My name is Zhutian Chen (陈竹天), currently a Ph.D. candidate at Hong Kong University of Science and Technology. I’m a member of HKUST VisLab, supervised by Prof. Qu Huamin. I received my bachelor degree from South China University of Technology in 2015, where I worked in CIKE group as an undergraduate research assistant advised by Prof. Yi Cai.
My research interests are in Information Visualization, Augmented Reality, and Machine Learning. Specifically, I have been working on tools and techniques powered by deep learning to facilitate the creation of data-driven infographics in and for AR environments. I characterize my research as Futuristic Infographics Creation.
Authoring Infographics in Mobile Augmented Reality
We systematically study AR glyph-based Infographics to design and implement MARVisT, a mobile authoring tool. MARVisT leverages information from reality to assist non-experts in creating expressive AR glyph-based visualizations rapidly and effortlessly, thereby reshaping the representation of the real world with data. We also explore potential methods to visualize and interact with infoVis in mobile AR.
Immersive Visualization of Urban Information
This project aims to explore visualizing urban information in immersive environments (e.g., VR and AR). We propose a theoretical model to characterize the immersive visualizations of urban information and a supporting guideline for designing immersive visualizations under certain circumstances. We also introduce a technique to create exploded views in immersive environments.
Animated Narrative Visualization for Video Clickstream Data
We propose to use animation and data storytelling to present insights in video clickstream data. Two novel designs, non-linear time mapping and foreshadowing, are introduced to make the presentation more engaging and interesting.
Visualization of Massive Open Online Courses (MOOC) Data
The aim of this project is to serve MOOC instructors, instructional designers, institutional curriculum leaders and learning scientists by developing an open framework that combines: 1) Analytical methods for learning behavior analysis and predictive analytics; and 2) Visualization interfaces for understanding the huge amount of data collected by MOOC platforms and the analytical results.
Visual Analytics on Streaming Social Media Data
We design and develop StreamExplorer, a visual analytic system that encompasses an online event detection method and a tailored GPU-assisted Self-Organizing Map method, to facilitate the visual analysis, tracking, and comparison of a social stream (e.g., Twitter) at macroscopic, mesoscopic, and microscopic levels.
MARVisT: Authoring Glyph-based Visualization in Mobile Augmented Reality
IEEE Transactions on Visualization and Computer Graphics (TVCG), 2018
Exploring the design space of immersive urban analytics
Visual Informatics, 2017
StreamExplorer: A Multi-Stage System for Visually Exploring Events in Social Streams
IEEE Transactions on Visualization and Computer Graphics (TVCG), 2017
Immersive Urban Analytics through Exploded Views
Workshop Proceedings of IEEE VIS 2017: Immersive Analytics
Animated narrative visualization for video clickstream data
Proceedings of ACM SIGGRAPH ASIA Visualization Symposium, 2016
Blossom: Design of a Tangible Interface for Improving Intergenerational Communication for the Elderly
Proceedings of ACM the International Symposium on Interactive Technology and Ageing Populations, 2016
STAC: Enhancing Stacked Graphs for Time Series Analysis
Proceedings of IEEE Pacific Visualization Symposium (PacificVis), 2016