes grow sublinearly with the size of the data set (poster). (One of 5 winners of travel award)

Harvard Statistics Departmental Colloquium, Sept 21, 2015, Harvard University. Robust Bayesian inference via coarsening.

Joint Statistical Meetings (JSM), August 11, 2015, Seattle, WA. Robust Bayesian inference via coarsening.

Bayesian Nonparametrics: Synergies between Statistics, Probability and Mathematics, June 30, 2015, SAMSI. Robust Bayesian inference via coarsening.

10th Conference on Bayesian Nonparametrics (BNP10), June 23, 2015, Raleigh, NC. An approach to inference under misspecification.

G70: A Celebration of Alan Gelfand's 70th Birthday, April 20, 2015, Duke University. The small clustering problem: What if the clusters don't grow with N?

Texas A&M Statistics Departmental Colloquium, October 31, 2014, Texas A&M University. Combinatorial stochastic processes for variable-dimension models.

International Society for Bayesian Analysis (ISBA) World Meeting, July 14 - 18, 2014, Cancun, Mexico. Combinatorial stochastic processes for variable-dimension models.

New England Statistics Symposium (NESS), April 26, 2014, Harvard School of Public Health. Combinatorial stochastic processes for variable-dimension models.

Dissertation defense, April 15, 2014, Brown University.

Duke Statistical Science Seminar, February 7, 2014, Duke University. Combinatorial stochastic processes for variable-dimension models.

Neural Information Processing Systems (NIPS) 2013, Lake Tahoe, NV. A simple example of Dirichlet process mixture inconsistency for the number of components. (Full oral presentation) (slides) (poster)

Pattern Theory Seminar, November 6, 2013, Brown University. Dirichlet process mixture inconsistency for the number of components, and dimension mixture models.

9th Conference on Bayesian Nonparametrics (BNP9), Amsterdam, 2013. Dimension mixtures of finite-dimensional models. (Winner of 1st place in poster competition) (poster)

New England Machine Learning day (NEML) 2013, Cambridge, MA. Posterior consistency for the number of components in a finite mixture.

New England Statistics Symposium (NESS) 2013, Storrs, CT. Posterior consistency for the number of components in a finite mixture.

Brown University Symposium for Undergraduates in the Mathematical Sciences (SUMS), 2013. High-dimensional parameter spaces and Fisher information.

Neural Information Processing Systems (NIPS) 2012, Lake Tahoe, NV (Workshop on Modern Nonparametric Methods in Machine Learning). Posterior consistency for the number of components in a finite mixture. (Please see correction to this abstract.)

ICERM Bayesian Nonparametrics Workshop, 2012, Providence, RI. Dirichlet process mixtures are inconsistent for the number of components in a finite mixture.

New England Statistics Symposium (NESS) 2011, Storrs, CT. A practical algorithm for exact inference on tables. (One of four winners of the IBM Thomas J. Watson Research Center Student Research Award)

Joint Statistical Meetings (JSM) 2010, Vancouver, BC. A practical algorithm for exact inference on tables.