Theoretical & Applied Statistical Innovation
I'm Azizur Rahman , obtained my PhD in Computational Biostatistics from University of Manitoba. My research focuses on developing fast approximate Bayesian estimation algorithms for discrete and continuous time models in intensive longitudinal data.
PhD (Computational Biostatistics, Univeristy of Manitoba)
Theoretical and Applied Statistical Innovation Laboratory
The TASi Lab is dedicated to advancing statistical science by integrating rigorous theoretical foundations with cutting-edge applied methodologies. We develop innovative statistical models and computational tools that address complex, real-world challenges across diverse domains.
To be a global leader in statistical innovation, shaping the future of data science through pioneering research and transformative applications.
Meet the TASiLab team
Background and education
Azizur Rahman obtained his PhD in Computational Biostatistics from University of Manitoba, worked under Dr. Depeng Jiang. He is on study leave from Associate Professor at Jahangirnagar University and serves as a Senior Data Scientist at the Social Innovation Office, Government of Manitoba.
PhD (Computational Biostatistics)
Department of Community Health Sciences, University of Manitoba, Canada
Thesis: Fast Approximate Bayesian Estimation Algorithm for Discrete and Continuous Time
Models in Intensive Longitudinal Data.
Supervisor: Dr. Depeng Jiang, Professor, University of Manitoba.
Graduation Completed: October 2026
Master of Philosophy (M.Phil) in Statistics
Dept. of Statistics and Data Science, Jahangirnagar University (2017)
Thesis: Density Estimation of Directional Data - A Circular Kernel Density Estimation.
Supervisor: Dr. Ajit Kumar Majumder, Professor, Jahangirnagar University.
MSc in Statistics
— Jahangirnagar University, 1st Position (2008)
BSc in Statistics
— Jahangirnagar University, 1st Position (2007)
Statistical methods at the intersection of theory and application
Fast approximate Bayesian estimation using variational inference for TAR and Hidden Markov models.
FDA methods for mortality forecasting, influenza patterns, climate, and partial functional data.
Interpretable ML for infant mortality, Alzheimer's prediction, and ECG-based AF detection.
Bayesian logistic regression, change point analysis, hierarchical models for public health.
Disease progression, air pollution dynamics, blood pressure transitions.
Maternal/child health, HIV testing, nutritional status, erosion trajectory modeling.
29 peer-reviewed articles (sorted by year)
Academic events and research talks organized by TASiLab
An application of CMP GAM regression
Speakers: Shafeel Umam & Munniara Yesmin Munni
Speaker 1: PhD Student in Public Health, Saint Louis University
Speaker 2: PM&E Officer, CCDB
Date & Time: February 28, 2026
10:30 AM (Central Time) | 10:30 PM (BD Time)
Meeting ID: 685 1516 2411 | Passcode: 745383
Evidence from an international sample of CRLM patients
Speaker: Dr Mohammed Moinuddin
Lecturer in Biostatistics, School of Medicine and Dentistry, University of Lancashire
Date & Time: January 24, 2026
08:00 AM (Central Time) | 08:00 PM (BD Time)
Zoom ID: 694 9341 6909 | Passcode: 454958
Evidence from the Multiple Indicator Cluster Survey 2019
Speaker: Md. Iqramul Haq
PhD Student, University of Calgary
Date & Time: December 13, 2025
09:00 AM (Central Time) | 09:00 PM (BD Time)
Zoom ID: 640 3612 6609 | Passcode: 247669
Statistical packages developed by TASiLab
An R package for fuzzy logistic regression modeling, enabling robust classification and prediction with fuzzy set theory integration.
Academic positions and supervision
Research and government positions
Data linkages, multivariate analyses, predictive modeling.
Mental Health and Addictions program evaluation.
Statistical methodology research with Dr. Depeng Jiang.
Sampling design for child health research.
Research partnerships and collaborations
Dr. Depeng Jiang – Variational Bayes, Longitudinal Data, HMM
VADA Program – Visual and Automated Disease Analytics
Shuvongkar Sarkar – Air Pollution Modeling, Markov Models
Md. Ismail Hossain Riday – Applied ML, Computational Statistics
Shafeel Umam – Public Health Informatics, Disease Mapping
Md Injamul Haq Methun – Global Health, MCH
Md. Sakaouth Hossain – Remote Sensing, Coastal Geomorphology, Erosion Modeling
PRIME Research Conference 2026- Trainee Poster Competition
CSSC, Carleton University
Case Studies in Data Analysis
Winner
Winner
NSERC, UManitoba & UVictoria
University Grant, Bangladesh
Open to collaborations and opportunities
204-290-7542
Social Innovation Office
Government of Manitoba
Winnipeg, MB, Canada