About

Yu-Ling Chang is an Associate Professor at Berkeley Social Welfare whose scholarly interests focus on the relationships among poverty, inequality, and social safety net programs. Her research addresses both the process of policymaking and the consequences of public policies for economically disadvantaged populations. Her professional experiences serving and advocating for individuals suffering from economic hardship during the global economic recession in the late 2000s inform her research agenda. Currently, her research centers on racial equity and the Temporary Assistance for Needy Families (TANF) program at the state and federal levels in the US. She also studies unconditional basic income (UBI) programs that support marginalized populations, including unhoused individuals, migrant youth, and single mothers in both the US and Taiwan. Her work has been supported by various regional and national research institutions, and she is expanding her research scope from cross-state comparative studies in the US to cross-national comparative research in a global context. Dr. Chang has established international collaborations with institutions such as the University of Hong Kong, National Taiwan University, and the Bern University of Applied Sciences in Switzerland. She earned her Bachelor and Master of Social Work from National Taiwan University and her PhD in social welfare from the University of Washington, with a concentration in public policy and management and a certificate in social science statistics.

Research topics

• Computer Science
• Biology
• Genetics
• Computational biology
• Medicine
• Bioinformatics
• Pathology
• Data science
• World Wide Web
• Internal medicine

Selected publications

• Tumor-immune trajectory context connects static tissue architecture to clinical outcomes

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-03-30 · 1 citations

  articleOpen accessSenior authorCorresponding

  Abstract Multiplexed tissue imaging (MTI) has revealed recurrent tumor microenvironment (TME) architectures with prognostic value, yet these measurements are inherently static, obscuring dynamic changes in the TME that govern therapeutic response. Here, we introduce a trajectory-centric framework that reconstructs continuous TME dynamics by integrating agent-based mathematical modeling and simulation with state space analysis. This approach yields a mechanistically constrained reference landscape built entirely from in silico simulation, and onto which static patient biospecimens can be projected and mapped onto simulated TME trajectories. Systematic simulation of tumor-immune interactions in triple-negative breast cancer identifies six metastable TME states connected by transition pathways spanning immune control to immune escape. Mapping MTI data from two independent patient cohorts, including longitudinal samples from a randomized immunotherapy trial, validates this landscape by positioning individual biospecimens along inferred TME trajectories rather than in static states. We show that treatment-phase TME states, but not pre-treatment configurations, robustly predict immunotherapy response, and identical terminal states can arise from distinct trajectory histories corresponding to immune failure or resolved inflammation. Thus, this framework enables mechanistic simulations to define a reference dynamical landscape that serves as a coordinate system for interpreting static clinical spatial data, providing a principled basis for evaluating consistency, predictiveness, and clinical relevance across independent patient cohorts. Altogether, this study advances spatial tumor profiling from static state classification of human tissues to dynamic trajectory inference, establishing a quantitative framework for trajectory-informed, state-guided, and temporally adaptive immunotherapy strategies.

  PublisherDOI

• Cell fusion reprograms tumor cells and promotes RUNX1-mediated invasion and dissemination in colorectal cancer

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-04-15

  articleOpen access

  Summary Metastasis remains the primary cause of cancer-related morbidity and mortality, despite significant advances in targeted therapies. Although metastatic dissemination requires tumor cells to escape the primary lesion and colonize distant organs, the mechanisms by which primary tumor cells gain metastatic competence remain poorly understood. Increasing evidence demonstrates that fusion of tumor (i.e., neoplastic) and immune (e.g., macrophages) cells generate a distinct population of tumor-immune hybrid cells with enhanced functional ability to migrate and disseminate into peripheral blood. Herein, our study investigates tumor-macrophage hybrid cells, an underexplored population of disseminated tumor cells, and their inherent heterogeneity and acquisition of molecular mechanisms underlying their dissemination as metastatic effectors in colorectal cancer (CRC). Through hybrid cell phenotyping utilizing integrative single-cell RNA sequencing (scRNA-seq), cyclic immunofluorescence (cyCIF) and functional assays with an in vitro model of CRC hybrid cells, we identify Runt-related transcription factor 1 ( Runx1) as a central regulator of hybrid cell motility and invasion. Runx1 depletion in hybrid cells suppressed functional protease expression, chemotactic activity and extracellular matrix (ECM) invasion. Furthermore, pharmacologic inhibition of RUNX1 in an in vivo model reduced hybrid tumor growth and dissemination into peripheral blood, key attributes of metastatic spread of disease. In patients with CRC, RUNX1 + hybrid cells were identified in both primary tumor and peripheral blood, where circulating hybrid cells (CHCs) exhibited enriched migratory and epithelial-to-mesenchymal transition (EMT) phenotypes. Taken together, these findings reveal a mechanistic role for RUNX1 in driving invasive behavior of tumor-immune hybrids and highlight disseminated CHCs as an under-recognized contributor to metastatic spread and a promising noninvasive biomarker for tumor progression.

