I am a senior lecturer at the School of Computing, with a PhD. in Computer Science, specializing in Artificial Intelligence techniques for analyzing biological data. My research interest is in designing machine learning algorithms as services in cloud environments. I am holding a position as the Chief Executive Officer (CEO) for my spin-off company, Synapse Innovation Sdn. Bhd., which I co-founded in 2019. Currently, the company is focusing on commercializing my research to the industries, majorly in the sectors of manufacturing, healthcare, and agriculture.

Doctor of Philosophy (Computer Science) 2013
Honor Level: Distinctive (Magna cum laude)
Thesis Title: Parameter Estimation of Microbial Models using Hybrid Optimization Methods
The development of biological models is essential as it represents and predicts complex processes within microbial cells. These models are formed by mathematical formulations that depend heavily on a set of parameters whose accuracy is often influenced by noisy and incomplete experimental data. This study is aiming to design and develop new optimization methods that can adequately estimate these parameters by iteratively fitting the model outputs to the experimental data. In this project, two new hybrid optimization methods based on the Firefly Algorithm (FA) method are proposed. Firstly, a technique using evolutionary operations from the Differential Evolution (DE) method was developed to improve the estimation accuracy of the parameters. Then, a second method using the Chemical Reaction Optimization (CRO) method was proposed to surmount the convergence speed problem during parameter estimation. The effectiveness of the proposed methods was evaluated using a synthetic transcriptional oscillator and extracellular protease production models. Computational experiments showed that these methods were able to estimate plausible parameters that produced model outputs that closely fitted in the experimental data. Statistical validation confirmed that these methods are competent at estimating the identifiable parameters. These findings are crucial to ensure that the estimated parameters can generate predictive and sensitive model outputs. In conclusion, this study has presented new hybrid optimization methods, capable of estimating the model parameters effectively whilst taking into account noisy and incomplete experimental data.

Master of Science (Computer Science) 2009
Cumulative Grade Point Average (CGPA): 4.00 / 4.00
Thesis Title: Clustering Functional Modules from Protein Interaction Networks
System-level understanding of biological organization is a crucial aspect in this post-genomic era. This is due to the fact that biological systems are made of many non-identical elements that interacted by diverse and various interactions. Therefore, these biological networks worked as a framework for biological systems study. Recent high-throughput experiments had produced a huge number of interactions data such as protein-protein interaction which in turn forming the biological networks. Furthermore, many pair-wise interactions proposed by genomic- context analyses basis, metabolic maps, and also the experimental approaches had presented networks of interactions in which the majority of genes and proteins have connected each other. Clustering the large-scale protein interaction network is used to extract modules with highly connected proteins that majorly shared common functions. These modules, which called functional modules, will be used to predict functions to uncharacterized proteins. Functional modules are defined as sets of interacting proteins that shared common functions in a single process.

Bachelor of Science (Computer Science) 2007
Cumulative Grade Point Average (CGPA): 3.33 / 4.00

Gold Award, Malaysia Technology Expo (MTE) 2020
Gold Award, Industrial Art & Technology Exhibition (INATEX) 2019
Excellent Service (Commercialization) 2019, Universiti Teknologi Malaysia (UTM)
Excellent Service (Industry Linkage) 2019, Universiti Teknologi Malaysia (UTM)
Excellent Service (Industry Linkage) 2015, Universiti Teknologi Malaysia (UTM)
MIMOS Prestigious Award 2013, MIMOS Berhad
Academic Excellent Award 2013, 51st Convocation of Universiti Teknologi Malaysia (UTM)