Eric Sun

Eric obtained an A.B. in Chemistry and Physics and S.M. in Applied Mathematics from Harvard University in 2020. He completed his Ph.D. in Biomedical Informatics at Stanford University in 2025 under the joint supervision of Professors Anne Brunet and James Zou, where his research involved building computational methods for the analysis of spatial and single-cell omics and machine learning tools to track cellular aging in the brain. Eric joins MIT as an Assistant Professor of Biological Engineering, where his lab develops computational and machine learning tools to decode the biology of aging across multiple scales.

Aging is the greatest risk factor for a broad range of chronic diseases. We have a long-standing interest in understanding the complex biology of aging from the level of the fundamental biological unit (the cell) to the level of the whole system (the organism). Such an understanding can be leveraged to discover and engineer interventions for broadly improving human health and staving off disease. To that end, we develop computational and artificial intelligence/machine learning (AI/ML) tools to (1) measure biological aging from cell to organism, (2) predict and simulate the effects of interventions, including genetic perturbations, on cells and tissues, and (3) design new interventions against aging and optimize their parameters for improved efficacy.

We integrate computational frameworks for model building (e.g. AI/ML, deep learning, statistical models) with experimental approaches for biological data generation (e.g. spatial omics and single-cell omics) and model validation (e.g. imaging, perturbational assays). We straddle the fields of computational biology and bioinformatics, machine learning, systems biology, and neuroimmunology.