  PublisherOA PDFDOI

• miniMTI: minimal multiplex tissue imaging enhances biomarker expression prediction from histology

  Research Square · 2026-03-19

  preprintOpen access1st authorCorresponding
  PublisherOA PDFDOI

• Trajectory Landscapes for Therapeutic Strategy Design in Agent-Based Tumor Microenvironment Models

  arXiv (Cornell University) · 2026-03-18

  preprintOpen accessSenior author

  Multiplex tissue imaging (MTI) enables high- dimensional, spatially resolved measurements of the tumor microenvironment (TME), but most clinical datasets are tempo- rally undersampled and longitudinally limited, restricting direct inference of underlying spatiotemporal dynamics and effective intervention timing. Agent-based models (ABMs) provide mech- anistic, stochastic simulators of TME evolution; yet their high- dimensional state space and uncertain parameterization make direct control design challenging. This work presents a reduced- order, simulation-driven framework for therapeutic strategy design using ABM-derived trajectory ensembles. Starting from a nominal ABM, we systematically perturb biologically plausible parameters to generate a set of simulated trajectories and construct a low-dimensional trajectory landscape describing TME evolution. From time series of spatial summary statistics extracted from the simulations, we learn a probabilistic Markov State Model (MSM) that captures metastable states and the transitions between them. To connect simulation dynamics with clinical observations, we map patient MTI snapshots onto the landscape and assess concordance with observed spatial phenotypes and clinical outcomes. We further show that conditioning the MSM on dominant governing parameters yields group-specific transition models to formulate a finite-horizon Markov Decision Process (MDP) for treatment scheduling. The resulting framework enables simulation-grounded therapeutic policy design for partially observed biological systems without requiring longitudinal patient measurements.

  PublisherDOI

• Trajectory Landscapes for Therapeutic Strategy Design in Agent-Based Tumor Microenvironment Models

  ArXiv.org · 2026-03-18

  articleOpen accessSenior author

  Multiplex tissue imaging (MTI) enables high- dimensional, spatially resolved measurements of the tumor microenvironment (TME), but most clinical datasets are tempo- rally undersampled and longitudinally limited, restricting direct inference of underlying spatiotemporal dynamics and effective intervention timing. Agent-based models (ABMs) provide mech- anistic, stochastic simulators of TME evolution; yet their high- dimensional state space and uncertain parameterization make direct control design challenging. This work presents a reduced- order, simulation-driven framework for therapeutic strategy design using ABM-derived trajectory ensembles. Starting from a nominal ABM, we systematically perturb biologically plausible parameters to generate a set of simulated trajectories and construct a low-dimensional trajectory landscape describing TME evolution. From time series of spatial summary statistics extracted from the simulations, we learn a probabilistic Markov State Model (MSM) that captures metastable states and the transitions between them. To connect simulation dynamics with clinical observations, we map patient MTI snapshots onto the landscape and assess concordance with observed spatial phenotypes and clinical outcomes. We further show that conditioning the MSM on dominant governing parameters yields group-specific transition models to formulate a finite-horizon Markov Decision Process (MDP) for treatment scheduling. The resulting framework enables simulation-grounded therapeutic policy design for partially observed biological systems without requiring longitudinal patient measurements.

  PublisherOA PDF

• Single-cell morphodynamical trajectories enable prediction of gene expression accompanying cell state change

  Cell Systems · 2026-04-02

  articleOpen access
  PublisherDOI

• Spatially resolved transcriptomic and proteomic profiling reveals cell interaction programs that predict Barrett’s esophagus progression

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-05-12

  articleOpen access

  Abstract Barrett’s esophagus (BE) is the precursor lesion of esophageal adenocarcinoma (EAC). It affects approximately 5% of adults in the United States and significantly increases the risk of developing EAC. However, current surveillance strategies cannot reliably distinguish patients who will progress from those who will remain stable. Direct studies of progressor BE are extremely limited due to availability of tissue with known progression outcomes, and have largely been restricted to genomic profiling approaches. The premalignant cellular landscape of progressor BE remains poorly understood. Here, we used complementary spatial transcriptomic and proteomic imaging to profile 34 non-dysplastic BE patients under endoscopic surveillance, including those who subsequently progressed to dysplasia or EAC, termed “Progressors” and those who remained stable, termed “Non-progressors”. Transcriptomics based Xenium analysis captured 974,604 cells across 70 whole-biopsy regions, while protein based imaging mass cytometry profiled 372,242 cells across 119 selected regions. FUME-TCRseq further quantified T cell clonotypes from matched tissues scrolls. Cellular composition was generally similar between Progressors and Non-progressors. However, Progressors showed increased intestinal Barrett’s columnar cells, B cells and gastric progenitor-like cells, together with enhanced immune-epithelial interactions, whereas Non-progressors retained coordinated stromal organization. Spatial interaction features strongly outperformed cell composition and density for progression prediction. Combined spatial interaction model achieved an area under the curve (AUC) of 0.97, compared with 0.62 and 0.68 for comparison and density alone. Complementary imaging mass cytometry further resolved the underlying immune programs, identifying cytotoxic and antigen presenting myeloid features enriched in progressors, and CD56⁺ associated memory T cell interactions enriched in non progressors. Together, these findings support a model that BE progression is driven by progressive remodeling of epithelial-immune-stromal architecture rather than emergence of distinct dysplasia-like cell subsets. Increased T cell clonal diversity and recruitment of cytotoxic and antigen-presenting immune niches may also reflect an evolving response to genomic alteration prior to dysplasia. These results establish spatial tissue architecture, rather than specific cell types, captures progression associated microenvironmental states in BE and provides a framework for spatially informed patient stratification and early cancer risk assessment.

