Dr. Ehsan Ullah


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Dr. Ehsan Ullah

Software Engineer

Software Engineering Team

eullah@hbku.edu.qa

Dr. Ehsan is a Software Engineer at Qatar Computing Research Institute. His work was focused on developing techniques and tools for biological applications before moving to the separate Software Engineering Team.

Besides developing techniques and tools, he also worked on data driven approaches for modeling of biological systems.

Dec, 2014 - Apr, 2018: Post-Doctoral Researcher, Qatar Computing Research Institute, Hamad Bin Khalifa University, Qatar
Developed LASSO based approach for GWAS. Analyzed Qatar Biobank Data for Diabetes and Obesity Biomarker identification in Qatari Population.


2008-2014: Doctorate at Tufts University, Boston, USA
Specialization: Computer Science
Thesis: Pathway Analysis of Metabolic Networks using Graph Theoretical Approaches

2003-2007: Masters at UET Lahore, Pakistan
Specialization: Computer Engineering
Thesis: Cancer Classification using Gene Expression Data

1999-2002: Bachelors at UET Lahore, Pakistan
Specialization: Electrical Engineering


  • Ullah, E., Mall, R., Rawi, R., Moustaid, N.M., Butt, A.A. and Bensmail, H., 2018. Harnessing Qatar Biobank to understand type 2 diabetes and obesity in adult Qataris from the First Qatar Biobank Project. Journal of translational medicine, 16(1), p.99.
  • Al Meer, H., Mall, R., Ullah, E., Megrez, N. and Bensmail, H., 2018, April. ISaaC: Identifying Structural Relations in Biological Data with Copula-Based Kernel Dependency Measures. In International Conference on Bioinformatics and Biomedical Engineering (pp. 71-82). Springer, Cham.
  • Mall, R., Ullah, E., Kunji, K., Ceccarelli, M. and Bensmail, H., 2018. An unsupervised disease module identification technique in biological networks using novel quality metric based on connectivity, conductance and modularity. F1000Research, 7.
  • Kunji, K., Ullah, E., Nato, A.Q., Wijsman, E.M. and Saad, M., 2017. GIGI-Quick: A Fast Approach to Impute Missing Genotypes in Genome-Wide Association Family Data. Bioinformatics, p.btx782.
  • Ullah, E., Mall, R., Bensmail, H., Rawi, R., Shama, S., Al Muftah, N. and Thmpson, I.R., 2017, November. Identification of cancer drug sensitivity biomarkers. In Bioinformatics and Biomedicine (BIBM), 2017 IEEE International Conference on (pp. 2322-2324). IEEE.
  • Mall, R., Ullah, E., Kunji, K., Bensmail, H. and Ceccarelli, M., 2017, November. An adaptive refinement for community detection methods for disease module identification in biological networks using novel metric based on connectivity, conductance & modularity. In Bioinformatics and Biomedicine (BIBM), 2017 IEEE International Conference on (pp. 2282-2284). IEEE.
  • Mall, R., Ullah, E., Kunji, K., D'Angelo, F., Bensmail, H. and Ceccarelli, M., 2017, August. Differential Community Detection in Paired Biological Networks. In Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (pp. 330-339). ACM.
  • Hassanpour, N., Ullah, E., Yousofshahi, M., Nair, N.U. and Hassoun, S., 2017. Selection Finder (SelFi): A computational metabolic engineering tool to enable directed evolution of enzymes. Metabolic Engineering Communications, 4, pp.37-47.
  • Ullah, E., Aeron, S. and Hassoun, S., 2016. gEFM: an algorithm for computing elementary flux modes using graph traversal. IEEE/ACM transactions on computational biology and bioinformatics, 13(1), pp.122-134.
  • Ullah, E., Shahzad, M., Rawi, R., Dehbi, M., Suhre, K., Selim, M., Mook, D. and Bensmail, H., 2015. Integrative 1H-NMR-based metabolomic profiling to identify type-2 diabetes biomarkers: An application to a population of Qatar. Metabolomics, 5(1), p.1.
  • Sridharan, G.V., Ullah, E., Hassoun, S. and Lee, K., 2015. Discovery of substrate cycles in large scale metabolic networks using hierarchical modularity. BMC systems biology, 9(1), p.5.
For other research see his Google Scholar page