Chen Chen

Hello, I'm Chen Chen

I am a Ph.D. Student at University of Maryland, College Park advised by Professor Leo Zhicheng Liu. My research lies at the intersection of visualization, data science, and artificial intelligence. I develop semi-automated systems to help understand chart semantics, reuse data visualizations, and perfrom visual data analysis.

Before coming to the US, I obtained my master’s degree from University of Chinese Academy of Sciences and my bachelor’s degree from Hefei University of Technology.

CV Scholar Email

News

May 2023: Our State-of-the-Art (STAR) paper on visualization corpora for automated chart analysis has been accepted to EuroVis 2023! Preprint here!
April 2023: Reviewer for IEEE VIS 2023.
May 2022: I have started my summer internship at Adobe!
July 2021: Atlas, our graphics-centric visualizaiton grammar, has been accepted to IEEE VIS 2021 (short paper track)!

Publications

The State of the Art in Creating Visualization Corpora for Automated Chart Analysis

Chen Chen, Zhicheng Liu

EuroVis, 2023

We present a state-of-the-art report on visualization corpora in automated chart analysis research. We survey 56 papers that created or used a visualization corpus as the input of their research techniques or systems. Based on a multi-level task taxonomy that identifies the goal, method, and outputs of automated chart analysis, we examine the property space of existing chart corpora along five dimensions: format, scope, collection method, annotations, and diversity.

Doi Pdf

Atlas: A Procedural Data Visualization Grammar

Zhicheng Liu, Chen Chen, Francisco Morales, Yishan Zhao

IEEE VIS, 2021

We present Atlas, a procedural grammar for constructing data visualizations. Atlas adopts a graphics-centric approach to expose the generative process of a visualization through concatenated high-level production rules.

Project Page Doi Pdf

ANOVA Gaussian process modeling for high-dimensional stochastic computational models

Chen Chen, Qifeng Liao

Journal of Computational Physics, 2020

We present a novel analysis-of-variance Gaussian process (ANOVA-GP) emulator for science models governed by partial differential equations (PDEs) with high-dimensional random inputs.

Doi Pdf