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:
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.
- 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 (email@example.com) with your application materials.
SS-18: CMSE 201 Intro to Computational Modeling
Click "Teaching" link to see past courses.