Abstract

The sensitive nature of genomic data poses significant challenges for data sharing and collaboration in biomedicine. Traditional safeguards, such as access control mechanisms, often lead to data fragmentation across silos, hindering large-scale analysis. In this talk, I will describe our recent work on secure federated (SF) algorithms, which leverage cryptography and distributed computing to enable collaborative genomic research without compromising privacy. I will showcase practical tools we have developed for key analysis tasks, including genome-wide association studies (Nature Genetics, 2025), principal component analysis (IEEE S&P, 2023), and the identification of genetic relatives (Genome Research, 2024). Finally, I will discuss our recent efforts to deploy these methods across the NIH All of Us and VA Million Veteran Program biobanks, as well as the broader opportunities that privacy-enhancing technologies offer for advancing biomedical data science

Short Bio

Hyunghoon (Hoon) Cho received his Ph.D. in Electrical Engineering and Computer Science at MIT in 2019. Previously, he received his M.S. and B.S. with Honors in Computer Science from Stanford University. His research focuses on overcoming key computational challenges in analyzing massive and distributed biomedical data, creating modern tools from applied cryptography and machine learning. He is especially interested in solving problems in the areas of biomedical data privacy, single-cell genomics, and network biology. He is a recipient of the NIH Director's Early Independence Award.