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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.


Research Projects

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.


Publications

MARVisT: Authoring Glyph-based Visualization in Mobile Augmented Reality

Zhutian Chen, Yijia Su, Yifang Wang, Qianwen Wang, Huamin Qu, Yingcai Wu.

IEEE Transactions on Visualization and Computer Graphics (TVCG), 2018

Exploring the design space of immersive urban analytics

Zhutian Chen, Yifang Wang, Tianchen Sun, Xiang Gao, Wei Chen, Zhigeng Pan, Huamin Qu, Yingcai Wu.

Visual Informatics, 2017

StreamExplorer: A Multi-Stage System for Visually Exploring Events in Social Streams

Yingcai Wu, Zhutian Chen, Guodao Sun, Xiao Xie, Nan Cao, Shixia Liu, Weiwei Cui.

IEEE Transactions on Visualization and Computer Graphics (TVCG), 2017

Immersive Urban Analytics through Exploded Views

Zhutian Chen, Huamin Qu, Yingcai Wu.

Workshop Proceedings of IEEE VIS 2017: Immersive Analytics

Animated narrative visualization for video clickstream data

Yun Wang, Zhutian Chen, Quan Li, Xiaojuan Ma, Qiong Luo, Huamin Qu

Proceedings of ACM SIGGRAPH ASIA Visualization Symposium, 2016

Blossom: Design of a Tangible Interface for Improving Intergenerational Communication for the Elderly

Mingqian Zhao, Zhutian Chen, Ke Lu, Chaoran Li, Huamin Qu, Xiaojuan Ma

Proceedings of ACM the International Symposium on Interactive Technology and Ageing Populations, 2016

STAC: Enhancing Stacked Graphs for Time Series Analysis

Yun Wang, Tongshuang Wu, Zhutian Chen, Qiong Luo, Huamin Qu.

Proceedings of IEEE Pacific Visualization Symposium (PacificVis), 2016