Spatial AI Intelligence

Computational Medicine

Zheng Lab

Spatial AI Intelligence

We develop next-generation multimodal AI for modeling spatially organized biological and physical systems, integrating imaging, omics, and sensing data to advance precision medicine and healthcare.

Explore research

Mission

Our mission is to develop next-generation AI systems that integrate multimodal data across biological and physical scales to uncover the mechanisms driving disease initiation, progression, and therapeutic response, with the overarching goal of improving treatment allocation, reducing healthcare burden, and improving the quality of life.

Mission illustration showing multimodal AI across biological and physical scales
Yuanning Eric Zheng headshot

Principal Investigator

Yuanning (Eric) Zheng, PhD

Dr. Yuanning Zheng is a translational AI scientist whose research focuses on developing intelligent systems to advance precision medicine and biomedical discovery. His work integrates multimodal artificial intelligence, statistical modeling and large-scale biomedical data to uncover patterns, mechanisms, and predictive insights across complex biological and medical systems.

Dr. Zheng received his M.S. in Computer Science from Georgia Institute of Technology and his Ph.D. from Texas A&M University. He completed his postdoctoral training at Stanford University, where he was mentored by Olivier Gevaert and Zinaida Good.

His research has been supported by the NIH/NCI K99/R00 Pathway to Independence Award. Dr. Zheng maintains active collaborations with clinicians, pathologists, engineers, and AI scientists across academia and medicine, with the goal of translating computational innovations into real-world impact in precision medicine and healthcare.

Contact: zheng1361@gmail.com LinkedIn Google Scholar ORCID

Research

Core directions

01

Spatial Intelligence for Biological Systems

Cells are the fundamental units of life. Within tissues, however, cells do not function in isolation; instead, they form complex physical and signaling interactions with neighboring cells and their surrounding microenvironment. Dysregulation of these interaction patterns can drive disease initiation, progression, and resistance to therapy.

We develop statistical and AI methods to model cellular interactions, tissue architecture, and spatial ecosystems in both health and disease. By leveraging single-cell and spatial technologies such as CODEX, CosMx, and Visium, we aim to characterize the functional states of immune cells, with a focus on antigen-presenting cells and T cells, and to understand how these cellular programs influence disease susceptibility, progression, and therapeutic response. Areas of particular interest include cancer, neurodegenerative diseases, and infectious diseases.

Illustration of spatial AI for molecular and biological systems
02

Foundation Models for Medical Imaging

Radiology and histopathology images are widely available diagnostic assays routinely collected from patients. These images contain rich information about tissue morphology, texture, cellular composition, and topological organization. However, this information is frequently underutilized because disease-related patterns are often subtle and complex, making them difficult to quantify consistently through human observation alone.

We develop representation learning strategies to extract, quantify and interpret the features from medical images and integrate them with genetic, transcriptomic, and proteomic information. Our goal is to enable mechanistic discovery, improve diagnosis, and guide treatment allocation.

Illustration of foundation models for multimodal medical imaging
03

Multimodal AI for Precision Biomedicine

Human diseases emerge from complex interactions across molecular, cellular, tissue, organ, and clinical scales. No single data modality can fully capture this complexity. Genomic, transcriptomic, spatial, imaging, clinical, and language data each provide complementary views of disease biology and patient trajectories.

We develop multimodal AI frameworks to integrate diverse biomedical data types, including omics profiles, spatial measurements, medical images, electronic health records, and clinical text. By connecting biological mechanisms with patient-level outcomes, we aim to build predictive and interpretable models that improve disease subtyping, treatment allocation, and therapeutic monitoring.

Illustration of multimodal AI for precision biomedicine

Software

Platforms

NucSegAI

Deep-learning model for robust nucleus segmentation in spatial proteomics and multiplexed tissue imaging workflows.

paper | website

SEQUOIA

AI-powered digital pathology platform that predicts transcriptomic profiles from whole-slide images of cancer tissues at bulk and locoregional levels.

paper | website

GBM360

End-to-end deep-learning framework for reconstructing spatial cellular architecture from glioblastoma histology images and predicting patient prognosis.

paper | website

EpiMix

R-based computational tool for population-scale analysis of epigenomic and transcriptomic data across promoters, enhancers, microRNAs, lncRNAs, and genes.

paper | website

Selected publications

Recent work

First page of Science Advances 2025 publication

Zheng Y, Sadée C, Ozawa M et al. Single-cell multi-modal analysis reveals tumor microenvironment predictive of treatment response in non-small cell lung cancer. Science Advances, 11(21), 2025.

First page of Nature Communications 2024 SEQUOIA publication

Pizurica M*, Zheng Y*, Carrillo-Perez F et al. Digital profiling of cancer transcriptomes from histology images with linearized vision attention. Nature Communications, 15(9886), 2024.

