Intelligent design of optical networks: which topology features help maximise throughput in the nonlinear regime? - Polina Bayvel; Ruijie Luo; Robin Matzner; Daniel Semrau; Georgios Zervas (ECOC, 2020)

Making intelligent topology design choices: understanding structural and physical property performance implications in optical networks [Invited]. - Robin Matzner; Daniel Semrau; Ruijie Luo; Georgios Zervas; Polina Bayvel (JOCN, 2021)

Message Passing: Towards Low-Complexity, Global Optimal Routing and Wavelength Assignment Solutions for Optical Networks. - Ruijie Luo; Yi-Zhi Xu; Robin Matzner; Georgios Zervas; David Saad; Polina Bayvel (OFC, 2022)

Towards a Traffic-Optimal Large-Scale Optical Network Topology Design. - Ruijie Luo; Robin Matzner; Georgios Zervas; Polina Bayvel (ONDM, 2022)

Ultra-fast Optical Network Throughput Prediction using Graph Neural Networks. - Robin Matzner; Ruijie Luo; George Zervas; Polina Bayvel (ONDM, 2022)

Exploring the relationship among traffic, topology, and throughput: towards a traffic-optimal optical network topology design. - Ruijie Luo; Robin Matzner; Alessandro Ottino; Georgios Zervas; Polina Bayvel (JOCN, 2023)

Topology Bench: Systematic Graph Based Benchmarking for Core Optical Networks. - Robin Matzner; Akanksha Ahuja; Rasoul Sadeghi; Michael Doherty; Alejandra Beghelli; Seb J. Savory; Polina Bayvel (CoRR, 2024)

Reinforcement learning for dynamic resource allocation in optical networks: hype or hope? - Michael Doherty; Robin Matzner; Rasoul Sadeghi; Polina Bayvel; Alejandra Beghelli (J. Opt. Commun. Netw., 2025)

Traffic-aware and physically constrained optical network growth techniques. - Rasoul Sadeghi; Robin Matzner; Yi-Zhi Xu; Alejandra Beghelli; David Saad; Polina Bayvel (J. Opt. Commun. Netw., 2025)