An interdisciplinary group at the intersection of artificial intelligence, statistics, and cancer research — developing computational tools that support cancer detection, diagnostic refinement, and therapeutic decision-making.
Models, tools, and methods we develop and share across biomedical tables, networks, and single-cell biology.
A topology-first foundation model that describes each node by its structural role, aligns that description with a graph representation, and transfers the resulting knowledge to unseen biological and non-biological networks.
Explore Graph Foundation Model
A differentiable framework that learns where tabular features belong in a two-dimensional map while it learns the prediction task. Retrain Dynomap on a labelled dataset, evaluate pooled out-of-fold performance, and inspect its learned topology and feature attribution.
Explore Dynomap
A semantic-cartography framework that independently lays out network structure and node attributes, then stacks the size-matched channels into a fixed-size representation for vision models. The analysis preserves a path from feature-channel attribution back to the original node attributes.
Explore Graph2Image
A vision foundation model for single-cell biology. It renders each cell as a continuous image via optimal-transport gene cartography and learns one frozen encoder by masked-image pretraining on 72 million human cells — the most accurate zero-shot cell-type annotator on every held-out atlas we tested.
Explore scVisionWe develop deep-learning and statistical methods that turn high-dimensional biomedical measurements — genomics, proteomics, imaging, and clinical data — into interpretable, transferable representations, and translate them toward clinical practice and biological discovery.
Tabular data hides the relationships between features. We reconfigure each sample into a spatially semantic 2D topographic map (TabMap) that keeps feature values as pixel intensities and encodes feature relationships as spatial distance — letting 2D convolutional networks extract association patterns while ranking features by importance.
Nat. Biomed. Eng. 2025Most deep-learning applications are regressions, whose features lie on a complex high-dimensional continuum that resists visualization. Our manifold discovery and analysis (MDA) method learns the manifold topology tied to a network's outputs, preserving local geometry to reveal a model's appropriateness, generalizability, and adversarial robustness.
Nat. Commun. 2023In radiation oncology and medical physics, we integrate diverse data types — medical imaging, clinical records, genomic profiles, and treatment parameters — to uncover complex patterns across modalities that are invisible in any single one, improving diagnosis, prognosis, and therapeutic decision-making.
Related publicationsWe are located in the Stanford Research Park in Palo Alto.
Free visitor parking is available on-site at 3145 Porter Drive.
Get directionsMd Tauhidul Islam, PhD
Principal Investigator · Room A202
tauhid@stanford.edu
We are looking for undergraduate and graduate students and postdocs to join the lab. If your expertise and interests match our projects, please email tauhid@stanford.edu.