  PublisherDOI

• miniMTI: minimal multiplex tissue imaging enhances biomarker expression prediction from histology

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-01-23

  articleOpen accessSenior authorCorresponding

  Virtual multiplexing from routine histology has advanced rapidly, yet morphology alone provides limited access to molecular state, imposing an intrinsic ceiling on H&E-only inference. Here, we introduce miniMTI, a molecularly anchored virtual staining framework that determines the minimal set of experimentally measured markers required, alongside H&E, to accurately reconstruct large multiplex tissue imaging (MTI) panels while preserving biologically and clinically relevant information. miniMTI learns from paired same-section H&E-MTI data using a unified multimodal generative model that can condition on arbitrary combinations of measured marker channels, coupled with an iterative panel selection strategy to rank informative molecular anchors. Across colorectal and prostate cancer cohorts spanning two MTI platforms and over 40 million cells, miniMTI reduces a 40-marker MTI assay to H&E plus as few as three measured molecular markers, while accurately recovering withheld markers, preserving cellular phenotypes and spatial tissue architecture, and disease-associated molecular programs, including Gleason grade-linked signatures. By integrating histology context with sparse molecular grounding, miniMTI overcomes the limitations of morphology-only virtual staining and provides a scalable, cost-effective approach for expanding MTI-level biomarker coverage with retained biological interpretability and clinical relevance.

  PublisherOA PDFDOI

• Leveraging deep learning for enhanced 3D confocal imaging of circulating hybrid cells

  2026-02-15

  articleSenior author
  PublisherDOI

• Language of Stains: Tokenization Enhances Multiplex Immunofluorescence and Histology Image Synthesis

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-03-11 · 1 citations

  preprintOpen accessSenior authorCorresponding

  Abstract Multiplex tissue imaging (MTI) is a powerful tool in cancer research, allowing spatially resolved, single-cell phenotype analysis. However, MTI platforms face challenges such as high costs, tissue loss, lengthy acquisition times, and complex analysis of large, multichannel images with batch effects. To address these challenges, we propose a novel computational method to model the interactions between dozens of panel markers and Hematoxylin & Eosin (H&E) staining, enabling in-silico generation of marker stains. This approach reduces the reliance on experimentally measured markers, bridging low-cost H&E data with MTI’s high-content information. Our approach uses a two-stage frame-work for channel-wise bioimage synthesis: first, vector quantization learns a visual token vocabulary, then a bidirectional transformer infers missing markers through masked language modeling. Comprehensive bench-marking across different MTI platforms and tissue types demonstrates the effectiveness of our method in improving marker prediction while maintaining biological relevance. This advance makes high-dimensional multiplex tissue imaging more accessible and scalable, supporting deeper insights and potential clinical applications in cancer research.

  PublisherOA PDFDOI

Recent grants

• Integrative single cell analyses of inflammation-derived circulating hybrid cells to identify aggressive disease

  NIH · $3.0M · 2021–2027

Frequent coauthors

• Joe W. Gray

  78 shared

• Guillaume Thibault

  44 shared

• Vahid Azimi

  44 shared

• Simone Arvisais‐Anhalt

  University of California, San Francisco

  42 shared

• Andrey Bychkov

  Nagasaki University

  42 shared

• Andrew M. Bellizzi

  University of Iowa

  42 shared

• Jacob T. Abel

  42 shared

• Ellen Araj

  The University of Texas Southwestern Medical Center

  42 shared

Education

• Postdoctoral researcher , Electrical Engineering and Computer Sciences

  UC Berkeley

  2015

• Ph.D, Mechanical Engineering

  University of California Berkeley

  2013

• M.S, Aerospace Engineering

  Korea Advanced Institute of Science and Technology

  2004

• B.S, Aerospace Engineering

  Korea Advanced Institute of Science and Technology

  2002

Awards & honors

• Family Self-Sufficiency and Stability Research Scholars Netw…
• Early Career Research Awards from the Upjohn Institute
• GSR Award from the Institute for Research on Labor and Emplo…
• Washington State Labor Research Grant from the Harry Bridges…
• Social Policy Fellowship Program from the West Coast Poverty…