I am a post-doctoral researcher in the Department of Computing at Imperial College London, working in the Large-Scale Data & Systems (LSDS) group with Prof. Peter Pietzuch. I conduct research of efficient data-intensive systems at the intersection of scalable reinforcement learning systems and distributed data management systems, as well as compilation-based optimisation techniques.
My long-term goal is to explore and understand the fundamental connections between data management and modern machine learning systems to make decision-making transparent, robust and efficient.
- Huanzhou Zhu*, Bo Zhao*, Gang Chen, Weifeng Chen, Yijie Chen, Liang Shi, Yaodong Yang, Peter Pietzuch, Lei Chen (*equal contribution)
MSRL: Distributed Reinforcement Learning with Dataflow Fragments, Preprint, ArXiv, 2022
- Gururaghav Raman, Bo Zhao, Jimmy Chih-Hsien Peng, Matthias Weidlich
Adaptive incentive-based demand response with distributed non-compliance assessment, Applied Energy (IF=11.446), Volume 326, 2022
- Bo Zhao
State Management for Efficient Event Pattern Detection
Dissertation, Humboldt-Universität zu Berlin, 2022.
- Bo Zhao, Han van der Aa, Nguyen Thanh Tam, Nguyen Quoc Viet Hung, Matthias Weidlich
EIRES: Efficient Integration of Remote Data in Event Stream Processing
In Proc. of the 47th ACM SIGMOD International Conference on Management of Data (SIGMOD), Xi'an, China, ACM, June 2021.
- Bo Zhao, Nguyen Quoc Viet Hung, Matthias Weidlich
Load Shedding for Complex Event Processing: Input-based and State-based Techniques
In Proc. of the 36th IEEE International Conference on Data Engineering (ICDE), Dallas, TX, USA, IEEE, April 2020.
- Gururaghav Raman, Jimmy Chih-Hsien Peng, Bo Zhao, Matthias Weidlich
Dynamic Decision Making for Demand Response through Adaptive Event Stream Monitoring
In Proc. of 2019 IEEE Power & Energy Society General Meeting (PESGM), Atlanta, GA, USA. IEEE, August 2019.
- Bo Zhao
Complex Event Processing under Constrained Resources by State-based Load Shedding
In Proc. of the 34th IEEE International Conference on Data Engineering (ICDE), Paris, France, IEEE, April 2018.
- Song Liu, Bo Zhao, Qing Jiang, Weiguo Wu
A Semi-Automatic Coarse-Grained Parallelization Approach for Loop Optimization And Irregular Code Sections
In Chinese Journal of Computers, 2017, 40(9): 2127-2147.
- Bo Zhao, Zhen Li, Ali Jannesari, Felix Wolf, Weiguo Wu
Dependence-Based Code Transformation for Coarse-Grained Parallelism
In Proc. of the International Workshop on Code Optimisation for Multi and Many Cores (COSMIC) held in conjunction with CGO, San Francisco, CA, USA, ACM, February 2015.
- Zhen Li, Bo Zhao, Ali Jannesari, Felix Wolf
Beyond Data Parallelism: Identifying Parallel Tasks in Sequential Programs
In Proc. of 15th International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP), Zhangjiajie, China, Lecture Notes in Computer Science, Springer, November 2015.
- Song Liu, Weiguo Wu, Bo Zhao, Qing Jiang
Loop Tiling for Optimization of Locality and Parallelism
In Journal of Computer Research and Development, 2015, 52(5): 1160-1176.
Availability Committee: SIGMOD 2022
Demonstration Track Committee: ICDE 2023
et al., 2022, Adaptive incentive-based demand response with distributed non-compliance assessment, Applied Energy, Vol:326, ISSN:0306-2619
et al., 2021, EIRES: Efficient Integration of Remote Data in Event Stream Processing, ACM SIGMOD International Conference on Management of Data (SIGMOD), ASSOC COMPUTING MACHINERY, Pages:2128-2141, ISSN:0730-8078
Zhao B, Nguyen QVH, Weidlich M, 2020, Load Shedding for Complex Event Processing: Input-based and State-based Techniques, IEEE 36th International Conference on Data Engineering (ICDE), IEEE COMPUTER SOC, Pages:1093-1104, ISSN:1084-4627
et al., 2019, Dynamic Decision Making for Demand Response through Adaptive Event Stream Monitoring, ISSN:1944-9925
Zhao B, 2018, Complex Event Processing under Constrained Resources by State-Based Load Shedding, 34th IEEE International Conference on Data Engineering Workshops (ICDEW), IEEE, Pages:1699-1703, ISSN:1084-4627