Seminar and conference talks
- Conformal Prediction, Sample Splitting, and Permutation (ICML 2021 Workshop on Distribution Free Uncertainty Quantification)
- What is the probability that two random integers are coprime?
- Convergence of Empirical Distributions under Wasserstein Distances
- Differentially Private Model Selection with Penalized and Constrained Likelihood
.
- Network representation using graph root distributions.
- Accounting for uncertainties in predictive inference.
- Cross-Validation with Confidence.
- A Framework for Assumption-Free Predictive
Regression Inference.
-
Set-valued Classification with Confidence: Least
Ambiguity with Bounded Error Levels.
The 10th ICSA International Conference, Shanghai, China, Dec 2016.
-
Network Model Comparison using Network Cross-Validation.
Nonparametric Statistics Workshop, Ann Arbor, MI, Oct 2016.
- Network Cross-Validation for Stochastic Block Model Selection.
Joint Statistical Meetings, Seattle, WA, August 2015.
- Structured Principal Component Analysis in High Dimensions.
Interface Symposium, Morgantown, WV, June 2015.
- Stochastic Block Models: Model Selection and Goodness of Fit.
UT Austin, Department of Statistics and Data Science, March 2015.
- Community Recovery and Model Selection for Stochastic Block Models.
Purdue University, Department of Statistics, November 2014.
- Sparse PCA in High Dimensions.
Simons Institute Workshop on Big
Data and Differential Privacy, Berkeley, CA, December 2013.
- Sparse PCA: Concepts, Theory, and Algorithms.
University of Pittsburgh, Department of Biostatistics, November 2013.
- Estimating Sparse Principal Components and Subspaces.
IMS-SWUFE International Conference on Statistics and Probability,
Chengdu, China. July 2013.
- Distribution free prediction sets.
14th Meeting of New Researchers
in Statistics and Probability,
University of California, San Diego, CA. July 2012.
- Debiasing
the ensemble Kalman filter: the NLEAF algorithm
The National Center for
Atmospheric Research (NCAR), Boulder, CO. February 2010.
- Predicting the Chaos Using Ensemble Filters: A Regression Approach
Poster, Theory and Practice of Computational Learning Summer Workshop, University of Chicago, IL. June 2009.
- On Stability and Sparsity of ensemble Kalman filters
Poster, Future Directions in High-Dimensional Data Analysis, Isaac Newton Institute, Cambridge, UK. June 2008.
- Particle filters and their potential use in numerical weather forecasting
The National Center for Atmospheric Research (NCAR), Boulder, CO. December 2007.
