Current graduate students

Assessing financial model risk

Model risk has a huge impact on any financial or insurance risk measurement procedure and its quantification is therefore a crucial step. In this talk, we introduce three quantitative measures of model risk when choosing a particular reference model within a given class: the absolute measure of model risk, the relative measure of model risk and the local measure of model risk. Each of the measures has a specific purpose and so allows for flexibility. We illustrate the various notions by studying some relevant examples, so as to emphasize the practicability and tractability of our approach.

Factor Models and PCA in light of the spiked covariance model

Some new phenomena in high-dimensional statistics and optimization

Statistical models in which the ambient dimension is of the same order
or larger than the sample size arise frequently in different areas of
science and engineering.  Examples include sparse regression in
genomics; graph selection in social network analysis; and low-rank
matrix estimation in video segmentation.  Although high-dimensional
models of this type date back to seminal work of Kolmogorov and

Constrained maximum likelihood estimation for model calibration using summary-level information from external big data sources.

Information from various public and private data sources of extremely large sample

Monitoring and Improving Surgical Quality

Some statistical issues related to the monitoring of surgical quality will be reviewed in this presentation. The important role of risk-adjustment in healthcare, used to account for variations in the condition of patients, will be described. Some of the methods for monitoring quality over time, including a new one, will be outlined and illustrated with examples.