Research
Areas of Research Interest
- AI-Driven Autonomous Networked Systems
My research investigates the fundamental principles underlying self-optimizing and self-healing communication networks. By integrating reinforcement learning, distributed intelligence, and adaptive control theory, this work advances autonomous operation in low-power wide-area networks (LPWANs), 5G, and next-generation wireless systems. The goal is to enable scalable, resilient, and energy-aware network architectures capable of operating under dynamic and uncertain environments.
- Intelligent Cyber-Physical and Health Systems
This research develops interpretable AI/ML frameworks for data-driven decision support in complex health science systems and cyber-physical infrastructures. Emphasis is placed on predictive modeling, uncertainty quantification, and trustworthy AI to improve system reliability, performance, and societal outcomes in connected health and smart infrastructure applications.
- Sustainable and Energy-Efficient Communication Architectures
This work advances green communication systems through the co-design of energy-efficient IoT hardware, adaptive protocols, and network optimization algorithms. Research spans smart buildings, industrial cyber-physical systems, and environmental monitoring networks, with a focus on minimizing energy consumption while preserving performance and reliability at scale.
- Optimal Computing and Distributed Resource Management
My research develops scalable load-balancing, scheduling, and resource allocation algorithms for heterogeneous computing environments, including cloud-edge systems and emerging quantum-enabled networks. By combining optimization theory, distributed systems design, and AI-based control, this work seeks to improve computational efficiency, latency performance, and system robustness across next-generation computing infrastructures.
