Seminar and conference talks

  1. Cross-Validation with Confidence.
       Invited seminar talk, University of California, Berkeley, Department of Statistics, April 2017.
       Invited seminar talk, University of Minnesota Twin Cities, School of Statistics, March 2017.
  2. A Framework for Assumption-Free Predictive Regression Inference.
       Invited seminar talk, University of Pittsburgh, Department of Statistics, Pittsburgh, PA, February 2017.
  3. Set-valued Classification with Confidence: Least Ambiguity with Bounded Error Levels.
       The 10th ICSA International Conference, Shanghai, China, Dec 2016.
  4. Network Model Comparison using Network Cross-Validation.
        Nonparametric Statistics Workshop, Ann Arbor, MI, Oct 2016.
  5. A Framework for Assumption-Free Predictive Regression Analysis.
        JSM, Chicago, IL, Aug 2016.
  6. A Framework for Assumption-Free Predictive Regression Analysis.
        Oberwolfach Workshop, Oberwolfach, Germany, Mar 2016.
  7. A Framework for Assumption-Free Regression.
       Invited seminar talk, Florida State University, Department of Statistics, Tallahassee, FL, November 2015.
  8. Network Cross-Validation for Stochastic Block Model Selection.
       Contributed talk, Joint Statistical Meetings, Seattle, WA, August 2015.
  9. Structured Principal Component Analysis in High Dimensions.
       Invited talk, Interface Symposium, Morgantown, WV, June 2015.
  10. Stochastic Block Models: Model Selection and Goodness of Fit.
       Invited seminar talk, UT Austin, Department of Statistics and Data Science, March 2015.
  11. Community Recovery and Model Selection for Stochastic Block Models.
       Invited seminar talk, Purdue University, Department of Statistics, November 2014.
  12. Sparse PCA in High Dimensions.
       Invited talk, Simons Institute Workshop on Big Data and Differential Privacy, Berkeley, CA, December 2013.
  13. Sparse PCA: Concepts, Theory, and Algorithms.
       Invited seminar talk, University of Pittsburgh, Department of Biostatistics, November 2013.
  14. Estimating Sparse Principal Components and Subspaces.
       Invited talk, IMS-SWUFE International Conference on Statistics and Probability, Chengdu, China. July 2013.
  15. Distribution free prediction sets.
       Contributed talk, 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
       Invited talk, 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
       Invited talk, The National Center for Atmospheric Research (NCAR), Boulder, CO. December 2007.