Research Summary

My research focuses on routing, resource allocation, and energy-efficient design of networked systems, with an emphasis on wireless ad hoc networks and underwater acoustic sensor networks. I develop optimization-based models and increasingly combine them with reinforcement learning to address scalability and uncertainty.

More recently, I have been exploring hybrid classical–quantum network routing, investigating how physical constraints such as entanglement lifetime and network reliability influence routing and control decisions.

Key Research Topics

Operations Research and Mathematical Optimization for Networked Systems

Development of linear, integer, and mixed-integer programming (MILP) models, including network flow–based formulations, for complex optimization problems in communication networks. Primary focus areas include network lifetime maximization, energy efficiency, resource allocation, energy–delay–reliability trade-offs, and resilience under physical, topological, and security constraints.

Wireless Ad Hoc and Underwater Sensor Networks

Design and optimization of energy-efficient routing, topology control, and communication strategies for terrestrial wireless ad hoc networks and underwater acoustic sensor networks. Research topics span network lifetime and energy-efficiency analysis, k-connectivity–based reliability, multi-sink architectures, void regions, adversarial effects, and operation under harsh and resource-constrained environments.

Hybrid Optimization and Learning-Based Network Control

Integration of mathematical optimization frameworks with machine learning and reinforcement learning techniques to enable adaptive, data-driven, and scalable control of complex networked systems. Recent work explores hybrid optimization–learning methodologies for parameter prediction, dynamic decision-making, and emerging paradigms such as quantum network routing and hybrid classical–quantum architectures.

Tutorials & Research Code

This repository provides a hands-on Python tutorial for network-flow–based optimization models in wireless sensor networks, intended for students and researchers.