Faculty Members Ongoing Researches
- 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.
- Cybersecurity
- Artificial Intelligence
- Cloud Computing
- Blockchain
- Internet of Things (IoT)
- 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:
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.
Photonics & Bio-sensing: Design of Photonic Crystal Fibers (SPR-PCF) for medical diagnostics (e.g., prostate cancer detection).
Microbiology, Cancer Biology, Machine Learning
Future Internet Architecture, Distributed System and Data Communication, Internet Protocols, Embedded Systems
His research topic includes the followings.
- Wireless networks
- Internet of Things (IoT)
- Machine Learning
- Renewable energy
- Pharmacological & Toxicological Studies
- Clinical Trial
- Public Health related Survey
Biostatistics
Clinical Trial
Data Sciences
Artificial Intelligence
Machine Learning
Medical AI Systems
Deep Biometric Recognition, Generation, and Analysis
Explainable AI
Image Processing
- Pharmacogenomics
- Pharmacogenetics
- In-vitro phytochemical screening
- In-vivo and in-vitro quality evaluation and comparison
- Public health
- Environmental Statistics
- Agricultural Statistics
- Applied Statistics
- Machine Learning
Dr. Huq's research interests lie primarily in mobile crowdsourcing, intelligent transportation and delivery systems, artificial intelligence, machine learning, human-centered AI, and data-driven decision support. Her research has explored intelligent worker selection, task allocation, service optimization, predictive modeling, and real-time decision-making in complex urban environments. She has also been involved in research on AI-assisted emergency medical service management, with a focus on predictive emergency demand, ambulance deployment, and human-centered decision support.
