Building smarter, safer
digital ecosystems
My research blends machine learning, quantum computing, and AI driven cybersecurity focused on scalable, explainable, and ethical AI models that solve real world challenges.
What I research
Each area connects to real-world impact from protecting financial transactions to advancing healthcare diagnostics.
AI-Driven Cybersecurity
Phishing detection, smishing, quishing threat models using QML and attention-based architectures.
Quantum Machine Learning
Hybrid quantum-classical models for image classification, pneumonia detection, and post-quantum cryptography.
Fraud Detection
Novel risk-scoring frameworks using ML for transaction card fraud mitigation and behavioral analysis.
Medical Imaging & Agriculture
Quantum-assisted feature extraction for disease detection, including CNN models for plant disease classification.
Published & accepted papers
All peer-reviewed at top-tier IEEE and Springer conferences.
Ongoing research projects
Decentralized quishing threat detection
Using multi-head self-attention models for decentralized, AI-driven cybersecurity. Developing a framework to detect QR-code based phishing attacks at scale.
Federated learning for remote education
Building privacy-preserving learning systems for remote education platforms, enabling collaborative model training without exposing student data.
Let's collaborate on research
I'm actively seeking PhD opportunities and open to co-authoring papers, peer reviews, and interdisciplinary projects in ML, quantum computing, and cybersecurity.