Ongoing Researches

• Research Interest

  Shamima Hossain’s research focuses on Bayesian Statistics, spatio-temporal modeling, and machine learning, with applications in public health, epidemiology, and climate data analysis. She is particularly interested in developing Bayesian Neural Networks (BNNs) and predictive models to address societal challenges through data-driven solutions.

Area of Specialization

Shamima Hossain specializes in Bayesian Statistics, Generalized Linear Models (GLMs), and Spatio-Temporal Modeling, with extensive expertise in applying advanced statistical methodologies to solve real-world problems. Her focus areas include epidemiology, public health analytics, and climate data modeling, with a particular interest in developing robust Bayesian Neural Networks (BNNs) for high-dimensional data analysis. She is also proficient in statistical software such as R, Python, SPSS, and STATA, enhancing her capabilities in computational statistics and predictive modeling. Through her academic and research endeavors, she has contributed significantly to understanding societal challenges, including public health crises and women's empowerment, using statistical frameworks.

Research Projects:

World Bank-Funded HEQEP Project: Research Team Member -“Covariate Dependent Models for Correlated Outcomes in    Longitudinal Data Analysis, 2014 to 2017.

• Bayesian Network and modelling
• Bio-statistics, epidemiology  and Statistical Computation
• Statistical Modeling, Longitudinal Data Analysis, causal Inference, Povarty issue analysis

Biostatistics

Clinical Trial

Data Sciences

Artificial Intelligence 

• Stochastic analysis 
• Statistical modelling
• Biostatistics
• Agricultural Statistics
• Experimental Design

• Bioinformatics
• Machine Learning
• Big Data
• Programming & Simulation

• Dynamical System
• Mathematical modeling in Biology

Materials Science 

Numerical methods, Biomathematics, Fractional partial differential equations.

• Mathematical Biology
• Mathematical Sociology
• Fluid Dynamics

Plasma Physics, Biophysics.

Computational fluid Dynamics(CFD)

• Mathematical Biology
• Machine Learning
• Deep Learning
• Data Science

• Mathematical Biology 
• Dynamical Systems
• Computational Modeling
• Machine Learning 

Traffic flow model, Fluid dynamics.

• Computational Condensed Matter Physics
• Density Functional Theory
• Topological Systems