BIOGRAPHICAL SKETCH
Provide the following information for the key personnel and other significant contributors.
Follow this format for each person. DO NOT EXCEED FOUR PAGES.
NAME
POSITION TITLE
Zhu, Ji
Associate Professor of Statistics
eRA COMMONS USER NAME
jizhu1
EDUCATION/TRAINING (Begin with baccalaureate or other initial professional education, such as nursing, and include postdoctoral training.)
INSTITUTION AND LOCATION
Peking University, Beijing, China
Stanford University, Stanford, CA
DEGREE
(if applicable)
YEAR(s)
B.Sc.
Ph.D.
1996
2003
FIELD OF STUDY
Physics
Statistics
A. Personal Statement
I have worked with Dr. Waljee and Dr. Hayward on several IBD clinical research projects since 2006, resulting in
3 publications. I have extensive experience in developing and implementing machine learning algorithms. I
received my B.Sc. in Physics from Peking University in China, and my Ph.D. in Statistics from Stanford University
in 2003. I am now an Associate Professor in the Department of Statistics at the University of Michigan. I am
recognized as a leading researcher in the areas of statistical machine learning and high-dimensional data
analysis. I received a CAREER award from the National Science of Foundation (USA) in 2008, and I was elected
as the Chair (2011-2012) of the Statistical Learning and Data Mining Section for the American Statistical
Association. I bring to the project an established research record in statistics and machine learning. I have
published 60 research papers (50 journal articles, 5 refereed conference articles and 5 discussion articles). I
have devoted my research to developing theory and methodologies in the fields of classification, clustering, kernel
methods, variable selection, high-dimensional data analysis and statistical network analysis. I will directly help Dr.
Waljee and Dr. Hayward in the work on developing and implementing machine learning algorithms for IBD
studies. I am excited about and committed to the implementation of this work in clinical care.
B. Positions and Honors
Professional Positions:
2003 - 2008 Assistant Professor, Department of Statistics, University of Michigan, Ann Arbor, MI
2006 Faculty Member, Center for Computational Medicine and Bioinformatics, University of Michigan
2008 Associate Professor, Department of Statistics, University of Michigan, Ann Arbor, MI
2010 Associate Professor (Courtesy), Department of EECS, University of Michigan, Ann Arbor, MI
Honors and Awards:
1998-2001
Kimball Graduate Fellowship, Stanford University
2002
Student Paper Competition Award, Computing Section, American Statistical Association
2008-2013
CAREER Award, National Science Foundation
2010
Elected Member of the International Statistical Institute
2011
Chair, Statistical Learning and Data Mining Section, American Statistical Association
C. Selected peer-reviewed publications (Selected from 55 peer-reviewed publications)
1. Zhu, J. and Hastie, T. (2004) Classification of gene microarrays by penalized logistic regression. Biostatistics
5(3):427-444.
2. Roe, B., Yang, H., Zhu, J., Liu, Y., Stancu, I. and McGregor, G. (2005) Boosted decision trees as an
alternative to artificial neural networks for particle identification. Nuclear Instruments and Methods for Physics
Research, Section A 543(2-3):577-584. (This paper consists of applications of tree based machine learning
algorithms in nuclear physics.)
3. Yang, H., Roe, B. and Zhu, J. (2005) Studies of boosted decision trees for MiniBooNE particle identification.
Nuclear Instruments and Methods for Physics Research, Section A 555(1-2):370-385. (This paper consists of
applications of tree based machine learning algorithms in nuclear physics.)
4. Ulintz, P., Zhu, J., Qin, Z. and Andrews, P. (2006) Improved classification of mass spectrometry database
search results using newer machine learning approaches. Molecular and Cellular Proteomics 5(3):497-509.
(This paper consists of applications of tree based machine learning algorithms in biochemistry.)
5. Li, Y. and Zhu, J. (2007) Analysis of array CGH data for cancer studies using the fused quantile regression.
Bioinformatics 23(18):2470-2476.
