We develop new intelligent learning methods and complex network models for biomedical knowledge discovery.


Research Interests

  •     Evolutionary Computing
  •     Bioinformatics
  •     Computational Biology
  •     Artificial Evolution
  •     Machine Learning
  •     Complex Networks


Funding Support

  • Discovery grant from National Sciences and Engineering Research Council (NSERC) of Canada
  • Ignite R&D grant from Research & Development Corporation (RDC) of Newfoundland and Labrador 



  • 2018-01: Our paper on "An evolutionary learning and network approach to identifying key metabolites for osteoarthritis" was accepted for publication in PLOS Computational Biology
  • 2018-01: Our paper on “Analyzing feature importance for metabolomics using genetic programming” was accepted for publication in EuroGP 2018
  • 2018-01: Faramarz Dorani’s paper on “Feature selection for detecting gene-gene interactions in genome-wide association studies” was accepted for publication in EvoApplications 2018
  • 2017-09: Welcome four new members to our lab, Asma(PhD), Zhendong (PhD), Arshad (MSc), and Yu(MSc)!
  • 2017-04: Karoliina Oksanen's paper on "Lexicase selection promotes effective search and behavioural diversity of solutions in linear genetic programming" was accepted for publication in IEEE CEC 2017
  • 2016-11: Ting Hu was selected for the Best Professor Award of the Department of Computer Science by the Computer Science Graduate Society (CSGS)


We are recruiting!

    We look forward to working with motivated undergraduate, graduate students, and postdocs who are enthusiastic about developing novel learning algorithms and network models to solve complex problems.