Ongoing Researches

• Wireless Communication

• Machine Learning

• Biomedical Engineering

• Microbiology and Clinical survey

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

• 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.

• Cybersecurity
• Artificial Intelligence
• Cloud Computing
• Blockchain
• Internet of Things (IoT)

MEMS, BioMEMS, Nanofabrication, Thin-film Solar cell, MEMS Resonator, Epitaxy, Heterostructure, Sensors and Actuators, Underwater energy harvesting, Brain-computer interface, Neural Sensing.

• Pharmacology and Toxicological studies
• Phytopharmacology
• Anticancer drug evaluation
• Computational Biology

My research focus lies at the intersection of advanced photonics, VLSI design, and biosensing technologies. Specific areas include:

Photonics & Bio-sensing: Design of Photonic Crystal Fibers (SPR-PCF) for medical diagnostics (e.g., prostate cancer detection).
VLSI & Circuit Design: Linearity analysis of CSR-VCO for RF applications and low-power approximate adders for DSP.
Nanotechnology: Impact of asymmetric salt concentration on nanopore properties and particle translocation.
IoT & Automation: Development of smart monitoring systems and industrial robotics.

 Microbiology, Cancer Biology, Machine Learning

Toxicology, Drug Metabolism, Pharmacokinetics, Neuropharmacology, Natural Product-Based Drug Discovery 

Future Internet Architecture, Distributed System and Data Communication, Internet Protocols, Embedded Systems

• Pharmacological & Toxicological Studies
• Clinical Trial
• Public Health related Survey

Biostatistics

Clinical Trial

Data Sciences

Artificial Intelligence 

 Machine Learning
 Deep Learning
Image processing

Medical AI Systems

Deep Biometric Recognition, Generation, and Analysis

Explainable AI

Image Processing