Seminar and conference talks

  1. Differentially Private Model Selection with Penalized and Constrained Likelihood .
       ASA Symposium on Data Science and Statistics, Reston VA, May 2018.
  2. Network representation using graph root distributions.
       George Mason University, Statistics Seminar, Fairfax VA, April 2018.
       Oberwolfach, De, March 2018.
  3. Accounting for uncertainties in predictive inference.
       Conference on Predictive Inference and Its Applications, Ames IA, May 2018.
       Amazon, Palo Alto, February 2018.
  4. Cross-Validation with Confidence.
       University of California, Berkeley, Department of Statistics, April 2017.
       University of Minnesota Twin Cities, School of Statistics, March 2017.
  5. A Framework for Assumption-Free Predictive Regression Inference.
       University of Pittsburgh, Department of Statistics, Pittsburgh, PA, February 2017.
       JSM, Chicago, IL, Aug 2016.
       Oberwolfach Workshop, Oberwolfach, Germany, Mar 2016.
       Florida State University, Department of Statistics, Tallahassee, FL, November 2015.
  6. Set-valued Classification with Confidence: Least Ambiguity with Bounded Error Levels.
       The 10th ICSA International Conference, Shanghai, China, Dec 2016.
  7. Network Model Comparison using Network Cross-Validation.
        Nonparametric Statistics Workshop, Ann Arbor, MI, Oct 2016.
  8. Network Cross-Validation for Stochastic Block Model Selection.
       Joint Statistical Meetings, Seattle, WA, August 2015.
  9. Structured Principal Component Analysis in High Dimensions.
       Interface Symposium, Morgantown, WV, June 2015.
  10. Stochastic Block Models: Model Selection and Goodness of Fit.
       UT Austin, Department of Statistics and Data Science, March 2015.
  11. Community Recovery and Model Selection for Stochastic Block Models.
       Purdue University, Department of Statistics, November 2014.
  12. Sparse PCA in High Dimensions.
       Simons Institute Workshop on Big Data and Differential Privacy, Berkeley, CA, December 2013.
  13. Sparse PCA: Concepts, Theory, and Algorithms.
       University of Pittsburgh, Department of Biostatistics, November 2013.
  14. Estimating Sparse Principal Components and Subspaces.
       IMS-SWUFE International Conference on Statistics and Probability, Chengdu, China. July 2013.
  15. Distribution free prediction sets.
       14th Meeting of New Researchers in Statistics and Probability, University of California, San Diego, CA. July 2012.
  16. Debiasing the ensemble Kalman filter: the NLEAF algorithm
       The National Center for Atmospheric Research (NCAR), Boulder, CO. February 2010.
  17. Predicting the Chaos Using Ensemble Filters: A Regression Approach
       Poster, Theory and Practice of Computational Learning Summer Workshop, University of Chicago, IL. June 2009.
  18. On Stability and Sparsity of ensemble Kalman filters
       Poster, Future Directions in High-Dimensional Data Analysis, Isaac Newton Institute, Cambridge, UK. June 2008.
  19. Particle filters and their potential use in numerical weather forecasting
       The National Center for Atmospheric Research (NCAR), Boulder, CO. December 2007.