Rate My Professor Mohammad Haque

Dr. Mohammad Haque is a Postdoctoral Research Associate in the School of Electrical Engineering and Computing, Faculty of Engineering and Built Environment, at the University of Newcastle, Australia. He holds a Doctor of Philosophy in Computer Science from the University of Newcastle, awarded in 2017 for his thesis 'Genetic algorithm-based ensemble methods for large-scale biological data classification.' His earlier education includes two Bachelor's degrees in Computer Science and Engineering from Daffodil International University in Bangladesh, completed between 2002-2006 and 2010-2011. Professionally, he served as an Associate Professor in the Department of Computer Science and Engineering at Daffodil International University from April to August 2012, teaching courses including Structured Programming, Data Structures, Computer Algorithms, E-Commerce, and Web Programming, while supervising theses and coaching programming teams. From July 2016 to December 2017, he worked as a Casual Academic at the University of Newcastle, contributing to teaching, lecturing, and curriculum development. He received the University of Newcastle Postgraduate Research Scholarship (UNIPRS) and University of Newcastle Research Scholarship Central (UNRSC) in 2012.

Haque's research encompasses evolutionary computing, optimisation, ensemble methods, machine learning for imbalanced data, cloud computing service selection, network alignment, continued fractions for machine learning and mathematical modeling, medical image reconstruction, meta-analytics, and community detection in networks. His interdisciplinary work applies to biological data, business analytics, image processing, toxicology, and material science. Key publications include 'Heterogeneous Ensemble Combination Search Using Genetic Algorithm for Class Imbalanced Data Classification' (2016), 'Cloud Service Selection Using Multicriteria Decision Analysis' (2014), 'Analytic Continued Fractions for Regression: A Memetic Algorithm Approach' (2021), 'Learning to Extrapolate Using Continued Fractions: Predicting the Critical Temperature of Superconductor Materials' (2023), 'Mathematical Modelling of Peak and Residual Shear Strength of Rough Rock Discontinuities Using Continued Fractions' (2023), and 'The (α, β)-k Boolean Signatures of Molecular Toxicity: Microcystin as a Case Study' (2024). With over 30 publications and more than 500 citations, his contributions demonstrate impact across computer science and applied domains. Haque is proficient in C, C++, pattern recognition, signal and image processing, feature extraction, and classification, with experience in software design, coding, debugging, and high-pressure team projects.