Dr. Zhang is a Computational Biologist focusing on developing and using statistical machine learning methods, cutting-edge single-cell multi-omics, and systems immunology approaches to study inflammatory disease pathogenesis. Dr. Zhang joined the faculty at CU Anschutz in January, 2022, and started her Computational and Systems Immunology Lab https://fanzhanglab.org/ in the Department of Medicine, Division of Rheumatology, and also the newly built Center for Health AI. Dr. Zhang also has close connections with the Interdisciplinary Joint Biology Program (IJBP). Dr. Zhang and her lab aims to bridge the gap between multidisciplines including computational and statistical methodologies, inflammatory diseases, and translational medicine. 

Dr. Zhang is also an Investigator involved in the NIH funded Accelerating Medicines Partnership Rheumatoid Arthritis and Systemic Lupus Erythematosus (AMP RA/SLE) Consortium. She did her Postdoc research with Dr. Soumya Raychaudhuri on using immunogenomics and single-cell multi-omics to study autoimmune diseases (e,g, rheumatoid arthritis) at Harvard Medical School, Brigham and Women’s Hospital, and Broad Institute of Harvard/MIT (2017 - 2021). Through collaborative research with rheumatologists and immunologists in the AMP consortium, she has been leading the computational analysis on the single-cell RNA-seq, single-cell proteomics, single-cell CITE-seq, and other high-dimensional omics integration on data from synovial tissues from patients with rheumatoid arthritis and osteoarthritis. These clinically phenotyped RA single-cell atlas generate from the AMP RA/SLE can be used to classify samples from other inflammatory diseased patient samples (e.g. lupus, IBD, etc), to identify shared pathways across clinical disorders, and to identify novel drug targets.

Before that, Fan completed her PhD (2013-2017) from Worcester Polytechnic Institute, Massachusetts. She developed novel statistical machine learning models in the fields of statistical modeling using variational inference and mixed membership modeling with Dr. Patrick Flaherty to characterize clinical patient heterogeneity using large-scale genomic data. Dr. Zhang is dedicated to the field of exploring novel and state-of-the-art computational tools and pipelines to identify potential drug targets to improve disease diagnosis and treatment.

Dr. Zhang has generated multiple deep collaborations with immunologists, rheumatologists, and clinicians in the past ten years. She and her lab believe in collaborations, encourage sharing, promote diversity, and maintain scientific integrity to pursue high-quality science for translational medicine. She is actively recruiting graduate students, postdoc fellows, computational biologists, and bioinformatic analysts to join her interdisciplinary and collaborative team.


  1. The Zhang lab integrates cross-disease tissue-blood single-cell datasets to identify shared/unique phenotypes which provide insights into disease etiology and drug repurposing. Tissue inflammation is a unifying feature across disparate diseases. We demonstrate the power of computational integrative strategies and generates a multi-disease single-cell reference to interpret cellular phenotypes and query new cells from clinically disorders, including rheumatoid arthritis (RA), lupus, IBD, RA-ILD, type I diabetes, psoriasis and other inflammatory diseases. We are particularly interested in myeloid heterogeneity and their interactions with other cells in inflammatory environments.

  2. The Zhang lab deciphers pathogenic cells and molecular mechanisms for autoimmune or inflammatory diseases using single-cell multi-omic integration. Rheumatoid arthritis is a typical autoimmune disease with chronic inflammation in the joint and other organs of the patients. Defining key cellular subsets and their activation states in the inflamed tissue is a critical step in defining new therapeutic targets for RA. We use single-cell RNA-seq, single-cell CTIE-seq, single-cell ATAC-seq, spatial transcriptomics and other single-cell multi-omics data integration strategies to decipher pathogenic features and RA heterogeneity. We have long-term interests in defining connections between tissue inflammation and blood markers to link tissue-level heterogeneity to clinical subphenotypes.

  3. The Zhang lab develops statistical and computational methods to study patient heterogeneity for translational research.To infer clinical sample heterogeneity, we develop mixed membership matrix factorization methods using variational inference and optimization, and deep canonical correlation analysis-based methods to perform accurate and scalable multimodal data integration. We are dedicated to the field of high-dimensional multi-omics data modeling and analysis using statistical machine learning methods for both primary and publicly available datasets.