Cen Wan, PhD, FHEA
Tenured Associate Professor
(in Computer Science)
cen.wan{at}bbk.ac.uk
Birkbeck, University of London
Malet St,
London, 
WC1E 7HX

Wan Lab: AI for Biology

Deciphering complex biological systems using AI


Recent News (2024-2026)
    ▷ Recognised by ScholarGPS as a world-leading scholar.

    Version

    Bioinformatics Area
    2025
    top 0.9%, ranked 1,427th among 160,982 scholars worldwide (lifetime, all citations)
    2024
    top 1.5%, ranked 2,268th among 152,591 scholars worldwide (lifetime)

    Version

    Knowledge Extraction Area
    2025
    top 1.7%, ranked 898th among 51,579 scholars worldwide (lifetime, all citations)
    2024
    top 0.5%, ranked 72nd among 16,224 scholars worldwide (prior five years)
    2024
    ranked 1st in the United Kingdom (prior five years)

    Version

    Longevity Area
    2025
    top 2.3%, ranked 1,600th among 68,366 scholars worldwide (lifetime, all citations)
    ▷ Academic Services and Activities

  • Joined the program committee of IEEE WCCI 2026 (IEEE IJCNN 2026) as an area chair.

  • Joined the program committee of IEEE ICASSP 2026.

  • Joined the editorial board of BMC Artificial Intelligence.

  • Joined the editorial board of Nature Scientific Reports.

  • Joined the program committee of ECAI 2025.

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

  • Joined the program committee of IEEE CIHM 2025.

  • Joined the program committee of ECAI 2024.

  • 🥉 Our team won the bronze prize in the recent Placental Clock DREAM Challenge.

  • 🎓 Ibrahim Alsaggaf successfully passed his PhD viva. Congratulations!

  • 🥇 Ibrahim Alsaggaf was awarded the 2025 Faculty of Science PhD prize. Congratulations!

    • ▷ New Publications

  • The source code and the trained models of Gen-DNA-TCN have been released.

  • A new version of our functional synthetic yeast promoter sequence design paper is deposited in bioRxiv.

  • Our new paper about GAN-enhanced scRNA-Seq augmentation-free contrastive learning is deposited in bioRxiv.

  • Our new paper about scRNA-Seq contrastive learning generative networks is deposited in bioRxiv.

  • Our new paper about scRNA-Seq contrastive learning is published by Bioinformatics.

  • Our new paper about the DREAM Placenta Clock Challenge is published by iScience.

  • The GsRCL cell-type identification server is released on PSIPRED.

  • One abstract is accepted by ISMB/ECCB 2025 - NetBio track as a talk.

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

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

  • Our new paper about the Random Promoter DREAM Challenge is published by Nature Biotechnology.

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

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

Academic Experience

I am an associate professor in computer science at the School of Computing and Mathematical Sciences, 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.