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URochester Medicine / Department of Biostatistics and Computational Biology / People & Research / Faculty / Seong-Hwan Jun, PhD

Seong-Hwan Jun, PhD

Professor Seong-Hwan JunAssistant Professor of Biostatistics and Computational Biology
 Ph.D. (2018) University of British Columbia

Contact Information

University of Rochester
Dept of Biostatistics and Computational Biology
265 Crittenden Boulevard, CU 420630 
Rochester, New York 14642-0630
 
Office: Saunders Research Building 4168
Phone: (585) 276-3079
E-mail: seonghwan_jun@urmc.rochester.edu 

Research Interests

  • Bayesian models and computation
  • Probabilistic machine learning
  • Graphical models
  • Statistical inference for combinatorial problems
  • Statistical methods for analysis of single cell transcriptomics data
  • Multi-omics data integration
  • Cancer genomics
  • Cancer-immune interaction
  • Phylogenetics

Selected Publications

  • S-H. Jun, M. K. Halushka, M. N. McCall. NBSR: a Negative Binomial Softmax Regression model for microRNA-seq data analysis. Biostatistics 27,

    kxag012 (2026).

  • Y. Zheng, D. P. Caron, J.Y. Kim, S-H. Jun, Y. Tian, F. Mair, K. D. Stuart, P. A. Sims, R. Gottardo. ADTnorm: Robust integration of single-cell protein

    measurement across CITE-seq datasets. Nat. Commun. 16, 5852 (2025)

  • A. M. Baran, A. H. Patil, E. Aparicio-Puerta, S-H. Jun, M. K. Halushka, M. N. McCall. miRglmm: a generalized linear mixed model of isomiR-level

    counts improves estimation of miRNA-level differential expression and uncovers variable differential expression between isomiRs. Genome Biol. 26, 102 (2025).

  • Jun, S-H., Wong, S.W., Zidk, J.V., and Bouchard-Cote, A., 2019. 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.
    variable differential expression between isomiRs. Genome Biol. 26, 102 (2025).

  • H. Koptagel, S-H. Jun, J. H˚ard, J. Lagergren. Scuphr: A probabilistic framework for cell lineage tree reconstruction. PLoS Comput.Biol. 20, e1012094 (2024).

  • S-H. Jun, H. Nasif, C. Jennings-Shaffer, D. H. Rich, A. Kooperberg, M. Fourment, C. Zhang, M. A. Suchard, F. A. Matsen IV. A topology-marginal composite likelihood via a generalized phylogenetic pruning algorithm. Algorithms Mol. Biol. 18, 10 (2023).

  • 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 Pathog. 19, e1011051 (2023).

  • 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).

  • X. Chen, E.G. Sifakis, S. Robertson, S.Y. Neo, S-H. Jun, J. L¨ovrot, 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).

  • M. M. Neyshabouri, S-H. Jun, and J. Lagergren. Inferring tumor progression in large datasets. PLoS Comput. Biol. 16(10), p.e1008183 (2020).

  • S-H. Jun, S.Wong, J. Zidek, and A. Bouchard-Cˆot´e. 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).

  • 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). Acceptance rate: 933/3800.

  • S-H. Jun, A. Bouchard-Cˆot´e, S. Wong, and J. Zidek. Sequential graph matching with sequential Monte Carlo. International Conference on Artificial Intelligence and Statistics (AISTATS). pp. 1075–1084 (2017). Acceptance rate: 168/530.

  • S-H. Jun and A. Bouchard-Cˆot´e. Memory (and time) efficient sequential Monte Carlo. International Conference on Machine Learning (ICML). pp. 514–522 (2014).

    Acceptance rate: 310/1238.

  • S-H. Jun, L.Wang, and A. Bouchard-Cˆot´e. Entangled Monte Carlo. Advances in Neural Information Processing Systems 25 (NIPS). pp. 2735–2743 (2012). Acceptance rate: 370/1467. Spotlight talk: 72/1467.

Last Updated: October 22, 2026