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Han Liu, PhD

Joint appointment with Computer Science

Assistant Professor

- Adjunct

Departmental Affiliation(s):

Biostatistics

Personal website : www.cs.jhu.edu/~hanliu

Education

PhD , Carnegie Mellon
MS , University of Toronto

Overview

Modern data acquisition routinely produces massive amounts of very large-scale, ultrahigh-dimensional and highly complex datasets. Driven by the complexity of these datasets, highly adaptive and scalable data analysis procedures are crucially needed.  My research lies at the intersection of Modern Statistics and Computer Science. Especially, I am interested in large-scale nonparametric methods, which directly conduct inference in infinite-dimensional spaces and are more flexible to capture the subtleties in modern applications. My long-term goal is to develop a new generation of more powerful and principled statistical theories and machine learning algorithms to explore, understand, and predict large-scale, complex datasets. 

Honors and Awards

Tinsley Oden Faculty FellowshipInstitute of Computational Engineering and Sciences, University of Texas-Austin, 2010

Best Paper AwardASA Student Paper Competition in Statistical Computating and GRaphics, American Statistical Association, 2010

Google PHD Fellowship in StatisticsGoogle Inc, 2009-2011

Best Student Paper AwardThe 26th International Conference on Machine Learning, ICML, 2009

Best Overall Paper Award Honorable MentionThe 26th International Conference on Machine Learning, ICML, 2009

Statistical machine learning, nonparametric inference, high dimensional data analysis, graphical models, functional data analysis, convex optimization, large-scale datasets, genomics, biomedical imaging, text mining. 

  • Forest Density Estimation
    Han Liu, Min Xu, Haijie Gu, Anupam Dasgupta, John Lafferty, and Larry Wasserman
    Journal of Machine Learning Research (JMLR) Volume 12. 907−951. 2011

  • Stability Approach to Regularization Selection (StARS) for High Dimensional Graphical Models 
    Han Liu, Kathryn Roeder and Larry Wasserman
    Proceedings of Advances in Neural Information Processing Systems (NIPS), 23, 2010.

  • Graph-Valued Regression 
    Han Liu, Xi Chen, John Lafferty, and Larry Wasserman
    Proceedings of Advances in Neural Information Processing Systems (NIPS), 23, 2010. 

  • Multivariate Dyadic Regression Trees for Sparse Learning Problems
    Han Liu and Xi Chen 
    Proceedings of Advances in Neural Information Processing Systems (NIPS), 23, 2010. 

  • Mining Past Query Trails to Label Long and Rare Search Engine Queries
    Peter Bailey, Ryen W. White, Han Liu, and Giridhar Kumaran 
    ACM Transactions on the Web (ACM TWEB) 1(2) 1-25, 2010.