6. Wang, S. and Zhu, J. (2007) Improved centroids estimation for the nearest shrunken centroid classifier.
Bioinformatics 23(8):972-979. (One of four winning papers in the 2007 ASA Student Paper Competition
sponsored by the Statistical Computing Section)
7. Yang, H., Roe, B. and Zhu, J. (2007) Studies of stability and robustness for artificial neural networks and
boosted decision trees. Nuclear Instruments and Methods for Physics Research, Section A 574(2):342-349.
(This paper consists of applications of tree based machine learning algorithms in nuclear physics.)
8. Wang, S. and Zhu, J. (2008) Variable selection for model-based high-dimensional clustering and its
application to microarray data. Biometrics 64(2):440-448.
9. Zou, H., Zhu, J. and Hastie, T. (2008) New multi-category boosting algorithms based on multi-category Fisherconsistent losses. Annals of Applied Statistics 2(4):1290-1306. (This paper develops a novel classification
framework based on boosting classification trees.)
10. Peng, J., Wang, P., Zhou, N. and Zhu, J. (2009) Partial correlation estimation using joint sparse regression
models. Journal of the American Statistical Association 104(486):735-746.
11. Wang, S., Nan, B., Zhou, N. and Zhu, J. (2009) Hierarchically penalized Cox regression for censored data
with grouped variables. Biometrika 96(2):307-322. (Winner of the 2008 ICSA J.P. Hsu Memorial Award)
12. Zhu, J., Zou, H., Rosset, S. and Hastie, T. (2009) Multi-class adaboost. Statistics and Its Interface 2(3):349360. (Special issue on statistics and machine learning) (This paper develops a novel multi-class classification
method based on boosting classification trees.)
13. Choi, N., Li, W. and Zhu, J. (2010) Variable selection with the strong heredity constraint and its oracle
property. Journal of the American Statistical Association 105(489):354-364. (One of the winning papers in the
2007 ENAR Student Paper Competition)
14. Peng, J., Zhu, J., Bergamaschi, A., Han, W., Noh, D., Pollack, J. and Wang, P. (2010) Regularized
multivariate regression for identifying master predictors with application to integrative genomics study of
breast cancer. Annals of Applied Statistics 4(1):53-77.
15. Waljee, A., Joyce, J., Wang, S., Saxena, A., Hart, M., Zhu, J. and Higgins, P. (2010) Algorithms outperform
metabolite tests in predicting response of patients with inflammatory bowel disease to thiopurines. Clinical
Gastroenterology and Hepatology 8:143-150. (This paper consists of applications of tree based machine
learning algorithms in thiopurine studies.)
D. Research Support
Ongoing
NSF DMS-0748389 Zhu (PI) 7/1/2008 - 6/30/2013
CAREER: Statistical Learning from Data with Graph/Network Structure
The research aims to develop new statistical methodologies and associated theory that incorporate the
network/graph structure in the data.
Role: PI
NIH R01-AG-036802 Nan (PI) 9/1/2010 - 8/31/2014
High-dimensional Data Issues in Aging Research
The research aims to address several emerging issues in high-dimensional data analysis and close certain gaps
between statistical theory and biomedical applications, with a focus on aging related diseases.
Role: Co-PI
NIH R01-GM-096194
Zhu (PI)
9/1/2010 – 8/31/2014
Sparse Structure Identification from High-dimensional Epigenomic Data
The research aims to develop novel statistical methods for sparse structure estimation from histone modification
data, identify various histone modification patterns and link them with functional elements of the genome.
Role: PI
Completed
NSF DMS-0705532 James (PI)
7/1/2007 - 6/30/2010
Generalized Variable Selection with Applications to Functional Data Analysis and Other Problems
The major goal of this project is to study four important applications of generalized variable selection in areas as
diverse as functional regression, principal component analysis (both standard and functional), multivariate nonparametric regression, and transcription regulation network problems for microarray experiments.
Role: Co-PI
NSF DMS-0505432 Zhu (PI) 7/1/2005 - 6/30/2008
Flexible Classification and Regression
The research aims to combine statistical and computational considerations in designing new and useful predictive
modeling tools and algorithms.
Role: PI