Dinh-Mao Bui

Компьютерлік ғылымдар, Ғылымдар және технология мектебі, National Laboratory Astana, Computer Science Laboratory
Ассистент профессор
Электр. пошта

I have finished the Ph.D. in Computer Engineering from the Ubiquitous Computing Lab, Department of Computer Science and Engineering, Kyung Hee University, Korea. My current research interests are machine learning (supervised learning, especially the prediction technique) and optimization for the distributed system, big data, and networking. Some of my works, which include the adaptive replication management in HDFS, the multiuser detection in DS-CDMA, and the energy efficiency in cloud computing, are based on my improved Gaussian process prediction method. In the past, my previous research works are related to modeling and optimizing the operation of Infrastructure-as-a-Services (IaaS) cloud computing in the datacenter.

Research Areas:

  • Machine learning (Supervised Learning, Neural Network).
  • Convex optimization.
  • Stochastic processes.
  • Performance enhancement for distributed system.
  • Energy efficiency.

Selected Publications:

  • Dinh-Mao Bui, Shujaat Hussain, Eui-Nam Huh, and Sungyoung Lee. “Adaptive replication management in HDFS based on supervised learning.” IEEE Transactions on Knowledge and Data Engineering (SCI, IF: 3.438), 2016.
  • Dinh-Mao Bui and Sungyoung Lee. “Fast Gaussian Process Regression for Multiuser Detection in DS-CDMA.” IEEE Communications Letters (SCI, IF: 1.988), 2017.
  • Dinh-Mao Bui, YongIk Yoon, Eui-Nam Huh, SungIk Jun, and Sungyoung Lee. “Energy efficiency for cloud computing system based on predictive optimization.” Journal of Parallel and Distributed Computing (SCI, IF:1.93), 2017.
  • Dinh-Mao Bui, Thien Huynh-The and Sungyoung Lee. “Early fault detection in IaaS cloud computing based on fuzzy logic and prediction technique.” The Journal of Supercomputing (SCI, IF: 1.326), 2017.
  • Dinh-Mao Bui, Huu-Quoc Nguyen, YongIk Yoon, SungIk Jun, Muhammad Bilal Amin, and Sungyoung Lee. “Gaussian process for predicting CPU utilization and its application to energy efficiency.” Applied Intelligence (SCI, IF: 1.215), 2015.