Statistical Machine Learning (SMiLe) LabHan Liu, Assistant Professor of Operations Research and Financial Engineering @ Princeton University

Effective data analysis at the required scale will be one of the greatest challenges of modern information society. To handle this challenge, we need to understand complexiy--both informational and computational complexity. My research lies at the intersection of modern statistics and computer science. Especially, I am interested in statistical and computational foundations of Big Data analysis. My application interest is to develop a unified set of computational, statistical, and software tools to extract and interpret significant information from the data collected from a variety of modern sources (Social media, biological experiments, medical diagnostics, etc.).

We develop flexible statistical models and computational algorithms that exploit hidden structure to overcome the curse of dimensionality when analyzing massive amounts of high dimensional datasets. These algorithms have strong theoretical guarantees and provide new recipes for many important inference tasks, ranging from unsupervised exploratory data analysis to supervised predictive modeling. We now focus on:

Statistical optimization is a new area that lies at the intersection of statistical learning, robust optimization, and stochastic optimization. We apply ideas from modern porbability and statistics to solve large and complex optimization problems. In particular, we develop rigorous theoretical framework to characterize the interaction between informational and computational complexity. Our current research include:

Our research has a wide variety of applications, raning from genomics, bio-imaging, and social media analysis. The data in these fields are usually very high dimensional and complex, which makes flexible nonparametric methods suitable for building accurate predictive models or discovering new scientific facts: