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Electrical Engineering and Computer Science

Faculty and Staff

Dr. Hani Zakaria Girgis

Associate Professor of Computer Science

Education

National Institutes of Health: Postdoctoral fellowship August 2014
John Hopkins University: Postdoctoral fellowship October 2009
University at Buffalo, The State University of New York: Ph.D., computer science,September 2008
M.S., computer science February, 2005
B.A., biology and computer science, June 2003

Work Experience

Assistant Professor: Texas A&M University-Kingsville October 2019–current
Assistant Professor: University of Tulsa August 2014–May 2019

Journal Publications

  1. A. Zielezinski, H. Z. Girgis, G. Bernard, C.-A. Leimeister, K. Tang, T. Dencker, J.-J. Choi, M. S. Waterman, S.-H. Kim, S. Vinga, J. Almeida, C.-X. Chan, B. T. James, F. Sun, B. Morgenstern, and W. M. Karlowski. Benchmarking of alignment-free sequence comparison methods. Genome Biology, 20(1):144, 2019.
  2. A. Velasco II, B. T. James, V. D. Wells, H. Z. Girgis. Look4TRs: A de-novo tool for detecting simple tandem repeats using self-supervised hidden Markov models. Bioinformatics, btz551, 2019.
  3. J. D. Valencia and H. Z. Girgis. LtrDetector: A tool-suite for detecting long terminal repeat retrotransposons de-novo. BMC Genomics, 20:450, 2019.
  4. H. Z. Girgis, A. Velasco II, Z. E. Reyes. HebbPlot: An intelligent tool for learning and visualizing chromatin mark signatures. BMC Bioinformatics, 19:310, 2018.
  5. B. T. James, B. B. Luczak, and H. Z. Girgis. MeShClust: an intelligent tool for clustering DNA sequences.
  6. Nucleic Acids Research, gky315, 2018.
  7. B. B. Luczak, B. T. James, and H. Z. Girgis. A survey and evaluations of histogram-based statistics in alignment-free sequence comparison. Briefings in Bioinformatics, bbx161, 2017.
  8. H. Z. Girgis. Red: an intelligent, rapid, accurate tool for detecting repeats de-novo on the genomic scale.
  9. BMC Bioinformatics, 16(1):227, 2015.
  10. A. Visel, L. Taher, H. Z. Girgis, M. J. Blow, J. A. Akiyama et al. A high-resolution enhancer atlas of the developing telencephalon. Cell, 152(4):895–908, 2013.
  11. H. Z. Girgis and S. Sheetlin. MsDetector: toward a standard computational tool for DNA microsatellites detection. Nucleic Acids Research, 41(1):e22, 2013.
  12. H. Z. Girgis and I. Ovcharenko. Predicting tissue specific cis-regulatory modules in the human genome using pairs of co-occurring motifs. BMC Bioinformatics, 13(1):25, 2012.

Peer-reviewed Conference Publications

  1. H. Z. Girgis, B. R. Mitchell, T. Dassopoulos, G. Mullin, and G. Hager. An intelligent system to detect Crohn’s disease inflammation in wireless capsule endoscopy videos. In Proceedings of the IEEE International Symposium on Biomedical Imaging, pages 1373–1376, 2010.
  2. H. Z. Girgis, J. J. Corso, and D. Fischer. On-line hierarchy of general linear models for selecting and ranking the best predicted protein structures. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pages 4949–4953, 2009.
  3. S. Seshamani, P. Rajan, R. Kumar, H. Z. Girgis, G. Mullin, T. Dassopoulos, and G. Hager. A meta registration framework for lesion matching. In Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention, pages 582–589, 2009.
  4. K. Ho¨ller, J. Penne, A. Schneider, J. Jahn, H. Z. Girgis, J. Gutierrez, T. Wittenberg, and J. Hornegger. Suppression of shock based errors with gravity related endoscopic image rectification. In Proceedings of the Fifth Russian-Bavarian Conference on Biomedical Engineering, pages 43–47, 2009.
  5. H. Z. Girgis and B. Jayaraman. JavaTA: a logic-based debugger for Java. In the Workshop on Logic-based Methods in Programming Environments, volume abs/cs/0701107, 2007.
  6. H. Z. Girgis, B. Jayaraman, and P. Gestwicki. Visualizing errors in object oriented programs. In Pro-ceedings Companion to the ACM SIGPLAN Conference on Object-oriented Programming, Systems, Languages, and Applications, pages 156–157, 2005.

Main research interests

Bioinformatics, Machine learning, Computational genomics, Sequence analysis, and non-coding DNA.

Main teaching interests

Bioinformatics, Machine learning, Data structures, Programming languages, Software engineering, and Operating systems.
Dr. Hani Zakaria Girgis