Education Profile

  • Ph.D. Computer Science, University of Utah, Utah, U.S.A., 2008
  • B.S. Computer Science, University of Utah, Utah, U.S.A., 2001

Honors & Awards

  • Ray and Stephanie Lane Fellowship, School of Computer Science, Carnegie Mellon University, 2009-2012

Research Interests

​My area of research is broadly in computational systems and synthetic biology. My main research focus is on the theoretical understanding of how various uncertainties contribute to the relation between genotypes and phenotypes through the development and the use of quantitative modeling and analysis methods. In particular, I am interested in gaining quantitative insights into how biological functions have emerged from stochastic interactions of molecules, how they might have evolved, and how they can be modified for specific objectives.

Professional Profile

  • 2012-current: Research Scientist, KAUST, Thuwal, Saudi Arabia
  • 2009-2012: Ray and Stephanie Lane Fellow, Carnegie Mellon University, Pittsburgh, USA
  • 2008-2009: Junior Researcher, Microsoft Research - University of Trento CoSBi, Trento, Italy

Selected Publications

Fan, M., Kuwahara, H., Wang, X., Wang, S., & Gao, X. (2015). Parameter estimation methods for gene circuit modeling from time-series mRNA data: a comparative study. Briefings in Bioinformatics, 16(6), 987–999. doi:10.1093/bib/bbv015
Kuwahara, H., & Gao, X. (2013). Stochastic effects as a force to increase the complexity of signaling networks. Scientific Reports, 3(1). doi:10.1038/srep02297
Kuwahara, H., Fan, M., Wang, S., & Gao, X. (2013). A framework for scalable parameter estimation of gene circuit models using structural information. Bioinformatics, 29(13), i98–i107. doi:10.1093/bioinformatics/btt232
Fujii, C., Kuwahara, H., Yu, G., Guo, L., & Gao, X. (2017). Learning gene regulatory networks from gene expression data using weighted consensus. Neurocomputing, 220, 23–33. doi:10.1016/j.neucom.2016.02.087