Academic Profile : Faculty

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Assoc Prof Kwoh Chee Keong
Associate Professor, School of Computer Science and Engineering, School of Computer Science and Engineering
Associate Professor, School of Computer Science and Engineering
External Links
 
Dr. Kwoh Chee Keong is currently in the School of Computer Engineering since 1993. He received his Bachelor degree in Electrical Engineering (1st Class) and Master in Industrial System Engineering from the National University of Singapore in 1987 and 1991 respectively. He received his Ph.D. degrees from the Imperial College, University of London in 1995. His research interests include Data Mining and Soft Computing and Graph-Based inference; applications areas include Bioinformatics and Biomedical Engineering. He has done significant research work his research areas and published over 90 quality international conferences and over 30 journal papers. He has been often invited as a organizing member or referee and reviewer for a number of premier conferences and journals, including GIW, IEEE BIBM, RECOMB, PRIB etc. Dr. Kwoh is a member of The Institution of Engineers Singapore, Association for Medical and Bio-Informatics, Imperial College Alumni Association of Singapore (ICAAS). He has provided many service to professional bodies, and the Singapore and was conferred the Public Service Medal, the President of Singapore in 2008.
Development of a Computer Prediction System For Rational Design Of HLA-Based Peptide Vaccine;
Data Mining and Analysis on Infectious Disease
Heterogeneous Multi-Core Systems For Bioinformatics
Constrained Optimzation for Bioinformatics
Protein Interaction Network Analysis Using Graph Mining Approaches
 
  • Challenge-Learn: Developing and Assessing an Andragogical Programme and System based on Co-Skilling to Enhance Employability and Learning
  • Hodge Laplacian based deep learning models for drug design
  • Host-pathogen protein-protein interaction approaches for predicting virulence
  • Investigating the regulation of 3D genome organization using machine learning
  • Predict the solubility of proteins using machine learning
  • Structural analysis and characterization of protein complexes
  • The discovery of neutralizing antibodies for potential novel coronavirus through machine learning approaches
  • Untangling cancer re-wiring: Pan-Cancer mapping of transcription factor driven dysregulatory hotspots using AlphaFold2 and integrative machine learning