Jiayu Su
--- Navigating the multidimensional universe of systems biology ---
I am a final-year PhD student in Systems Biology at Columbia University, advised by Raul Rabadan and David Knowles. My research focuses on developing statistical and computational methods to understand the complexities of human diseases from large-scale sequencing data. I am particularly interested in RNA biology and the applications of single-cell and spatial omics technologies. My most recent work (SPLISOSM, 2026) has uncovered a hidden layer of tissue regulation through spatially variable RNA processing in the brain and glioblastoma. Additionally, I also work on kernel-based statistical machine learning and subspace learning problems.
Previously, I received my Bachelor’s degrees in Biology and Mathematics from Peking University, where I worked with Cheng Li on methods for single-cell data to study aging. I have also interned at the University of Chicago and Harvard Medical School and briefly worked as a bioinformatics engineer at a precision medicine startup during the pandemic.
Some other research directions I am excited about include:
The interaction between cancer cells and surrounding tissue affects immune response and treatment. Spatial multi-omics across samples and time may help unravel this complex ecosystem.
Splicing dysregulation generates oncogenic isoforms and targetable neoantigens. Identifying these can open new therapeutic possibilities.
Aging involves molecular changes that can lead to cancer. Studying the link between RNA biology, aging, and cancer offers insights into prevention and treatment.
News
| Feb 04, 2026 | New work on the consistent and scalable detection of spatial patterns where we show that all spatial variability testing methods share a same quadratic form and further identify when and why they may fail. (tl;dr: no single winner, but some are worse!) We further scale up the test to process millions of spatial locations in a second. Check out the implementation. |
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| Oct 23, 2025 | The SPLISOSM paper (A computational framework for mapping isoform landscape and regulatory mechanisms from spatial transcriptomics data) was accepted (in principle) at Nature Biotechnology! |
| Nov 01, 2024 | The sisPCA paper (Disentangling Interpretable Factors with Supervised Independent Subspace Principal Component Analysis) was accepted at NeurIPS 2024! |
Selected publications
† co-corresponding authors; * equal contribution;
- On the consistent and scalable detection of spatial patterns2026Detecting spatial patterns is fundamental to scientific discovery, yet current methods lack statistical consensus and face computational barriers when applied to large-scale spatial omics datasets. We unify major approaches through a single quadratic form and derive general consistency conditions. We reveal that several widely used methods, including Moran’s I, are inconsistent, and propose scalable corrections. The resulting test enables robust pattern detection across millions of spatial locations and single-cell lineage-tracing datasets.
- Mapping isoforms and regulatory mechanisms from spatial transcriptomics data with splisosmNature Biotechnology, 2026
- Disentangling interpretable factors with supervised independent subspace principal component analysisIn Advances in Neural Information Processing Systems (NeurIPS), 2024
- Smoother: a unified and modular framework for incorporating structural dependency in spatial omics dataGenome Biology, 2023
- A transcriptome-based single-cell biological age model and resource for tissue-specific aging measuresGenome Research, 2023
- Single-cell transcriptome profiling reveals neutrophil heterogeneity in homeostasis and infectionNature Immunology, 2020