Minghao Ye is a fifth-year Ph.D. Candidate at the Department of Electrical and Computer Engineering of New York University (NYU), working with Professor H. Jonathan Chao at the NYU High-Speed Networking Lab. His research projects mainly focus on designing intelligent traffic engineering solutions for wide-area networks with machine learning. Before joining the Ph.D. program, he received an M.S. degree in electrical engineering from NYU in 2019 and obtained dual B.Eng. degrees from Sun Yat-sen University and The Hong Kong Polytechnic University in 2017.
Minghao Ye, Junjie Zhang, Zehua Guo, and H. Jonathan Chao, “Roracle: Enabling Lookahead Routing for Scalable Traffic Engineering with Supervised Learning,“ accepted. The 31st IEEE International Conference on Network Protocols (ICNP), 2023.
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.
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,” IEEE/ACM 31st International Symposium on Quality of Service (IWQoS), 2023.
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), 2023.
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.
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), 2022.
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.
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.
Minghao Ye, Junjie Zhang, Zehua Guo, and H. Jonathan Chao, “DATE: Disturbance-Aware Traffic Engineering with Reinforcement Learning in Software-Defined Networks,” IEEE/ACM 29th International Symposium on Quality of Service (IWQoS), 2021.
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.
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.
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.
Instructor
ECE-GY 6363 Data Center and Cloud Computing (Spring 2023)
Course Assistant
ECE-UY 3613 Communication Networks (Spring 2020)
ECE-GY 6363 Data Center and Cloud Computing (Fall 2021 - Fall 2022)
ECE-GY 6383 High-Speed Networks (Spring 2019 - Spring 2021, Fall 2023)
Research Centers, Labs, and Groups
High Speed Networking Lab
Our research is concentrated on developing complete solutions for data center networks, ...