Danielle Azar

Associate Professor of Computer Science

Dr. Danielle Azar is an associate professor of computer science, and was the chairperson of the Computer Science and Mathematics Department. Dr. Azar holds a PhD in Computer Science from McGill University, Canada. She joined LAU as an assistant professor of computer science in 2004.

She has been teaching courses in Machine Learning, Artificial Intelligence, Heuristics, Object-Oriented Programming, Theoretical Computer Science, Advanced Algorithms, Software Engineering, Operating Systems, Algorithms and Data Structures among others both at the graduate and undergraduate levels.

Research Interests

Dr. Azar’s research interests lie in the field of artificial intelligence specifically machine learning and meta-heuristics, empirical software engineering and search-based software engineering. Her work focuses on the design of approaches to optimize machine learning prediction models. She is particularly interested in the transfer of knowledge/expertise between different domains.

Professional Memberships

Dr. Azar is a member of the United Nations ESCWA Knowledge Hub and a CERN deputy team leader.

Selected Publications

  1. Israa Alnazer, Omar Falou, Remie Nasr, Danielle Azar Eno Hysi, Lauren Wirtzfeld, Elizabeth S. L. Benrdl, Michael C. Kolios, Qualitative Ultrasound Imaging for the Differentiation between Fresh and Decellularized Mouse Kidneys,  41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany, 2019, pp. 6624-6627.
  2. Firas Gerges, Germain, Zouein, Danielle Azar, Genetic Algorithms with Local Optimal Handling to Solve Sudoku Puzzles,  Proceedings of the 2018 International Conference on Computing and Artificial Intelligence, March 2018 pp.19–22.
  3. Christian Salem, Danielle Azar, Sima Tokajian, An Image Processing and Genetic Algorithm-Based Approach for the Detection of Melanoma in Patients. In  Methods of Information in Medicine, 2018.
  4. Rebecca Moussa and Danielle Azar, A PSO-GA approach targeting fault-prone modules. In Journal of Systems and Software, Volume 132, Pages 41-49, October 2017.
  5. Danielle Azar, Karl Fayad and Charbel Daoud, A Combined Ant Colony Optimization and Simulated Annealing Algorithm to Assess Stability and Fault-Proneness of Classes Based on Internal Software Quality Attributes. In International Journal of Artificial Intelligence, Vol. 14, Number 2, October 2016.
  6. D. Azar, M. Bitar. AI-Based Methods for Predicting Required Insulin Doses for Diabetic Patients. In International Journal of Artificial Intelligence, Vol. 13, Issue 1, pp. 8-24, March 2015.
  7. D. Azar, J. Vybihal. An Ant Colony Optimization Algorithm to Improve Software Quality Predictive Models. In Journal of Information and Software Technology, Vol. 53, Issue 4,  pp. 388-393, April 2011.
  8. Danielle Azar, A Genetic Algorithm for Improving Accuracy of Software Quality Predictive Models: A Search-based Software Engineering Approach. In  International Journal of Computational Intelligence and Applications, Vol. 9, No. 2,  pp. 125-136,  Imperial College Press, June, 2010.
  9. Izadi Tabaeh, M., Precup D., Azar, D. Design and Analysis of Belief Selection in Point-Based POMDP Approximation. In The 19th Canadian Conference on Artificial Intelligence, Quebec City, Canada, June 2006.


  • J. Vybihal, D. Azar Software Systems, 2020. Kendall-Hunt Pub. ISBN 9781524988654

Academic Degrees

  • PhD in Computer Science, 2004, McGill University, Canada.
  • MS in Computer Science, Lebanese American University.
  • BS in Computer Science, Lebanese American University.