Cyril Hsu

About Me

I am Cyril Shih-Huan Hsu, a research scientist working at the intersection of evolutionary computation, deep reinforcement learning, generative models, and multi-agent intelligent systems design.

My research focuses on developing and applying state-of-the-art ML algorithms to address complex, real-world problems. I am particularly interested in how adaptation, cooperation, and emergence can guide the design of intelligent, data-driven systems.

My background spans both academia and industry, including research in scalable optimization, applied AI development, and international startup experience arcoss Asia, Europe, and Central America. I am currently a postdoctoral researcher at the University of Amsterdam, where I explore LLM-based agentic AI and prompt engineering for distributed multi-agent systems in next-generation networking.

When not debugging code or trapping myself in difficult math derivations, I try to live by a simple belief:
If you work really hard and you're kind, amazing things will happen. — Conan O’Brien.

Selected Publications

Conference Papers

• Hsu, C. S. H., Li, X., Zanzi, L., Yang, Z., Papagianni, C., & Costa-Pérez, X. (2026). MapViT: A Two-Stage Vision Transformer-Based Framework for Real-Time Radio Quality Map Prediction in Dynamic Environments. 2026 IEEE International Conference on Communications (ICC). [Link]
• Hsu, C. S. H., Papagianni, C., & Grosso, P. (2025). RAILS: Risk-Aware Iterated Local Search for Joint SLA Decomposition and Service Provider Management in Multi-Domain Networks. 2025 IEEE Conference on High Performance Switching and Routing (HPSR). [Link]
• Dalgkitsis, A., Hsu, C. S. H., Papagianni, C., Grosso, P., & de Laat, C. (2025). LLM-based Optimization Algorithm Selection for High-Performance Networks Orchestration. Proceedings of the SC '25 Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC Workshops). [Link]
• Hsu, C. S. H., De Vleeschauwer, D., Papagianni, C., & Grosso, P. (2025). Online SLA Decomposition: Enabling Real-Time Adaptation to Evolving Network Systems. 2025 Joint European Conference on Networks and Communications & 6G Summit (EuCNC). [Link]
• Hsu, C. S. H., Martín-Pérez, J., Papagianni, C., & Grosso, P. (2023). V2N Service Scaling with Deep Reinforcement Learning. 2023 IEEE Conference on Network Operations and Management Symposium (NOMS). [Link]
• Sun, M. C., Hsu, C. S. H., Yang, M. C., & Chien, J. H. (2018). Context-Aware Cascade Attention-Based RNN for Video Emotion Recognition. 2018 First Asian Conference on Affective Computing and Intelligent Interaction (ACII Asia). [Link]
• Chang, W. Y., Hsu, C. S. H., & Chien, J. H. (2017). FATAUVA-Net: An Integrated Deep Learning Framework for Facial Attribute Recognition, Action Unit Detection, and Valence-Arousal Estimation. 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). [Link]
• Hsu, C. S. H., & Yu, T. L. (2015). Optimization by Pairwise Linkage Detection, Incremental Linkage Set, and Restricted/Back Mixing: DSMGA-II. Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation (GECCO). [Link]

Journal Papers

• Hsu, C. S. H., Dalgkitsis, A., Papagianni, C., & Grosso, P. (2025). Transformer-Empowered Actor-Critic Reinforcement Learning for Sequence-Aware Service Function Chain Partitioning. IEEE Transactions on Network Science and Engineering (TNSE), under revision, 2025. [Link]
• Hsu, C. S. H., Martín-Pérez, J., De Vleeschauwer, D., Valcarenghi, L., Li, X., & Papagianni, C. (2025). A Deep RL Approach on Task Placement and Scaling of Edge Resources for Cellular Vehicle-to-Network Service Provisioning. IEEE Transactions on Network and Service Management (TNSM), 2025. [Link]
• Hsu, C. S. H., De Vleeschauwer, D., & Papagianni, C. (2023). SLA Decomposition for Network Slicing: A Deep Neural Network Approach. IEEE Networking Letters (NL), 2023. [Link]

Contact

s.h.hsu[AT]uva.nl · Google Scholar · LinkedIn