Sehi L'Yi, PhD

Biography

Research Fellow in Biomedical Informatics

Sehi L’Yi received his PhD in Computer Science and Engineering at Seoul National University, where he studied Information Visualization and Human-Computer Interaction under the supervision of Dr. Jinwook Seo. His doctoral research includes designing and implementing visual analytics tools for bioinformaticians and helping people to effectively compare information using visualization techniques. In the HIDIVE lab, he is working on HiGlass project, which is a web-based viewer for genome interaction maps.

News

• Best Poster and Best Paper Honorable Mention at IEEE VIS
• Participants wanted! User study on Genome-mapped Data Visualizations

Publications

• International Electronic Health Record-Derived COVID-19 Clinical Course Profiles: the 4CE Consortium
• International Changes in COVID-19 Clinical Trajectories Across 315 Hospitals and 6 Countries: a 4CE Consortium Study
• International comparisons of laboratory values from the 4CE collaborative to predict COVID-19 mortality
• Potential pitfalls in the use of real world data to study Long COVID
• GenoREC: A Recommendation System for Interactive Genomics Data Visualization
• Changes in laboratory value improvement and mortality rates over the course of the pandemic: an international retrospective cohort study of hospitalised patients infected with SARS-CoV-2
• Gosling: A Grammar-based Toolkit for Scalable and Interactive Genomics Data Visualization
• Multi-View Design Patterns and Responsive Visualization for Genomics Data
• Chromoscope: interactive multiscale visualization for structural variation in human genomes
• Cistrome Explorer: An Interactive Visual Analysis Tool for Large-Scale Epigenomic Data
• The Role of Visualization in Genomics Data Analysis Workflows: The Interview
• Cistrome Data Browser: integrated search, analysis and visualization of chromatin data
• Gos: a declarative library for interactive genomics visualization in Python
• Enabling Multimodal User Interactions for Genomics Visualization Creation
• Drava: Concept-Driven Exploration of Small Multiples using Interpretable Latent Vectors