First page of Frontiers in Oncology 2024 publication

Brooks J*, Zheng Y*, Hunter K et al. Digital Spatial Profiling identifies distinct patterns of immuno-oncology-related gene expression within oropharyngeal tumours in relation to HPV and p16 status. Frontiers in Oncology, 14, 2024.

First page of Nature Communications 2023 GBM360 publication

Zheng Y, Carrillo-Perez F, Pizurica M et al. Spatial cellular architecture predicts prognosis in glioblastoma. Nature Communications, 14(4122), 2023.

First page of Cell Reports Methods 2023 EpiMix publication

Zheng Y, Jun J, Brennan K et al. EpiMix is an integrative tool for epigenomic subtyping using DNA methylation. Cell Reports Methods, 3-100515, 2023.

First page of Nature Medicine 2023 publication

Thieme AH, Zheng Y, Machiraju G et al. A deep-learning algorithm to classify skin lesions from mpox virus infection. Nature Medicine, 29, 738-747, 2023.

First page of Nature Biomedical Engineering 2024 publication

Carrillo-Perez F, Pizurica M, Zheng Y et al. Generation of synthetic whole-slide image tiles of tumours from RNA-sequencing data via cascaded diffusion models. Nature Biomedical Engineering, 2024.

First page of PLOS Computational Biology Moonlight publication

Nourbakhsh M, Zheng Y, Noor H et al. Revealing cancer driver genes through integrative transcriptomic and epigenomic analyses with Moonlight. PLOS Computational Biology, 21(4), e1012999, 2025.

First page of PLOS Computational Biology reliability publication

Zhan X, Xu Q, Zheng Y et al. Reliability-based cleaning of noisy training labels with inductive conformal prediction in multi-modal biomedical data mining. PLOS Computational Biology, 21(2), e1012803, 2025.

First page of npj Breast Cancer 2025 publication

Noor H, Zheng Y, Mantz A et al. A 20-feature radiomic signature of triple-negative breast cancer identifies patients at high risk of death. npj Breast Cancer, 11-79, 2025.

First page of Communications Medicine 2023 brain tumors publication

Steyaert S, Qiu YL, Zheng Y et al. Multimodal deep learning to predict prognosis in adult and pediatric brain tumors. Communications Medicine, 3-44, 2023.

First page of Nature Communications accepted foundation models benchmark publication

Bareja R, Carrillo-Perez F, Zheng Y et al. Evaluating Vision and Pathology Foundation Models for Computational Pathology: A Comprehensive Benchmark Study. Nature Communications, accepted.

Team

Lab members

We are recruiting highly motivated PhD students, postdoctoral fellows, and research staff who are passionate about developing scalable spatial intelligence and multi-modal AI to advance precision medicine. Successful candidates will have:

  1. Strong programming and computational skills in languages such as Python, R, or C++;
  2. Experience with modern deep-learning frameworks and AI software packages such as PyTorch, TensorFlow, or related machine-learning libraries;
  3. Preferred experience in imaging, signal, or text processing using tools such as OpenCV and related computational frameworks;
  4. A strong interest in applying AI to real-world biomedical challenges;
  5. A collaborative mindset, intellectual curiosity, and excellent communication skills.

Candidates with backgrounds in artificial intelligence, machine learning, computational biology, computer vision, medical imaging, bioinformatics, data science, electrical engineering, or related quantitative disciplines are highly encouraged to apply.

Interested applicants should send the following materials to the PI's email address:

  1. Curriculum vitae (CV);
  2. A cover letter describing their research experience, technical background, and long-term career goals;
  3. Contact information for three references, including their relationship to the applicant.
Yuanning Eric Zheng headshot

Principal investigator

Yuanning (Eric) Zheng, PhD

Spatial AI, multimodal biomedicine, digital pathology, and translational AI.

zheng1361@gmail.com

PhD student

Recruiting soon

Future trainee in spatial AI for complex biological and physical systems.

Postdoctoral scholar

Recruiting soon

Future researcher in foundation models, image processing, and translational AI.

Software engineer

Recruiting soon

Future builder of robust research software, model infrastructure, and data platforms.

Alumni

Current and previous trainees

  • Jane Feng (Stanford Cancer Immunology Program)
  • Clove Tayler (Stanford Cancer Immunology Program)
  • Alexa Chen (Stanford Institutes of Medicine Summer Research Program)
  • Keshav Narang (Stanford Institutes of Medicine Summer Research Program)
  • Arnoldo Sanchez (Stanford-Foothill STEM Internship Program)
  • Jessie Altamirano (Stanford-Foothill STEM Internship Program)
  • John Jun (Undergraduate research program)
  • Markus Sujansky (Undergraduate research program)

Contact

Collaborations

We welcome collaborations and discussions with clinicians, pathologists, biologists and industrial partners who are interested in applying spatial AI and multimodal data to advance precision medicine and healthcare.

Yuanning (Eric) Zheng
zheng1361@gmail.com
LinkedIn