Publications

I have published 18 papers (10 first-authored, 400+ citations) in top-tier journals/conferences with a book chapter. 

Please refer to my Google Scholar profile for an indexed publication list.

1. [ToN 25] Minghao Ye, Junjie Zhang, Zehua Guo, and H. Jonathan Chao, “Learning-based Adaptive Range Routing for Traffic Engineering with Graph Neural Networks,” accepted. IEEE Transactions on Networking (ToN), 2025. (Impact factor: 3.6)

2. [JSAC 25] Minghao Ye, Junjie Zhang, Zehua Guo, and H. Jonathan Chao, “Path-Based Graph Neural Network for Robust and Resilient Routing in Distributed Traffic Engineering,” IEEE Journal on Selected Areas in Communications (JSAC), 2025. (Impact factor: 17.2) [Paper URL] [PDF] 

3. [ToN 25] Haowen Zhu, Zehua Guo, and Minghao Ye, “DINA: Toward Determined In-Network Aggregation for Distributed Machine Learning,” IEEE Transactions on Networking (ToN), 2025. (Impact factor: 3.6) [Paper URL] [PDF] 

4. [ToN 24] Yuntian Zhang, Ning Han, Tengteng Zhu, Junjie Zhang, Minghao Ye, Songshi Dou, and Zehua Guo, “Prophet: Traffic Engineering-centric Traffic Matrix Prediction,” IEEE/ACM Transactions on Networking (ToN), 2024. (Impact factor: 3.6) [Paper URL] [PDF] 

5. [ToN 23] Minghao Ye, Junjie Zhang, Zehua Guo, and H. Jonathan Chao, “FlexDATE: Flexible and Disturbance-Aware Traffic Engineering with Reinforcement Learning in Software-Defined Networks,” IEEE/ACM Transactions on Networking (ToN), 2023. (Impact factor: 3.6) [Paper URL] [PDF] 

6. [JSAC 22] Minghao Ye, Yang Hu, Junjie Zhang, Zehua Guo, and H. Jonathan Chao, “Mitigating Routing Update Overhead for Traffic Engineering by Combining Destination-based Routing with Reinforcement Learning,” IEEE Journal on Selected Areas in Communications (JSAC), 2022. (Impact factor: 17.2) [Paper URL] [Codes] [PDF] 

7. [JSAC 20] Junjie Zhang, Minghao Ye, Zehua Guo, Chen-Yu Yen, and H. Jonathan Chao, “CFR-RL: Traffic Engineering with Reinforcement Learning in SDN,” IEEE Journal on Selected Areas in Communications (JSAC), 2020. (200+ citations on Google Scholar. Impact factor: 17.2) [Paper URL] [arXiv] [Codes] [PDF] 

8. [TNSM 19] Huazhong Liu, Laurence T. Yang, Jinjun Chen, Minghao Ye, Jihong Ding, and Liwei Kuang, “Multivariate Multi-order Markov Multi-modal Prediction with Its Application in Network Traffic Management,” IEEE Transactions on Network and Service Management (TNSM), 2019. (Impact factor: 5.4) [Paper URL] [PDF] 

1. [HPSR ’25] Minghao Ye, Boyu Han, Xing Fang, Xiaocheng Zou, Xingda Bao, Xiao Xie, Senlin Xiao, Yihao Lin, and H. Jonathan Chao, “Dynamic Path Switching for Traffic Engineering in SD-WAN with eBPF,” The 26th IEEE International Conference on High Performance Switching and Routing (HPSR), 2025. [Paper URL] [PDF]

2. [IWQoS ’24] Haowen Zhu, Minghao Ye, and Zehua Guo, “Toward Determined Service for Distributed Machine Learning,” The 32nd IEEE/ACM International Symposium on Quality of Service (IWQoS), 2024. (Acceptance rate: 24.8%, 81/326) [Paper URL] [PDF]

3. [ICNP ’23] Minghao Ye, Junjie Zhang, Zehua Guo, and H. Jonathan Chao, “Roracle: Enabling Lookahead Routing for Scalable Traffic Engineering with Supervised Learning,” The 31st IEEE International Conference on Network Protocols (ICNP), 2023. (Acceptance rate: 18.8%, 34/181) [Paper URL] [PDF] 

4. [INFOCOM ’23] Minghao Ye, Junjie Zhang, Zehua Guo, and H. Jonathan Chao, “LARRI: Learning-based Adaptive Range Routing for Highly Dynamic Traffic in WANs,” IEEE International Conference on Computer Communications (INFOCOM), 2023. (Selected as one of the five fast-tracked papers for submission to IEEE/ACM Transactions on Networking. Acceptance rate: 19.2%, 252/1312) [Paper URL] [PDF] 

5. [IWQoS ’23] Minghao Ye, Yang Hu (co-first author), Junjie Zhang, Zehua Guo, and H. Jonathan Chao, “Reinforcement Learning-based Traffic Engineering for QoS Provisioning and Load Balancing,” The 31st IEEE/ACM International Symposium on Quality of Service (IWQoS), 2023. (Acceptance rate: 23.5%, 62/264) [Paper URL] [PDF] 

6. [MMSys ’22] Ke Chen, Han Wang, Shuwen Fang, Xiaotian Li, Minghao Ye, and H. Jonathan Chao, “RL-AFEC: Adaptive Forward Error Correction for Real-time Video Communication Based on Reinforcement Learning,” The 13th ACM Multimedia Systems Conference (MMSys), 2022. (Acceptance rate: 34%, 16/46) [Paper URL] [Codes] [PDF] 

7. [ICNP ’21] Minghao Ye, Junjie Zhang, Zehua Guo, and H. Jonathan Chao, “Federated Traffic Engineering with Supervised Learning in Multi-region Networks,” The 29th IEEE International Conference on Network Protocols (ICNP), 2021. (One of the 11 pre-accepted papers without conditional acceptance. Acceptance rate: 24.7%, 38/154) [Paper URL] [Video] [Teaser] [PDF] 

8. [IWQoS ’21] Minghao Ye, Junjie Zhang, Zehua Guo, and H. Jonathan Chao, “DATE: Disturbance-Aware Traffic Engineering with Reinforcement Learning in Software-Defined Networks,” The 29th IEEE/ACM International Symposium on Quality of Service (IWQoS), 2021. (Acceptance rate: 25%, 64/256) [Paper URL] [Video] [PDF] 

1. [APNet ’25] Chong Wu, Xiaoyang Fu, Minghao Ye, and Zehua Guo, “Critical Flow Range Routing for Wide Area Networks,” The 9th Asia-Pacific Workshop on Networking (APNet) Poster Session, 2025. [Paper URL] 

2. [NetAI ’20] Junjie Zhang, Zehua Guo, Minghao Ye, and H. Jonathan Chao, “SmartEntry: Mitigating Routing Update Overhead with Reinforcement Learning for Traffic Engineering,” ACM SIGCOMM Workshop on Network Meets AI & ML (NetAI), 2020. [Paper URL] [Slides] [Video] [PDF] 

1. Zehua Guo, Minghao Ye, and Jiaxin Tan, “Machine Learning for Sensory Data Analytics,” Empowering IoT with Big Data Analytics, Elsevier, November 2024. [Chapter URL] [Book URL]