Research Interests

  • Multiomics data integration:
    • Bulk DNA-seq and single cell RNA-seq for cancer clonal analysis
    • CITE-seq
  • Gene regulation/gene regulatory network
    • microRNA
    • Perturb-seq
  • Phylogenetics
  • Probabilistic machine learning
  • Bayesian computation

Refereed publications

  1. H. Koptagel, S-H. Jun, and J. Lagergren. SCuPhr: A Probabilistic Framework for Cell Lineage Tree Reconstruction. PLoS Computational Biology 20, e1012094 (2024).
  2. S-H. Jun, H. Nasif, C. Jennings-Shaffer, D.H. Rich, A. Kooperberg, M. Fourment, C. Zhang, M.A. Suchard, and F.A. Matsen. A topology-marginal composite likelihood via a generalized phylogenetic pruning algorithm. Algorithms Molecular Biology 18, 10 (2023).
  3. D. A. Oyong, F. J. Duffy, M. L. Neal, Y. Du, J. Carnes, K. V. Schwedhelm, N. Hertoghs, S-H. Jun, H. Miller, J. D. Aitchison, S. C. De Rosa, E. W. Newell, M. J. McElrath, S. M. McDermott, and K. D. Stuart. Distinct immune responses associated with vaccination status and protection outcomes after malaria challenge. PLOS pathogens. In press.
  4. S-H. Jun, H. Toosi, J. Mold, C. Engblom, X. Chen, C. O’Flanagan, M. Hagemann-Jensen, R. Sandberg, S. Aparicio, J. Hartman, A. Roth, J. Lagergren, Reconstructing clonal tree for phylo-phenotypic characterization of cancer using single-cell transcriptomics. Nature Communications. 14, 982 (2023).
  5. X. Chen, E.G. Sifakis, S. Robertson, S.Y. Neo, S-H. Jun, J. Lövrot, V. Jovic, J. Bergh, T. Foukakis, J. Lagergren, A. Lundqvist, R. Ma, and J. Hartman. Breast cancer patient-derived whole-tumor cell culture model for efficient drug profiling and treatment response prediction. Proceedings of the National Academy of Sciences of the United States of America. 120, e2209856120 (2023).
  6. M. Mohaghegh Neyshabouri, S-H. Jun, and J. Lagergren. Inferring tumor progression in large datasets. PLOS Computational Biology. 16(10), p.e1008183 (2020).
  7. S-H. Jun, S. Wong, J. Zidek, and A. Bouchard-Côté. Sequential decision model for inference and prediction on non-uniform hypergraphs with application to knot matching from computational forestry. The Annals of Applied Statistics. 13(3), pp. 1678-1707 (2019).
  8. E. Haber, L. Ruthotto, E. Holtham, S-H. Jun. Learning across scales - A multiscale method for convolution neural networks. Association for the Advancement of Artificial Intelligence (AAAI). (2018).
  9. S-H. Jun, A. Bouchard-Côté, S. Wong, and J. Zidek. Sequential graph matching with sequential Monte Carlo. International Conference on Artificial Intelligence and Statistics (AISTATS). pp. 1075–1084 (2017).
  10. S-H. Jun and A. Bouchard-Côté. Memory (and time) efficient sequential Monte Carlo. International Conference on Machine Learning (ICML). pp. 514-–522 (2014).
  11. S-H. Jun, L. Wang, and A. Bouchard-Côté. Entangled Monte Carlo. Advances in Neural Information Processing Systems 25 (NIPS). pp. 2735–2743 (2012).