I hold the position of Professor (JUSA C) in Epidemiology and Statistics at the School of Medical Sciences, Universiti Sains Malaysia, Kelantan, Malaysia. I am an academic and clinician-scientist with over two decades of experience in medical epidemiology, biostatistics, disease modelling, and the application of artificial intelligence in health and medicine.
I am actively engaged in the education of undergraduate medical students and play a central role in postgraduate training, with primary involvement in the Master of Public Health (MPH), Doctor of Public Health (DrPH) and MSc (Medical Statistics) programmes. My teaching philosophy integrates rigorous quantitative methods with real-world clinical and public health applications.
My research spans the full spectrum of epidemiological inquiry — from communicable and non-communicable disease modelling to advanced statistical computing, machine learning, and deep learning methodologies applied to medical data. I am a dedicated advocate for open-source analytical tools, with the majority of my computational work conducted in R and Python.
I am a gazetted Public Health Physician registered with the Ministry of Health, Malaysia, and a Fellow of the American College of Epidemiology (FACE). I value leadership and have secured and managed competitive research grants at both national and international levels. I have led a Newton-Ungku Omar Fund — an international collaborative grant with researchers from the London School of Hygiene and Tropical Medicine — focused on developing evidence-based solutions to caregivers for stroke patients in Malaysia.
I have also led a Fundamental Research Grant Scheme (FRGS) investigating the role of clinical characteristics in breast cancer diagnosis using statistical and machine learning approaches, and a Research University (RU) grant examining cardiovascular and stroke risk factors among the Malaysian population. These projects reflect my commitment to translating epidemiological evidence into actionable public health and clinical insights.
To date, I have authored or co-authored more than 150 articles indexed in SCOPUS, accumulating over 65,000 citations and an h-index of 48. I have also authored two books published by USM and another one by CRC Press — all focused on data analysis and epidemiological methods in medicine and public health.
Academic Qualifications
I obtained my undergraduate medical degree (MD, USM) in 1997. I subsequently completed a four-year Master of Community Medicine with specialisation in Epidemiology and Biostatistics (USM) in 2005. In 2017, I was awarded a PhD in Statistics and Epidemiology from Lancaster University, United Kingdom, under the supervision of Dr Thomas Keegan and Distinguished Professor Peter Diggle — a world-renowned authority in spatial statistics and geostatistics.
Lancaster University
Front gate
From above
Areas of Expertise
Medical Epidemiology & Disease Modelling
My primary academic and research focus lies in medical epidemiology and disease modelling. I conduct and supervise research on communicable and non-communicable diseases, employing a broad range of quantitative techniques including spatial epidemiology, survival analysis, longitudinal modelling, and compartmental disease transmission models. My work provides critical insights for public health decision-making and disease surveillance across Malaysia and beyond.
Biostatistics & Statistical Modelling
With deep expertise in biostatistics, I apply general and generalised linear models, mixed-effects models, and Bayesian statistical approaches to complex health data. I routinely employ a diverse range of study designs — including cross-sectional surveys, cohort studies (with survival and longitudinal analyses), and clinical trials — to address pressing questions in clinical and population health research.
Statistical Computing
I am a strong advocate for reproducible and transparent research. He conducts the vast majority of his analytical work using R (via RStudio), with additional proficiency in Python. Selected projects and code repositories are publicly available on GitHub. I also have prior experience with STATA and SPSS.
Artificial Intelligence & Predictive Analytics in Health
I am actively applying artificial intelligence and machine learning techniques to medical and health data. My work encompasses classical machine learning algorithms, ensemble methods, and deep learning models for disease prediction, clinical risk stratification, and health outcome forecasting. I have served as a sessional lecturer at Monash University Malaysia, where I taught Data Analytics for Business, further demonstrating the breadth of my expertise across disciplines.