My research aims to identify novel therapeutic strategies for chronic lung disease. These diseases affect hundreds of millions of people worldwide, yet remain poorly understood at the cellular and molecular level. My lab attacks this problem by combining large-scale data generation with experimental biology by performing computational discovery in human cohorts and mechanistic validation in laboratory models. We generate and integrate multiple high-dimensional data modalities from human lung tissue, including single-cell and single-nucleus RNA sequencing, spatially resolved transcriptomics, proteomics, and quantitative clinical measurements such as pulmonary function testing and CT-based imaging. Together, these data characterize the cellular states, tissue microenvironments, and molecular signals that define individual patients’ disease biology. Computational findings are then directly coupled to experimental models such as precision-cut lung slices, organoids, and genetically modified mice, thereby creating a pipeline from population-scale discovery to causal insight and, ultimately, to therapeutic targets
Please describe one or two of your most interesting projects.
One project I find particularly compelling is our work on biological heterogeneity in Chronic Obstructive Pulmonary Disease (COPD) – one of the world’s leading causes of death, yet a disease for which there are few targeted therapies. The core question driving us is simple – why do patients with the same diagnosis follow such different paths?
To answer it, we are building molecular maps of the diseased lung at single-cell resolution. In a recent study, we profiled over 1.5 million individual nuclei from 141 patients spanning the full disease spectrum — one of the largest efforts of its kind. We identified distinct cellular states across every major lung cell type, and showed that the balance of these states shifts in coordinated ways that track closely with how patients actually progress clinically. Spatial transcriptomics then allowed us to ask not just what cells are doing, but where they are doing it: mapping multicellular niches within intact tissue and identifying neighborhood-level interactions. Paired plasma proteomics extended these tissue-level discoveries toward biomarkers detectable in blood – a first step toward precision medicine for lung.
How did you end up where you are today? (Your research journey)
I trained as a pulmonologist because I wanted to take care of patients with serious lung disease. These conditions are common, progressive, and despite how much we’ve learned, still remarkably difficult to treat. Spending time in clinic with patients who have COPD or pulmonary fibrosis makes one thing clear very quickly: we understand surprisingly little about why some patients progress rapidly while others remain stable for years, or what the biological underpinnings of that difference actually are. Those questions pulled me into research, and they still drive everything my lab does.
My path here wasn’t linear. I started in more traditional molecular biology, asking focused questions about specific proteins and pathways. Over time, as the tools evolved, I found myself drawn toward approaches that could capture biology at a broader scale — first transcriptomics, then single-cell methods, and now spatial transcriptomics and multi-omic integration. Each transition opened up questions that simply weren’t askable before. The shift to single-cell sequencing was a genuine turning point. For the first time, we could study the lung not as a homogeneous mass of averaged signals, but as a complex ecosystem, with every cell’s individual state preserved and resolvable.
I came to the University of Michigan after many years at Yale, drawn by the depth of the pulmonary program and the opportunity to build a lab that is genuinely translational – one where discoveries move between the computational, the experimental, and the clinical, and where each informs the others. That back-and-forth is, I think, where the most interesting science happens. The patients I see in clinic keep the research grounded. The research gives me hope that