Introduction
π Hi, I'm Di Weng (ηΏθ»), a tenure-track assistant professor at School of Software Technology, Zhejiang University. My research interest lies in information visualization and visual analytics, focusing on interactive data transformation and spatiotemporal data analysis. I've published over 15 papers in prestigious computer science conferences and journals, including IEEE VIS, ACM CHI, IEEE TVCG, IEEE ITS, etc. I've also served as program committee members for IEEE VIS 2023, ChinaVis 2022 and 2023, and CGI 2023, as well as reviewers for many journals and conferences. I received my B.Eng. degree from Shandong University in 2016 and Ph.D. degree from Zhejiang University in 2021. Prior to my current position, I was a researcher at Microsoft Research Asia from 2022 to 2023. For more information on my experience, please refer to the Experience page.
π₯ Looking for motivated students to join my research group! Please drop me an email if you are interested.
Newsβ
- Jul 16, 2023: Our paper, "Visualizing Large-Scale Spatial Time Series with GeoChron", has been accepted by IEEE VIS 2023!
- Jul 10, 2023: I'll present our latest work on data transformation, "Rigel: Transforming Tabular Data by Declarative Mapping", and also chair the "Application 1" session at the upcoming ChinaVis 2023 conference to be held in Chongqing, China.
- Apr 23, 2023: Our paper, "GeoCamera: Telling Stories in Geographic Visualizations with Camera Movements", will be presented at the upcoming CHI 2023 conference.
- Mar 6, 2023: I've joined School of Software Technology, Zhejiang University as a full-time tenure-track assistant professor.
Selected Publicationsβ
Zikun Deng, Shifu Chen, Xiao Xie, Guodao Sun, Mingliang Xu, Di Weng*, Yingcai Wu*
IEEE Transactions on Visualization and Computer Graphics (Early Access)
Abstract: Numerous patterns found in urban phenomena, such as air pollution and human mobility, can be characterized as many directed geospatial networks (geo-networks) that represent spreading processes in urban space. These geo-networks can be analyzed from multiple levels, ranging from the macro-level of summarizing all geo-networks, meso-level of comparing or summarizing parts of geo-networks, and micro-level of inspecting individual geo-networks. Most of the existing visualizations cannot support multilevel analysis well. These techniques work by: 1) showing geo-networks separately with multiple maps leads to heavy context switching costs between different maps; 2) summarizing all geo-networks into a single network can lead to the loss of individual information; 3) drawing all geo-networks onto one map might suffer from the visual scalability issue in distinguishing individual geo-networks. In this study, we propose GeoNetverse, a novel visualization technique for analyzing aggregate geo-networks from multiple levels. Inspired by metro maps, GeoNetverse balances the overview and details of the geo-networks by placing the edges shared between geo-networks in a stacked manner. To enhance the visual scalability, GeoNetverse incorporates a level-of-detail rendering, a progressive crossing minimization, and a coloring technique. A set of evaluations was conducted to evaluate GeoNetverse from multiple perspectives.
Shuhan Liu, Di Weng*, Yuan Tian, Zikun Deng, Haoran Xu, Xiangyu Zhu, Honglei Yin, Xianyuan Zhan, Yingcai Wu
IEEE Transactions on Visualization and Computer Graphics, vol. 29, no. 1, pp. 1091-1101, Jan. 2023
Abstract: Improving the efficiency of coal-fired power plants has numerous benefits. The control strategy is one of the major factors affecting such efficiency. However, due to the complex and dynamic environment inside the power plants, it is hard to extract and evaluate control strategies and their cascading impact across massive sensors. Existing manual and data-driven approaches cannot well support the analysis of control strategies because these approaches are time-consuming and do not scale with the complexity of the power plant systems. Three challenges were identified: a) interactive extraction of control strategies from large-scale dynamic sensor data, b) intuitive visual representation of cascading impact among the sensors in a complex power plant system, and c) time-lag-aware analysis of the impact of control strategies on electricity generation efficiency. By collaborating with energy domain experts, we addressed these challenges with ECoalVis, a novel interactive system for experts to visually analyze the control strategies of coal-fired power plants extracted from historical sensor data. The effectiveness of the proposed system is evaluated with two usage scenarios on a real-world historical dataset and received positive feedback from experts.
Ran Chen, Di Weng*, Yanwei Huang, Xinhuan Shu, Jiayi Zhou, Guodao Sun, Yingcai Wu
IEEE Transactions on Visualization and Computer Graphics, vol. 29, no. 1, pp. 128-138, Jan. 2023
Abstract: We present Rigel, an interactive system for rapid transformation of tabular data. Rigel implements a new declarative mapping approach that formulates the data transformation procedure as direct mappings from data to the row, column, and cell channels of the target table. To construct such mappings, Rigel allows users to directly drag data attributes from input data to these three channels and indirectly drag or type data values in a spreadsheet, and possible mappings that do not contradict these interactions are recommended to achieve efficient and straightforward data transformation. The recommended mappings are generated by enumerating and composing data variables based on the row, column, and cell channels, thereby revealing the possibility of alternative tabular forms and facilitating open-ended exploration in many data transformation scenarios, such as designing tables for presentation. In contrast to existing systems that transform data by composing operations (like transposing and pivoting), Rigel requires less prior knowledge on these operations, and constructing tables from the channels is more efficient and results in less ambiguity than generating operation sequences as done by the traditional by-example approaches. User study results demonstrated that Rigel is significantly less demanding in terms of time and interactions and suits more scenarios compared to the state-of-the-art by-example approach. A gallery of diverse transformation cases is also presented to show the potential of Rigel's expressiveness.
Contactβ
- Office: Room E502, School of Software Technology, Zhejiang University, Ningbo, Zhejiang, China
- Email: dweng@zju.edu.cn