Research Interests

I am a computer scientist whose primary focus is on developing novel machine learning methods to extract useful information from complex datasets. My experience, however, has covered intellectually wide-ranging areas spanning several disciplines in computer science, biology, and chemistry.

At the undergraduate level, I focused on computer networks, and I did an internship for six months at Cisco Systems Inc., and I achieved CCNA and CCNP certification. At the Master's level, however, I joined the Computational Bioscience Research Center. I was in charge of a text-mining project to extract associations between methylated genes and diseases automatically from the scientific literature.

As an ambitious researcher, my aspiration has expanded. I wanted to investigate another research area to which my skills are transferable. I chose cheminformatics. I currently work on developing multi-label classification methods for datasets with missing labels, which requires solving several associated problems including feature selection and resampling imbalanced multi-label datasets with missing labels. The primary motivation for this project is to develop predictive multi-label classification models to predict several toxic effects of chemical compounds. Subsequently, the scope of the project has been extended to include other domains such as text, images, and biological datasets, as well as utilizing supercomputing clusters for efficient application of these methods.

Additionally, I wanted to explore another way to be influential by becoming a Teaching Assistant for Master and Ph.D. level computer science courses. Currently, I mentor students enrolled to the Applications of Artificial Intelligence in Bioinformatics course to complete several research projects including DNA transcription initiation sites recognition, DNA PolyA signal recognition, DNA acceptor and donor splice site recognition, and acronym finding from biomedical literature.

Professional Profile

  • Spring 2018: Teaching Assistant for Applications of Artificial Intelligence in Bioinformatics Course, KAUST, Thuwal, Saudi Arabia
  • Spring 2016: Teaching Assistant for Systems Programming and Architecture Course, KAUST, Thuwal, Saudi Arabia
  • Spring 2015: Teaching Assistant for High-Performance Computing and Architecture Course, KAUST, Thuwal, Saudi Arabia
  • Spring 2014: Teaching Assistant for High-Performance Computing and Architecture Course, KAUST, Thuwal, Saudi Arabia
  • Spring 2013: Teaching Assistant for Programming Languages Course, KAUST, Thuwal, Saudi Arabia
  • Summer 2012: Intern, Dow Research Center, KAUST, Thuwal, Saudi Arabia
  • February 2011 – August 2011: Intern, Cisco Systems Inc., Riyadh, Saudi Arabia
  • 2009-2011: Events Organizer, Community Service, and Continuing Education Center, PSU, Riyadh, Saudi Arabia
  • Spring 2010: Team Leader, Software Development Unit, PSU, Riyadh, Saudi Arabia

Scientific and Professional Memberships

  • Association for Computing Machinery (ACM)
  • Society for Industrial and Applied Mathematics (SIAM)

Education Profile

  • MS with a thesis, Computer Science, KAUST, Thuwal, Saudi Arabia, 2012
  • BS Computer Science, Prince Sultan University, Riyadh, Saudi Arabia, 2011

Awards and Distinctions

  • KAUST Fellowship for Ph.D. students, 2013
  • KAUST Fellowship for Master students, 2011
  • KAUST Provost Award for outstanding entering Master students, 2011
  • Prince Sultan University Scholarship, 2006

Selected Publications

Medvedeva, Y.A., Khamis, A.M., Kulakovskiy, I.V., Ba-Alawi, W., Bhuyan, M.S., Kawaji, H., Lassmann, T., Harbers, M., Forrest, A.R., Bajic, V.B.; FANTOM consortium (2014) Effects of cytosine methylation on transcription factor binding sites. BMC Genomics. 2014; 15: 119. doi: 10.1186/1471-2164-15-119.
Raies, A. B., & Bajic, V. B. (2016). In silico toxicology: computational methods for the prediction of chemical toxicity. Wiley Interdisciplinary Reviews: Computational Molecular Science, 6(2), 147–172. doi:10.1002/wcms.1240
Bin Raies, A., Mansour, H., Incitti, R., & Bajic, V. B. (2013). Combining Position Weight Matrices and Document-Term Matrix for Efficient Extraction of Associations of Methylated Genes and Diseases from Free Text. PLoS ONE, 8(10), e77848. doi:10.1371/journal.pone.0077848
Raies, A. B., Mansour, H., Incitti, R., & Bajic, V. B. (2014). DDMGD: the database of text-mined associations between genes methylated in diseases from different species. Nucleic Acids Research, 43(D1), D879–D886. doi:10.1093/nar/gku1168