Hailiang ZHAO @ ZJU.CS.CCNT

  


  Ph.D. Student, CCNT Lab
  AdvanCed Computing aNd SysTem Laboratory
  College of Computer Science and Technology
  Zhejiang University, Hangzhou 310027, China

  Supervisor: Prof. Shuiguang Deng
  Email: hliangzhao97 {AT} gmail {DOT} com
  Laboratory: Cao Guangbiao Sci-tech Building, Yuquan Campus of Zhejiang University

Biography

Currently I am a second-year Ph.D. student of College of Computer Science and Technology, Zhejiang University. Before my Ph.D. career, I was an undergraduate student from Wuhan University of Technology and received my B.Eng. degree in Computer Science and Technology on June, 2019. In September 2019, I was admitted to study for a Ph.D. degree in Zhejiang University under the supervision of Prof. Shuiguang Deng without entrance examination. [CV] [简历]

Github Profile: https://github.com/hliangzhao
Research Gate: http://dwz.win/uJY
Google Scholar: http://t.cn/AiWSETna

Research Interests

I am interested in Cloud & Edge Computing, Distributed Systems, and Network Management. Currently I am focusing on:

  • Dependent Task Scheduling & Online Resource Allocation
    Cluster schedulers are key to realizing improvements in resource utilization for the cloud-native apps. Current cluster schedulers rely on heuristics that prioritize generality, ease of understanding, and straightforward implementation over achieving the ideal performance on a specific workload. However, they ignore readily available information about job structure (i.e., internal dependencies) and efficient parallelism for jobs’ input sizes. How to design a better online resource allocation algorithm (mechanism) is of importance.
  • AI-driven Optimization
    In our Edge Intelligence paper, we divide Edge Intelligence into AI for edge and AI on edge. The former focuses on providing a more optimal solution to the key concerns in Edge Computing with the help of popular and resultful AI technologies while the latter studies how to carry out the entire process of AI models, i.e., model training and inference, on edge. What I put emphasis on is that how to apply AI-based models, especially reinforcement learning and graph neural networks, to improve the QoE.

Publications

Documents & Notes

  • 最优化理论基础
    这篇文档给出了最优化理论的数学基础,包含范数、梯度、海瑟矩阵、Lipschitz连续、闭函数、凸集、仿射集、超平面、凸函数的定义与保凸运算、共轭函数等内容,Jul 2021。
  • 最优化简介
    这篇文档给出了最优化理论的基本知识,包含优化问题的分类、解的形式、收敛速度等,Jun 2021。
  • 深度学习理论速查清单
    这篇文档给出了深度学习模型的理论原理,Oct 2020。
  • 理解共轭梯度法
    这篇文档给出了共轭梯度法的设计原理、算法实现以及收敛性分析,Jun 2020。
  • 需要熟练掌握的算法理论基础 (ver. 21.06.22)
    这篇文档从“为什么要这样”的角度罗列并分析了概率论、数理统计、微积分、优化理论以及线性代数中一些广泛应用的话题,May 2020。
  • MEC业务系统架构
    这篇笔记总结了多接入边缘计算的业务架构,May 2020。
  • 比特币与区块链总结 [lightChain]
    这篇笔记总结了我在理解比特币和区块链概念时遇到的一些问题,Nov 2019。
  • 里亚普洛夫优化导论
    这篇文档给出了里亚普洛夫优化技术的使用方法及性能分析,Nov 2018。
  • 线性代数中的一些基本观念
    这篇文档对线性代数中的一些基本观念给出了有趣的阐释,Oct 2018。

Discussions & Slides

Awards & Honors & Contests

  • The Outstanding Postgraduates Award of CCNT Lab and College of Computer Science and Technology, Zhejiang University, Oct 2020.
  • The Doctoral Freshman Scholarship of Zhejiang University, Sep 2019.
  • The Best Student Paper Award of 2019 IEEE International Conference on Web Services, Jul 2019.
  • The Outstanding Graduates Award of Wuhan University of Technology, Apr 2019.
  • The Excellence Scholarship of Wuhan University of Technology (only 20 students per year), Nov 2018.

Professional Services

  • Reviewer for: IEEE Transactions on Services Computing, Transactions on Emerging Telecommunications Technologies.
  • Sub-reviewer for: IEEE Internet of Things Journal, ACM Transactions on Internet Technology, IEEE Transactions on Cloud Computing, ICWS 2020, ICSOC 2020, SCC 2021.

Experiences

  • R&D Intern of Cloud BU, Huawei Technologies Co., Ltd, Nov 2020 ~ Jan 2021.

Correspondence

Email: hliangzhao97 {AT} gmail {DOT} com

Laboratory Address: Cao Guangbiao Sci-tech Building, Yuquan Campus of Zhejiang University.
中国浙江省杭州市西湖区浙大路38号, 浙江大学玉泉校区 310027.