Research Output
My research journey is centered on developing intelligent systems while contributing to the academic community through manuscripts, conference submissions, technical writing, and ongoing research.
Adaptive Evacuation: A GNN-Enhanced Deep Reinforcement Learning Framework
Developing a hybrid Graph Neural Network and Deep Reinforcement Learning framework for adaptive indoor emergency response. This approach bridges theoretical RL with real-time execution in IoT-enabled environments.
A Novel Adaptive Caching Framework for Dynamic Web Portals
Explored adaptive caching strategies using machine learning to improve dynamic web portal performance. Demonstrated significant latency reduction by predicting temporal data access patterns.
ProcureAI: An AI-Assisted Procurement Platform with Multi-Criteria Vendor Intelligence and Agentic Workflow Automation
A human-in-the-loop AI procurement platform combining deterministic multi-criteria vendor evaluation, LLM-assisted recommendation and explanation, tool-augmented interaction, and approval-controlled agentic workflow automation.
Future Research Directions in Trustworthy AI
Currently studying theoretical bounds of Reinforcement Learning and Graph Neural Networks while preparing future research directions in trustworthy AI.
"I view research as a continuous learning process where every project, experiment, and manuscript contributes toward building intelligent systems with practical and societal impact."