Cen Wan, PhD
Assistant Professor
cen.wan{at}bbk.ac.uk
Birkbeck, University of London
Malet St,
London, 
WC1E 7HX

Recent News

  • Joined the editorial board of Nature Scientific Reports.

  • Joined the program committee of ECAI 2025.

  • Joined the program committee of IJCNN 2025 as an area chair.

  • Joined the program committee of IEEE CIHM 2025.

  • Joined the program committee of ECAI 2024.

  • Our new paper about functional DNA sequence design was deposited in bioRxiv.

  • Our new paper about contrastive learning and protein-protein interaction networks was published by NAR Genomics and Bioinformatics.

  • Our new paper about the random promoter DREAM challenge was published by Nature Biotechnology.

  • Our new paper about contrastive learning and scRNA-Seq was published by Briefings in Functional Genomics.

  • Our new paper about hierarchical feature selection was accepted by ACM SAC 2024.

Academic Experience

I am an assistant professor in computer science at the Department of Computer Science and Information Systems, Birkbeck, University of London. 
I was a post-doctoral researcher in machine learning and bioinformatics at the Bioinformatics Group (Professor David T. Jones' Lab), Department of Computer Science, University College London (UCL), and jointly affiliated with the Biomedical Data Science Laboratory at the Francis Crick Institute. 
Before joining UCL, I was pursuing the PhD in computer science at the University of Kent (supervised by Professor Alex A. Freitas), where I continued my research interests on developing novel machine learning and data mining classification algorithms. I started research on machine learning (
supervised by Dr Irina V. Biktasheva) at the University of Liverpool.

I was with the ProCovar project being sponsored by the ERC Advanced Grant, after finishing the BBSRC flagship Strategic LoLa project - DDIP (Drosophila Developmental Interactome Project), to characterise the developmental proteome of Drosophila melanogaster.
Research Interests

My research interests lie in the algorithmic novelty of machine learning and data mining methods, particularly in large language models, contrastive learning and hierarchy/structure-constrained algorithms. I am also interested in the research of machine learning applications, particularly in bioinformatics and computational biology, e.g. single cell RNA-seq data analysis, biological sequence analysis and gene/protein function prediction.