Research Overview
Investigating the synergy between hardware and software to enable the next generation of intelligent, efficient, and practical computing systems for developing economies.
Current Research Areas
My research focuses on practical solutions that address real-world challenges in resource-constrained environments.
Artificial Intelligence
Developing machine learning and deep learning models for various applications including cybersecurity threat detection, healthcare diagnostics, and predictive analytics.
Cybersecurity
Research on detecting and mitigating cybersecurity threats in IoT networks, mobile payment systems, and cloud computing environments using hybrid ML approaches.
Cyber-Physical Systems
Investigating the security and efficiency of cyber-physical systems, including smart grids, embedded systems, and Industry 4.0 applications.
Software Engineering
Developing robust software solutions for enterprise applications, e-procurement systems, identity management, and workflow automation in institutional settings.
Active Projects
Ongoing research grants and funded projects.
Hybrid Deep Learning for IoT Cybersecurity Threat Detection
Developing CNN-BiLSTM-DNN hybrid models for real-time detection of cybersecurity threats in IoT networks, with applications in smart homes and industrial IoT.
Green AI for Healthcare Diagnostics
Applying energy-efficient AI model selection strategies for computer-aided detection of diseases including Mpox, breast cancer, and metabolic conditions.
Secure Mobile Payment Systems for Financial Inclusion
Analyzing vulnerabilities and developing secure frameworks for mobile payment applications in countries with low financial inclusion.
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Lab & Team
Our research is powered by the Ecology Research Lab. We are a diverse group of doctoral, graduate, and undergraduate students dedicated to pushing the limits of practical computing solutions for real-world challenges.