Maz Jamilah Masnan

Dr. Associate Professor

Maz Jamilah Masnan is an academician and researcher in the field of applied mathematics and statistical modelling, currently affiliated with Universiti Malaysia Perlis (UniMAP). With extensive experience in teaching, research, and academic leadership, she has contributed significantly to the advancement of statistical programming, data analytics, and computational intelligence in engineering and applied sciences.

Her research expertise lies primarily in statistical modelling, machine learning, and multivariate data analysis, with a strong focus on real-world applications. She has been actively involved in interdisciplinary research spanning healthcare analytics, engineering systems, and intelligent sensing technologies. Her recent works include the application of data fusion techniques and machine learning models for medical diagnosis, particularly in improving classification performance for diseases such as diabetic retinopathy and dengue. She is also experienced in predictive modelling, logistic regression, discriminant analysis, and feature selection methods, which are widely applied in high-dimensional and complex datasets.

Maz Jamilah has a strong background in computational intelligence and pattern recognition, including the use of electronic nose (e-nose) and sensor-based systems for classification and detection problems. Her earlier research contributed to the development of intelligent systems for bacterial detection, food quality assessment, and agricultural applications using multivariate statistical techniques and data fusion approaches. This demonstrates her ability to bridge theoretical statistical methods with practical engineering solutions.

In addition to her research, she is actively involved in academic development and knowledge dissemination. She has authored and co-authored numerous academic modules and publications, including books on engineering statistics and mathematics, as well as journal articles indexed in Scopus and other recognized databases. She is also a frequently invited speaker, trainer, and workshop facilitator in areas such as supervised learning, statistical analysis using R, and research methodology.

Her academic contributions extend to research supervision, examination, and scholarly reviewing, where she has served as an internal examiner for PhD and MSc candidates, journal reviewer for multiple indexed journals, and evaluator for academic and innovation competitions. Her engagement reflects a strong commitment to maintaining academic quality and advancing research standards.

Overall, Maz Jamilah Masnan’s work is characterized by the integration of statistical theory, machine learning techniques, and applied analytics, with a focus on solving complex problems in healthcare, engineering, and data-driven decision-making. Her multidisciplinary approach positions her as a key contributor in the evolving field of data science and applied statistics