Albert Higgins-Chen, MD, PhD

The Yale Omics & Longevity Lab develops computational approaches to measure biological aging and translate those measurements into tools for geroscience. A central challenge in the field is that aging unfolds over decades, varies across organs and individuals, and cannot be directly observed in short-term studies. Our goal is to build reliable molecular and computational measurements that capture meaningful aspects of the aging process and can ultimately accelerate the evaluation of interventions designed to extend healthspan.

Our work integrates large-scale epigenomic and multi-omic data with machine learning, artificial intelligence, and rigorous biomarker validation. We develop biological aging measures from DNA methylation, transcriptomics, proteomics, metabolomics, and clinical phenotypes, with particular emphasis on distinguishing true biological change from technical noise. We also study heterogeneity in aging across physiological systems, recognizing that different tissues and organ systems may age at different rates within the same individual.

A major focus of the lab is determining which aging biomarkers are useful for intervention studies. Rather than evaluating biomarkers only by their association with chronological age or mortality, we examine properties such as technical reliability, longitudinal stability, responsiveness to interventions, associations with risk factors, and risk prediction of various health outcomes. We apply these approaches across observational cohorts, clinical trials, and experimental systems to identify measurements that can serve as robust intermediate outcomes for geroscience research.

We also build open computational infrastructure to make aging research more scalable and reproducible. Through projects including POLARIS, TranslAGE, methylCIPHER, and related resources, we harmonize aging biomarkers and datasets, systematically benchmark their performance, and make molecular aging data easier to discover, compare, and analyze. Increasingly, we use AI agents and foundation models to automate data curation and harmonization and to learn directly from high-dimensional biological measurements rather than relying solely on existing human annotations or literature.

Our long-term objective is to create a shared computational framework for aging biology that connects molecular measurements across datasets, omic modalities, species, and interventions. By combining better measurement, large-scale harmonization, and AI-driven biological modeling, we aim to identify mechanisms of aging, improve the design of human studies and clinical trials, and ultimately help determine which interventions can meaningfully extend healthy human lifespan.

As a psychiatrist, I am also interested in the intersection of aging and mental health. Psychiatric illness, psychosocial exposures, and their treatments may influence biological aging, while aging-related molecular changes may contribute to neuropsychiatric vulnerability. This clinical perspective provides an additional lens through which we study the biological heterogeneity of aging and its consequences for human health.