Ongoing Researches: Department of Mathematics and Data Science

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Dr. Mohammed Shadman Salam

Particle Physics, String Theory and Phenomenology
Machine Learning Applications to Theoretical Physics

Dr. Afsana Al Sharmin

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

M H M Imrul Kabir

Statistical Modeling
Time Series Analysis
Stochastic Processes
Analysis of Longitudinal Data
Bayesian Analysis
Public Health

Shamima Hossain

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.