Jianrong Wang

Jianrong Wang

Assistant Professor, Department of Computational Mathematics, Science & Engineering;
Room 2507J, Engineering Building
  428 S. Shaw Ln.
 (517) 432-0370

Ph.D., 2012, Bioinformatics, Georgia Institute of Technology

B.S., 2007, Control Science and Engineering, Tsinghua University


Jianrong Wang’s interdisciplinary research is in the fields of bioinformatics, machine learning, gene regulation and systems biology, with a focus on developing novel statistical models and machine learning algorithms to infer large-scale context-dependent gene regulatory networks and their association with complex human disease (e.g. cancer) by integrating heterogeneous high-dimensional datasets.

Jianrong is also actively collaborating with experimental experts in cancer research, neurogenomics, genetics and proteomics. The efficient interactions between experiment and computation facilitate the transformative capacity of our innovative methodology development, leading to deeper biological insights and better biomedical approaches.

Four major computational biology research topics include:

Probabilistic modeling of long-range three-dimensional enhancer-gene networks in diverse cellular contexts and inference of hierarchical regulatory logic of combinatorial transcription factors on gene expression.
Network-based prediction and functional annotation of non-coding genetic variants associated with human diseases and traits, including cancer.
Machine learning algorithms to predict regulatory elements of gene expression (insulators, enhancers and boundary elements), large-scale chromatin domains, and histone modification signatures (‘histone-code’) based on genomics, epigenomics and transcriptomics data.
Statistical and computational methodology development for efficient design, analysis and interpretation of biological big-data generated from new high-throughput techniques.
Jianrong Wang, Cristina Vicente-Garcia, Davide Seruggia, Eduardo Molto, Ana Fernandez-Minan, Ana Neto, Elbert Lee, Jose Luis Gomez-Skarmeta, Lluis Montoliu, Victoria V. Lunyak and I. King Jordan. MIR retrotransposon sequences provide insulators to the human genome. 2015 Proc Natl Acad Sci USA 112(32):E4428-4437.
Roadmap Epigenomics Consortium et al. Integrative analysis of 111 reference human epigenomes. 2015 Nature 518:317-330. (integrative analysis lead author)
Jianrong Wang, Victoria V. Lunyak and I. King Jordan. BroadPeak: a novel algorithm for identifying broad peaks in diffuse ChIP-seq datasets. 2013 Bioinformatics 29(4):492-493.
Jianrong Wang, Victoria V. Lunyak and I. King Jordan. Chromatin signature discovery via histone modification profile alignments. 2012 Nucleic Acids Research 40(21):10642-10656.
Jianrong Wang, Victoria V. Lunyak and I. King Jordan. Genome-wide prediction and analysis of human chromatin boundary elements. 2012 Nucleic Acids Research 40(2):511-529, (Cover Story).
Jianrong Wang*, Glenn J. Geesman*, Sirkka Liisa Hostikka, Michelle Atallah, Benjamin Blackwell et al. Inhibition of activated pericentromeric SINE/Alu repeat transcription in senescent human adult stem cells reinstates self-renewal. 2011 Cell Cycle 10(17):3016-3030.
Jianrong Wang, Ahsan Huda, Victoria V. Lunyak and I. King Jordan. A Gibbs sampling strategy applied to the mapping of ambiguous short-sequence tags. 2010 Bioinformatics 26(20):2501-2508.


Research in Dr. Wang’s lab is in the intersection of machine learning, statistical modeling, bioinformatics and computational biology, with a focus on building novel hierarchical Bayesian models and efficient learning algorithms to construct large-scale regulatory networks and to decode the genetic basis of human diseases, based on big-data from genomics. The postdoc positions will be supported by NIH. Detailed information of Dr. Wang and his research can be found:


The candidates are expected to be highly self-motivated and have a strong passion for scientific research. Candidates should have (or expect to have soon) a PhD degree in related fields (bioinformatics, statistics, computer science, mathematics etc), have solid background in machine learning and statistical modeling, and have strong programming skills in Python or R. Candidates with prior research experiences in computational biology, bioinformatics, functional genomics, gene regulation, NGS data processing are preferred.

Application materials:

  • CV;
  • Statement of prior research experiences and future research interests (<=2 pages);
  • Two publications (as first author or co-first author) from prior research;
  • Contact information of 3 references.

If you are interested, please send an email to Dr. Wang (wangj164@msu.edu) with your application materials.


SS-18: CMSE 201 Intro to Computational Modeling

Click "Teaching" link to see past courses.