The Department of Computer Science and Mathematics is an important research hub within LAU, with specific interests in the following areas.
Protein structure prediction
We have been exploring computational solutions for predicting tertiary protein structures. The focus has been on the use of metaheuristics for ab initio solutions.
We have been exploring computational techniques for discovering associations between disease, such as some type of cancer, and genes. For this purpose, we have been experimenting with data mining and evolutionary algorithms.
Electronic design automation and high-level synthesis
High-level synthesis is the process of transforming a behavioral description into a structural one. From the input specification, the synthesis system produces a description of a datapath, that is, a network of registers, functional units, multiplexers and buses. The synthesis must also produce the specification of the control path. There are many different structures that can be used to realize a given behavior. One of the main tasks of high-level synthesis is to find the structure that best meets the constraints while minimizing other costs. Research in this area aims at the development of CAD tools and methodologies for highly testable electronic systems at the behavioral and structural levels.
Rule-based software quality estimation
Assessing software quality is very important in the software developing field as it helps reduce cost, time and effort. However, most of the software quality characteristics such as stability and maintainability cannot be directly measured. However, they can be estimated based on other measurable attributes. For this purpose, machine learning algorithms have been extensively used to build software quality estimation models. These models build a relationship between what can be directly measured and what can only be predicted/estimated. Rule-based models are the most widely used due to their white-box nature. However, the accuracy of these models deteriorates when we use them to estimate the quality of new software components. Research in this area concentrates on genetic algorithm-based approaches to optimize already existing rule-based software quality estimation models. The research touches on three fields in computer science: machine learning, evolutionary computation and software quality.
- Contact person: Dr. Danielle Azar
SOC design and embedded systems
Recent advances in semiconductor process technologies enable the integration of an entire system on a chip (SOC) based on a reuse philosophy that divides the CAD community into core providers and core integrators. Core providers create embedded cores that are pre-designed and pre-verified complex logic blocks. Research in this area aims at the development of tools for SOC test scheduling, test access mechanism design, and SOC integration.
Testing and regression testing
We have been developing code-based testing techniques to gain confidence in the correctness of web applications that include dynamic features. Also, we have developing regression testing algorithms to provide confidence in modified programs based on design and code information.
- Contact person: Dr. Nashat Mansour
We have been developing algorithms for timetabling university exams and courses.
- Contact person: Dr. Nashat Mansour
Non-smooth analysis and optimal control
Non-smooth analysis refers to differential analysis in the absence of differentiability. It can be regarded as a branch of a vast subject known as nonlinear analysis. Since its inception in the early 1970s, there has been a sustained and fruitful interplay between non-smooth analysis and optimal control. A familiarity with non-smooth analysis is, therefore, essential for an in-depth understanding of present day research in optimal control. The main object of our research involves the application of new methods from non-smooth analysis for the study of certain Hamilton-Jaccobi equations arising in optimal control. The viscosity methods play an important role in this research.