Jacob (Jake) Chang

I am a Ph.D. candidate in Biomedical Data Science at Stanford University, co-advised by Dr. Sylvia Plevritis and Dr. Lu Tian. My research develops statistical and machine-learning methods for complex biomedical data, especially in settings where observations are linked by patients, tissues, spatial neighborhoods, perturbations, or other sources of biological structure.

A recurring theme in my work is that biomedical datasets are rarely made up of fully independent observations. Cells, tissue regions, and patients are often connected through spatial organization, shared biology, clinical context, technical variation, and latent heterogeneity. I am interested in methods that account for these connections directly, including representation-learning approaches that preserve shared and subgroup-specific biological variation, hierarchical models that quantify uncertainty across samples and patients, and evaluation strategies that better reflect realistic sources of dependence and distribution shift.

My recent work has focused on spatial and single-cell genomics, where I study multi-sample inference, uncertainty-aware modeling, and replicate-aware representation learning. More broadly, I hope to develop statistical machine-learning methods that support reliable biological and clinical insight across a range of biomedical data modalities, including genomics, histopathology, perturbation studies, longitudinal health data, and clinical decision-making.

I am originally from Mililani, Hawaiʻi, and earned bachelor’s degrees in Statistics and Sociology from the University of Notre Dame. Before graduate school, I worked as a Data Scientist at 84.51°, where I contributed to projects on the Health and Wellness Pharmacy Team and the Algorithmic Fairness Team. As a graduate student, I have interned as a computational biologist and bioinformatician at Genentech and Amgen, and in summer 2026 I will be a Genomic Data Science Intern at Element Biosciences.

Outside of research, I enjoy reading, playing hockey, and spending time with my wife and our dog.