Tufazzul Miah Saju is a Lecturer in the Statistics Section and Acting Chairman in the Department of
Biometry at Habiganj Agricultural University, Bangladesh. He holds an M.S. and B.Sc. (Hons.) in Statistics
from Mawlana Bhashani Science and Technology University, Tangail. His academic background includes
statistical theory, applied statistics, data science, and computational methods. His research focuses on
statistical modeling and machine learning applications in public health and social science, emphasizing
predictive modeling and data-driven approaches to address complex real-world problems. His M.S. thesis
examined mental health prediction among reproductive-age women using BDHS data using statistical and
machine learning techniques. He has manuscripts under review and in press in peer-reviewed journals and
has presented at national and international conferences. He is proficient in R, SPSS, Stata, SQL, and
MATLAB, along with data visualization tools. His career goal is to contribute to quality education,
evidence-based policy development, and the advancement of the Sustainable Development Goals through
impactful research and academic leadership in statistics and data science.
Conference Proceedings:
1. Saju, T. M. (2025, August 22–23). Integrated statistical and machine learning
approaches for predicting mental health among reproductive-age women: A
comparative model evaluation using BDHS-2022 data. Presented at the 19th
National Statistical Conference, Bangladesh Statistical Association, Dhaka,
Bangladesh.
2. Saju, T. M. (2025, September 6–7). Comparative analysis of SMOTE and ROSE for handling class imbalance in mental health prediction among ever-married women using ensemble and non-ensemble machine learning models. Presented at the 1st International Research Conference, University of Rajshahi, Bangladesh.
Project:
1. M.Sc. Thesis
• Status: Completed
• Title: Integrated Statistical and Machine Learning Approaches for Predicting
Mental Health Among Reproductive-Age Women
• Duration: 2023–2024
• Funding Agency: Self-funded (Academic Research)
• Role: Student Researcher (Under Supervision)
2. B.Sc. Project
• Status: Completed
• Title: Determining the Relationship Between Non-Communicable Diseases and
Mental Health
• Duration: 2021–2022
• Funding Agency: Self-funded (Academic Research)
• Role: Student Researcher (Under Supervision)
Research Interest:
• Statistical Modeling & Data Analysis
• Machine Learning Applications
• Biostatistics & Epidemiology
• Public Health Data Analysis
• Agricultural & Biometric Data Analysis
Research philosophy:
My research philosophy integrates statistical theory with data-driven approaches to address complex real-world problems. I apply statistical and machine learning methods to develop practical, scalable, and reproducible solutions, emphasizing methodological rigor, transparency, and relevance. My work focuses on interpretable predictive models that support evidence-based decision-making, interdisciplinary collaboration, ethical research practices, and continuous innovation for societal impact.
