Our research goal is to enable computer machines to learn. Our research mainly focuses on developing automated methods for machine learning and data mining to discover and manage knowledge from big data for diverse applications. Currently our main research themes are summarized as follows.

Research Themes

  • Algorithms of Data mining, machine learning and artificial intelligence
    • Big data mining, streaming data mining
  • Computer network security
    • Anomaly detection, intrusion detection, fault diagnosis, network traffic analysis
  • Recommender systems
  • Web mining, social networks
  • Information retrieval​​​

Current Projects

Our aim is to enable computer machines to learn. We are also data "miners". Our work is focusing on developing machine learning algorithms, on discovering and managing knowledge from big data for diverse applications and on design autonomic computing systems. The applications include recommender systems, network management, social networks and computer security.   ​​
The Liquid Execution (LIQUIDX) framework is a foundational step for enabling elastic computation in High Performance Computing clouds. We mainly perform two parallel research: i) LIQUID-CPU: A new resource broker that codifies application elasticity requirements and provides better predictability of resource needs; ii) LIQUID-IO: A new technique that uses data and compute interactions to predict resource requirement of distributed applications​. ​
The project aims to develop a revolutionary approach to animal-borne sensing technology that will be coupled with evolving artificial platforms to observe and monitor the marine environment and its inhabitants in a ground-breaking manner​. In this big project, we contribute to a subtask: focusing on analyzing animal trajectories​.
Extreme Classification of Complex Data with Applications to Multi-media and Recognition of Plastic Debris Floating in Seawater, funded by KAUST Competitive Research Grants Program.