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Eric Sun

Eric Sun

Incoming Assistant Professor at MIT | Aging, AI/ML, spatial & single-cell biology

Palo Alto, California
stanford
Cambridge, Massachusetts
mit+1
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Summary

Eric Sun is an emerging leader in computational biology, specializing in aging research. He will be an Assistant Professor of Biological Engineering at MIT and a Principal Investigator of the Sun Lab starting in early 2026. His research focuses on developing advanced computational and machine learning tools to decode the complex biology of aging at multi-scales, from single cells to whole organisms. mit+2
Eric has a strong academic foundation from elite institutions. He is completing his PhD in Biomedical Informatics at Stanford University, where he is a Knight-Hennessy Scholar and a Paul & Daisy Soros Fellow. He holds a Master's degree in Applied Mathematics and a Bachelor's degree in Chemistry and Physics from Harvard University, where he was a John Harvard Scholar, Amgen Scholar, SENS Summer Scholar, and elected to Phi Beta Kappa. stanford+1
His research contributions are notable, with a focus on machine learning, spatial transcriptomics, and single-cell omics to understand and quantify aging. He has published on topics such as cell type-specific aging clocks in the brain, spatial transcriptomic clocks revealing cell proximity effects, and uncertainty-calibrated prediction methods for single-cell spatial transcriptomics. google+3
Eric is committed to fostering scientific literacy and community engagement. As a high school student, he founded "Pueblo Programming," a community organization that provides workshops to teach programming skills. He is also fluent in English, Mandarin, and Spanish. youtube+2

Work

Education

Projects

Writing

Spatial transcriptomic clocks reveal cell proximity effects in brain ageing

January 1, 2025

This paper investigates how spatial transcriptomic data can be used to develop aging clocks, highlighting the influence of cellular proximity on the aging process in the brain.

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Eric Sun: Understanding brain aging at spatial and single-cell resolution with machine learning

January 1, 2025

An overview or perspective piece discussing how machine learning can be leveraged to gain a deeper understanding of brain aging at both spatial and single-cell resolutions.

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TISSUE: uncertainty-calibrated prediction of single-cell spatial transcriptomics improves downstream analyses

January 1, 2024

Introduces a method called TISSUE for uncertainty-calibrated prediction in single-cell spatial transcriptomics, aiming to enhance the accuracy and reliability of subsequent data analyses.

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Cell-type-specific aging clocks to quantify aging and rejuvenation in neurogenic regions of the brain

January 1, 2022

A research paper focused on developing aging clocks specific to cell types to understand and measure aging processes within brain regions related to neurogenesis.

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Optimal control of aging in complex networks

January 1, 2020

Research focusing on mathematical models to achieve optimal control over aging processes within complex biological networks.

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Hobbies

Enjoys photography, with a personal website dedicated to pictures taken in daily life. github