LI Wenchao

About Me

Thinker, Researcher, Creative Doer

Hi! My name is LI Wenchao (李文超).

I'm a senior research and development engineer at Huawei. I develop business intelligence (BI) software and services for Huawei Cloud.

I received my Ph.D. from the Department of Computer Science and Engineering at The Hong Kong University of Science and Technology (HKUST), where I worked with Prof. Huamin Qu in the VisLab.

Prior to joining HKUST, I obtained my master's degree in Computer Technology from University of Chinese Academy of Sciences and my bachelor's degree in Computer Science and Technology from Sichuan University. During my Master's programme, I was advised by Prof. Hui Huang and Prof. Ruizhen Hu.

I've also interned at Microsoft Research Asia, ByteDance, Huawei Cloud, and others. Details are in my resume.

  • Research Interests
  • To facilitate the understanding and communication of complex data, I develop innovative tools that synergistically leverage human and machine capabilities. My systems incorporate techniques from information visualization, human-data interaction, and data storytelling to enable effective data exploration and communication.
  • Email
  • wenc.li at outlook.com

Resume

Experience

Education

09/2017 - 10/2023

The Hong Kong University of Science and Technology

Department of Computer Science and Engineering

Advisor: Huamin Qu

09/2014 - 07/2017

University of Chinese Academy of Sciences

Shenzhen Institutes of Advanced Technology

Advisors: Hui Huang and Ruizhen Hu

09/2010 - 07/2014

Sichuan University

College of Computer Science

Research & Work

08/2022 - 04/2023

Data Intelligence Innovation Lab, Huawei Cloud

Research intern, working with Ke Xu.

04/2022 - 07/2022

Data Platform, ByteDance

Software development intern.

10/2021 - 04/2022

Data, Knowledge, Intelligence (DKI) Group, Microsoft Research Asia

Research intern, working with Yun Wang

08/2019 - 04/2020

Data, Knowledge, Intelligence (DKI) Group, Microsoft Research Asia

Research intern, working with Yun Wang

04/2016 - 07/2017

Visual Computing Research Center, Shenzhen University

Research assistant, working with Ruizhen Hu

12/2013 – 04/2014

Shenzhen VisuCA Key Lab, Shenzhen Institutes of Advanced Technology

Research intern, working with Qian Zheng

Teaching

Spring 2020

Data Visualization

Teaching assistant, The Hong Kong University of Science and Technology

Fall 2018

Cryptography and Security

Teaching assistant, The Hong Kong University of Science and Technology

Spring 2017

Introduction to Visual Information Processing

Teaching assistant, Shenzhen University

Fall 2016

Computer Graphics

Teaching assistant, Shenzhen University

Community Involvement

2014

Student Volunteer

SIGGRAPH Asia

Publications

Research Works

2023

NetworkNarratives: Data Tours for Visual Network Exploration and Analysis

This paper introduces semi-automatic data tours to aid the exploration of complex networks. Compared to guidance and recommender systems for visual analytics, we provide a set of goal-oriented tours for network overview, ego-network analysis, community exploration, and other tasks. Based on interviews with five network analysts, we developed a user interface (NetworkNarratives) and 10 example tours. The interface allows analysts to navigate an interactive slideshow featuring facts about the network using visualizations and textual annotations. On each slide, an analyst can freely explore the network and specify nodes, links, and subgraphs as seed elements for follow-up tours.

GeoCamera: Telling Stories in Geographic Visualizations with Camera Movements

This work aims to lower the barrier of crafting diverse camera movements for geographic data videos. We first analyze a corpus of 66 geographic data videos and derive a design space of camera movements with a dimension for geospatial targets and one for narrative purposes. Based on the space, we propose a set of adaptive camera shots and further develop GeoCamera, an interactive tool that empowers users to flexibly design camera movements for geographic visualizations.

2021

AniVis: Generating Animated Transitions Between Statistical Charts with a Tree Model

We present AniVis, an automated approach for generating animated transitions to demonstrate the changes between two statistical charts. AniVis models each statistical chart into a tree-based structure. Given an input chart pair, the differences of data and visual properties of the chart pair are formalized as tree edit operations. The edit operations can be mapped to atomic transition units. Through this approach, the animated transition between two charts can be expressed as a set of transition units. Then, we conduct a formative study to understand people's preferences for animation sequences. Based on the study, we propose a set of principles and a sequence composition algorithm to compose the transition units into a meaningful animation sequence. Finally, we synthesize these units together to deliver a smooth and intuitive animated transition between charts.

2020

Improving Engagement of Animated Visualization with Visual Foreshadowing

Informed by the role of foreshadowing that builds the expectation in film and literature, we introduce visual foreshadowing to improve the engagement of animated visualizations. In specific, we propose designs of visual foreshadowing that engage the audience while watching the animation. Moreover, we implement a proof-of-concept authoring tool to demonstrate our approach and conduct a user study to learn the efficacy of engagement enhancement.

2017

Learning to Predict Part Mobility from a Single Static Snapshot

We introduce a method for learning a model for the mobility of parts in 3D objects. Our method allows not only to understand the dynamic functionalities of one or more parts in a 3D object, but also to apply the mobility functions to static 3D models.

Co-Locating Style-Defining Elements on 3D Shapes

We introduce a method for co-locating style-defining elements over a set of 3D shapes. Our goal is to translate high-level style descriptions, such as "Ming" or "European" for furniture models, into explicit and localized regions over the geometric models that characterize each style. For each style, the set of style-defining elements is defined as the union of all the elements that are able to discriminate the style. Another property of the style-defining elements is that they are frequently-occurring, reflecting shape characteristics that appear across multiple shapes of the same style.

LI Wenchao

Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
+852 65444084 (WhatsApp)
wenc.li at outlook.com