Research

My research focuses on routing, resource allocation, and energy-efficient design of networked systems, with an emphasis on wireless ad hoc networks, underwater acoustic sensor networks, and drone-assisted aerial 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.

Swipe horizontally to explore the full map.

Solid boxes: established lines of work, evidenced by the publication record. Dashed outlines: ongoing integration and exploratory directions. Select an area to see the related publications; each title links to the paper. The relations view shows how shared topics connect the research areas — selecting a node jumps to its publications in the overview.

Key research topics

Operations research and mathematical optimization for networked systems

Core

Methods: linear, integer, and mixed-integer programming (MILP) · network-flow formulations · goal programming

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

Representative Packet size optimization for smart grid WSNs (TIE 2017) · Goal programming for broadcasting backbones (Ad Hoc Netw. 2023)

Wireless ad hoc and underwater acoustic sensor networks

Core

Methods: MILP lifetime models · k-connectivity analysis · transmission-power and packet-size optimization

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-region mitigation, adversarial effects, and operation under harsh and resource-constrained environments.

Representative Fountain codes for UWSN lifetime (TII 2019) · Non-uniform k-connectivity (IoT-J 2025) · Void regions & sink architecture (IEEE Sensors J. 2023)

Resilient drone-assisted aerial networks

Core

Methods: integer programming · heuristic optimization · mobility-aware topology adaptation

Design and restoration of resilient k-connected drone networks using integer programming and heuristic optimization techniques. This research investigates mobility-aware connectivity restoration, minimum-movement strategies, and topology adaptation in grid-based aerial deployments. Recent work develops exact optimization models and scalable heuristics to balance resilience, execution time, and mobility cost in drone-assisted communication systems.

Representative k-connectivity restoration strategies (Comput. Stand. Interfaces 2025)

Hybrid optimization and learning-based network control

Ongoing

Methods: optimization–learning integration · neural parameter prediction · reinforcement learning

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

Representative Neural network–based instant parameter prediction (Wireless Netw. 2019)

Software

VERA — Visual Evaluation, Reporting & Analytics

A modern platform for academic juries, capstone evaluations, and accreditation workflows. Jurors enter through a QR code, score projects against a configurable rubric, and administrators access real-time rankings and accreditation-ready reports the moment scoring closes.

vera-eval.app GitHub repository

KAIROS — Course timetabling

A conflict-free university course timetabling system built on OR-Tools CP-SAT. Given raw course and room data, KAIROS produces a weekly schedule with no double-booked rooms, no instructor conflicts, and every placement verified by an independent validator — then polishes it with soft optimization for cohort idle gaps, instructor compactness, and department fairness. Available as a web app and a command-line solver.

kairos.huguryildiz.com GitHub repository

Underwater Acoustic Ray Bench

A benchmark pitting five LLMs against a genuine 3D BELLHOP3D reference solver on underwater acoustic ray tracing: each model traces a fan of rays through a synthetic ocean with a depth-dependent sound-speed profile and seamounts. Scoring compares each model's exported transmission-loss field against the reference solver on field accuracy, coverage, and geometry fidelity.

uwa-ray-bench.vercel.app GitHub repository

IEEE / ACM Paper Writing Skills

An evidence-aware manuscript workflow for drafting, rewriting, humanizing, and auditing engineering papers while preserving claims, citations, notation, scope, and uncertainty.

ieee-acm-paper-writing.vercel.app GitHub repository

Code & learning resources

wsn-opt-python — a hands-on Python tutorial for network-flow–based optimization models in wireless sensor networks, intended for students and researchers. GitHub
Underwater networks — basics — an introductory document covering core principles of underwater acoustic communication and sensor networks. PDF (opens in a new tab)