Hyunghoon Cho

TX-Phase securely phases private genomes using Trusted Execution Environments, achieving high accuracy and efficiency on UK Biobank and Haplotype Reference Consortium datasets.

This paper introduces SF-GWAS, a secure federated framework for GWAS that enables efficient and accurate analysis of private genomic data across institutions.

The paper improves the SkSES method for privacy-preserving GWAS by integrating a learning-augmented approach, achieving up to 40% higher accuracy under memory constraints.

SF-Relate identifies genetic relatives across distributed genomic datasets using a privacy-preserving federated approach with high accuracy and efficiency.

This paper introduces a discriminative sequence model to assess transcriptomic re-identification risks by improving linking attack accuracy across datasets.

This paper introduces SafeGENIE, a secure and efficient algorithm for LMM-based GWAS that enables collaborative analysis without data sharing.

The paper introduces FAMHE, a privacy-preserving federated analytics system using multiparty homomorphic encryption, enabling accurate analysis of distributed medical data without sharing patient-level information.

This paper introduces a privacy-preserving genotype imputation method using a Trusted Execution Environment to protect genetic data while maintaining accuracy comparable to existing tools.

The paper introduces density-preserving visualization methods den-SNE and densMAP for single-cell RNA-seq data, revealing new insights into transcriptomic variability in biological processes.

This paper introduces privacy-preserving query-answering mechanisms for biomedical databases that maximize utility while achieving formal privacy guarantees, extending optimality results to asymmetric utility functions.

The paper introduces Trajectorama, an algorithm that uses gene coexpression to integrate cellular trajectories across studies, revealing developmental and disease-related processes.

The paper introduces a geometric sketching method to compactly summarize scRNA-seq data, revealing rare cell types and accelerating analysis tasks.

The paper introduces net-SNE, a neural network method for visualizing single-cell RNA sequencing data that scales to millions of cells and generalizes to new cells.