Hao Wang
Assistant Professor
Charles V. Schaefer, Jr. School of Engineering and Science
Department of Electrical and Computer Engineering
Education
- PhD (2020) University of Toronto (Computer Engineering)
- MA (2015) Shanghai Jiao Tong University (Software Engineering )
- BA (2012) Shanghai Jiao Tong University (Information Security Engineering )
Research
Distributed Computing, Distributed Machine Learning, Large-scale Data Analytics, Data Center Networking
General Information
I received my Ph.D. degree from the ECE Department, UofT 2020 July. I finished both my Bachelor’s and Master’s degrees at Shanghai Jiao Tong University in 2012 and 2015, respectively. I received the NSF CRII Award in 2022.
Honors and Awards
2024, Distinguished Member of IEEE INFOCOM 2024 TPC recognition
2022, Excellent Reviewer for the IEEE Transactions on Network Science & Engineering
2022, NSF CRII Award
2022, Excellent Reviewer for the IEEE Transactions on Network Science & Engineering
2022, NSF CRII Award
Grants, Contracts and Funds
Towards the Resilient NextG Network Design for Federated Learning over Mobile Devices
Co-PI Hao Wang, Funded by CISE/MSI/RDP/CNS (2025–2026)
Enhancing Energy Awareness for Efficient Federated Learning over Mobile AI Systems
Lead PI Hao Wang, Funded by CSR/Core (2024–2028)
Advancing Model Forensics with Systematic Parsing, Injection Detection, and Model Provenance Attribution
Co-PI Hao Wang, Funded by SaTC/Core (2024–2026)
Harvesting Idle Resources Safely and Timely for Large-scale AI Applications in High-Performance Computing Systems
Lead PI Hao Wang, Funded by OAC/Core (2024–2027)
Critical Learning Periods Augmented Robust Federated Learning
Lead PI Hao Wang, Funded by SaTC/Core (2023–2025)
High-Efficiency Serverless Computing Systems for Deep Learning: A Hybrid CPU/GPU Architecture
Hao Wang, Funded by OAC/CRII (2022–2025)
Co-PI Hao Wang, Funded by CISE/MSI/RDP/CNS (2025–2026)
Enhancing Energy Awareness for Efficient Federated Learning over Mobile AI Systems
Lead PI Hao Wang, Funded by CSR/Core (2024–2028)
Advancing Model Forensics with Systematic Parsing, Injection Detection, and Model Provenance Attribution
Co-PI Hao Wang, Funded by SaTC/Core (2024–2026)
Harvesting Idle Resources Safely and Timely for Large-scale AI Applications in High-Performance Computing Systems
Lead PI Hao Wang, Funded by OAC/Core (2024–2027)
Critical Learning Periods Augmented Robust Federated Learning
Lead PI Hao Wang, Funded by SaTC/Core (2023–2025)
High-Efficiency Serverless Computing Systems for Deep Learning: A Hybrid CPU/GPU Architecture
Hao Wang, Funded by OAC/CRII (2022–2025)
Selected Publications
Stellaris: Staleness-Aware Distributed Reinforcement Learning with Serverless Computing. Hanfei Yu, Hao Wang, Devesh Tiwari, Jian Li, and Seung-Jong ParkAccepted by the International Conference for High Performance Computing, Networking, Storage, and Analysis (SC) – Best Student Paper Finalist 2024
RainbowCake: Mitigating Cold-starts in Serverless with Layer-wise Container Caching and Sharing. Hanfei Yu, Rohan Basu Roy, Christian Fontenot, Devesh Tiwari, Jian Li, Hong Zhang, Hao Wang, and Seung-Jong ParkIn Proceedings of the ACM Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) 2024
Backdoor Federated Learning by Poisoning Backdoor-Critical Layers. Haomin Zhuang, Mingxian Yu, Hao Wang, Yang Hua, Jian Li, and Xu YuanAccepted by the International Conference on Learning Representations (ICLR) 2024
CriticalFL: A Critical Learning Periods Augmented Client Selection Framework for Efficient Federated Learning. Gang Yan, Hao Wang, Xu Yuan, and Jian LiIn Proceedings of the ACM Special Interest Group on Knowledge Discovery and Data Mining (KDD) 2023
RainbowCake: Mitigating Cold-starts in Serverless with Layer-wise Container Caching and Sharing. Hanfei Yu, Rohan Basu Roy, Christian Fontenot, Devesh Tiwari, Jian Li, Hong Zhang, Hao Wang, and Seung-Jong ParkIn Proceedings of the ACM Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) 2024
Backdoor Federated Learning by Poisoning Backdoor-Critical Layers. Haomin Zhuang, Mingxian Yu, Hao Wang, Yang Hua, Jian Li, and Xu YuanAccepted by the International Conference on Learning Representations (ICLR) 2024
CriticalFL: A Critical Learning Periods Augmented Client Selection Framework for Efficient Federated Learning. Gang Yan, Hao Wang, Xu Yuan, and Jian LiIn Proceedings of the ACM Special Interest Group on Knowledge Discovery and Data Mining (KDD) 2023