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Yongbo Wang (王勇博)

Master & Bachelor at Computer Science, Chongqing University (CQU)

Now working at Red Hat Inc., Beijing, China

Email: yongbowin@outlook.com
Phone: +86 136 3799 5210
LinkedIn: here
Homepage: https://yongbowin.github.io

Biography

I am now an associate quality engineer in Red Hat, Beijing, since October 2018. Before this, I was a visiting student in the AI&NLP team in SIAT, Chinese Academy of Sciences, under the supervision of Prof. Min Yang since December 2017. Previously, I got my master's degree in CS from CQU, under the supervision of Prof. Yunni Xia in June 2018, and double bachelor degree in CS and Business Administration in 2015, respectively.

My Resume/CV (Download)

Research Interests

• Natural Language Processing
• Deep Learning

News!

We won the second place in the finals in the Intelligent Investment Adviser Game of the Big Data and Artificial Intelligence Innovation and Entrepreneurship Contest of 2017. This is one of the competitions about machine learning and NLP in China.

Education

• Master | The Department of Computer Science and Technology, Chongqing University , 2015 - 2018
  
• Bachelor | The Department of Computer Science and Technology, Chongqing University , 2011 - 2015
  
• Bachelor (Double degree) | The School of economics and Business Administration, Chongqing University , 2012 - 2015
  

Recent Highlights

NAIRS: A Neural Attentive Interpretable Recommendation System
Shuai Yu, Yongbo Wang, Min Yang, Baocheng Li, Qiang Qu, Jialie Shen. In ACM International Conference on Web Search and Data Mining (WSDM) (demo paper), 2018.
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NAIRS: A Neural Attentive Interpretable Recommendation System

Growing In this paper, we develop a neural attentive interpretable recommendation system, named NAIRS. A self-attention network, as a key component of the system, is designed to assign attention weights to interacted items of a user. This attention mechanism can distinguish the importance of the various interacted items in contributing to a user profile. Based on the user profiles obtained by the self-attention network, NAIRS offers personalized high-quality recommendation. Moreover, it develops visual cues to interpret recommendations. This demo application with the implementation of NAIRS enables users to interact with a recommendation system, and it persistently collects training data to improve the system. The demonstration and experimental results show the effectiveness of NAIRS.

An energy-aware and under-SLA-constraints VM consolidation strategy based on the optimal matching method
Weiling Li, Yongbo Wang, Yuandou Wang, Yunni Xia, Kunyin Guo, Quanwang Wu, Qingsheng Zhu. In International Journal of Web Services Research Volume 14, Issue 4 (IJWSR), 2017.
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An energy-aware and under-SLA-constraints VM consolidation strategy based on the optimal matching method

Growing demand of computational power brings increasing scale and complexity of cloud datacenters. However, such increase also generates growing energy consumption and related cost incurred for cooling and maintenance. With concerns of cost and energy saving by both industry and academy, the reduction of energy consumption of cloud datacenters becomes a hotspot issue. Recently, virtual-machine-consolidationbased strategies are proposed as promising methods for reduction of cloud energy consumption. Virtual machine (VM) consolidation effectively increases the resource utilization rate. However, it remains a great challenge how to reduce energy consumption while maintaining the quality of service (QoS) at a satisfactory level. In this work, a comprehensive framework is presented for the above-mentioned problem, which aims at maximizing the number of physical machines (PMs) to be turned off within a consolidation period following the constraints of QoS, in terms of Service-Level-Agreement (SLA) violation rate. In comparison with most existing related works which consider invariant utilization rate of PMs in computing energy reduction of candidate migration plans, propose framework considers time-varying utilization rate and employs the number of PMs to be turned off within a consolidation period (NPTCP for simple) as the optimization objective. The proposed framework consists of a resource selection algorithm taking the predicted migration overhead (derived by the Pareto distribution) as inputs and another algorithm generating optimal matching plans based on preference scores of candidate VMs. For the model validation purpose, a case study is conducted on the CloudSim simulation platform and it shows that the proposed method achieves better energy reduction and less SLA violation.

Stochastic performance analysis of unreliable IaaS clouds
YongBo Wang, HaiChuan Zhang, KunYin Guo, Yunni Xia*, WeiLing Li. In Proceedings of S2 International Conference on Internet of Things (ICIOT), 2016.
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Stochastic performance analysis of unreliable IaaS clouds

Performance evaluation of cloud data-centers has drawn considerable attention from academy and industry. In this study, we present an analytical approach to the performance analysis of Infrastructure-as-a-Service cloud data-centers with unreliable task executions and resubmissions of unsuccessful tasks. Several performance metrics are considered and analyzed under variable load intensities, failure frequencies, multiplexing abilities, and service intensities. We also conduct a case study based on a real-world cloud data-center and employ a confidence interval check to validate the correctness of the proposed model.

Experience

• Associate Quality Engineer | Red Hat
  Topic: Real Time Kernel
  Oct 2018 - present
• Research Intern | BMI, Chinese Academy of Sciences
  Topic: Natural Language Processing
  Dec 2017 - Sept 2018

Awards

• 2nd place, The Intelligent Investment Adviser Game of the Big Data and Artificial Intelligence Innovation and Entrepreneurship Contest of 2017.
• 2nd prize, Chongqing Division, China Undergraduate Mathematical Contest in Modeling (2013).
• National Endeavor Scholarship (top 5%) (2012).
• 2nd prize, Scholarship, Chongqing University (top 3%) (2012).

Skills

Python, Java, Shell, Linux, Docker, Hadoop, HBase, Sqoop, Pig, Elasticsearch, Tensorflow, Keras, MySQL,MongoDB, SVN, Git, Django, Web frameworks and skills (Django, SpringMVC, Struts2, HTML, Javascript, Ajax).

Others

Click here to see my Self-evaluation.

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