Jie Liu

Senior Fellow

University of Washington

Biography

I received my Ph.D. in computer science from the University of Wisconsin-Madison. I am currently a postdoctoral fellow in the Genome Sciences Department at the University of Washington. My research areas are machine learning and its applications in biomedical informatics.

I will join as an assistant professor in the Department of Computational Medicine and Bioinformatics at the University of Michigan, Ann Arbor in January 2019. Interested students/postdocs can reach out to me via email.

Download my CV here.

Interests

Machine Learning
Computational Genomics
Medical Informatics
Statistics

Education

PhD in Computer Science, 2014

University of Wisconsin-Madison
MS in Statistics, 2011

University of Wisconsin-Madison
MS in Signal & Information Processing, 2007

Peking University
BS in Info. Management & Info. System, 2004

Beijing University of Posts and Telecommunications

Publications

J Liu, D Lin, G Yardimci, W Noble (2018). Unsupervised embedding of single-cell Hi-C data. In ISMB.

J Liu, J Halloran, J Bilmes, R Daza, C Lee, E Mahen, D Prunkard, C Song, S Blau, M Dorschner, V Gadi, J Shendure, A Blau, W Noble (2017). Comprehensive statistical inference of the clonal structure of cancer from multiple biopsies. In Scientific Reports.

PDF Code Project Cancer Genomics Dynamic Graphical Models

J Liu, C Zhang, D Page (2016). Multiple Testing under Dependence via Graphical Models. In Annals of Applied Statistics.

PDF Project Multiple Testing Graphic Model GWAS

J Fan, Y Wu, M Yuan, D Page, J Liu, I Ong, P Peissig, ES Burnside (2016). Structure-leveraged Methods in Breast Cancer Risk Prediction. In JMLR.

PDF Project

Y Wu, C Abbey, J Liu, I Ong, P Peissig, A Onitilo, J Fan, M Yuan, E Burnside (2016). Discriminatory power of common genetic variants in personalized breast cancer diagnosis. In SPIE Medical Imaging conference.

Preprint

ES Burnside, J Liu, Y Wu, A Onitilo, C McCarty, D Page, P Peissig, A Trentham-Dietz, T Kitchner, J Fan, M Yuan (2016). Comparing Mammography Abnormality Features to Genetic Variants in the Prediction of Breast Cancer in Women Recommended for Breast Biopsy. In Acad Radiol.

Preprint Project

J Weiss, F Kuusisto, K Boyd, J Liu, D Page (2015). Machine Learning for Treatment Assignment: Improving Individualized Risk Attribution. In AMIA Symposium.

PDF

Y Wu, C Abbey, X Chen, J Liu, D Page, O Algoz, P Peissig, A Onitilo, ES Burnside (2015). Developing a Utility Decision Framework to Evaluate Predictive Models in Breast Cancer Risk Estimation. In J. of Medical Imaging.

PDF

J Liu, Y Wu, I Ong, D Page, P Peissig, C McCarty, A Onitilo, ES Burnside (2015). Machine Learning for Treatment Assignment: Improving Individualized Risk Attribution. In AMIA Joint Summits.

PDF Project

Y Wu, J Liu, A del Rio, D Page, O Alagoz, P Peissig, A Onitilo, ES Burnside (2015). Developing a Clinical Utility Framework to Evaluate Prediction Models in Radiogenomics. In SPIE Medical Imaging conference.

Preprint

Y Wu, J Liu, D Page, P Peissig, C McCarty, A Onitilo, ES Burnside (2014). Comparing the Value of Mammographic Features and Genetic Variants in Breast Cancer Risk Prediction. In AMIA Symposium.

PDF Project

J Liu, C Zhang, ES Burnside, D Page (2014). Multiple Testing under Dependence via Semiparametric Graphical Models. In ICML.

PDF Code Project

J Liu, C Zhang, ES Burnside, D Page (2014). Learning Heterogeneous Hidden Markov Random Fields. In AISTATS.

PDF

J Liu, D Page, P Peissig, C McCarty, A Onitilo, A Trentham-Dietz, ES Burnside (2014). New Genetic Variants Improve Personalized Breast Cancer Diagnosis. In AMIA-TBI.

PDF Project Marco Ramoni Distinguished Paper Award

J Liu, D Page (2013). Bayesian Estimation of Latently-grouped Parameters in Undirected Graphical Models. In NIPS.

PDF

J Liu, D Page, P Peissig, C McCarty, A Onitilo, A Trentham-Dietz, ES Burnside (2013). Genetic Variants Improve Breast Cancer Risk Prediction on Mammograms. In AMIA Symposium.

PDF Project

J Liu, ES Burnside, D Page (2013). Predicting Breast Cancer and Prostate Cancer Susceptibility from Single Nucleotide Polymorphisms. In ICML Workshop.

PDF

J Liu, D Page (2013). Structure Learning of Undirected Graphical Models with Contrastive Divergence. In ICML Workshop.

PDF

J Liu, C Zhang, C McCarty, P Peissig, ES Burnside, D Page (2012). Graphical-model Based Multiple Testing under Dependence, with Applications to Genome-wide Association Studies. In UAI.

PDF Code Project

J Liu, C Zhang, C McCarty, P Peissig, ES Burnside, D Page (2012). High-Dimensional Structured Feature Screening Using Binary Markov Random Fields. In AISTAT.

PDF Code Project

J Liu, H Vidaillet, ES Burnside, D Page (2012). A Collective Ranking Method for Genome-wide Association Studies. In ACM-BCB.

Project

J Liu*, Z Hu*, S Tan (2009). Cross-Sectional Stock Return Analysis Using Support Vector Regression. In Applied Economics Letters.

PDF

J Wan, S Kang, C Tang, Y Ren, J Liu, X Gao, A Banerjee, L Ellis, T Li (2008). Meta-prediction of Phosphorylation Sites with Weighted Voting and Restricted Grid Search Parameter Selection. In Nucleic Acids Res.

PDF

J Liu, S Kang, C Tang, LB Ellis, T Li (2007). Meta-prediction of Protein Subcellular Localization with Reduced Voting. In Nucleic Acids Res.

PDF

Jie Liu

Senior Fellow

University of Washington

Biography

Interests

Education

Publications

Projects

Multiple Testing Under Dependence

Personalized Breast Cancer Diagnosis

Tumor Heterogeneity Anaysis

Service

Journal Referee

Conference Program Committee

Awards

Teaching

Courses

Mentees

Contact