My passion on research hinges upon the love for visualizations that are simple, trustful, and beautiful. I develop interactive visualizations and visual analytics to promote the interplay between human, machines, and big data. I like to work with clever minds from other fields, for example, architects, urban planners, and graphic designers!

Current research interests

Computational design

Computational design is the merge of design techniques and computational technologies, through data-driven research and development that takes advantage of mass computing power, machine learning, and big data. I particularly focus on computational design for data visualization that aims to understand design patterns of visualizations and visual analytics, and develop computational tools to facilitate visualization design. I also do some research on computational design for floorplan and signboard.
ActFloor-GAN (TVCG'21)
a human-centric and steerable GAN model for floorplan design.
MV Layout (TVCG'21, JoV'21)
fundamental understanding for the design patterns of MV visualization.
Colormap Extraction (TVCG'21)
to extract colormaps from visualizations using deep learning.

Situated visualization in AR/VR

People wish to gain an in-depth understanding of the world we live in. The emerging VR/AR devices promote situated visualizations beyond the desktop. I develop advanced algorithms for context recognition for AR/VR applications, and novel interaction and visualization techniques to support immersive analytics.
UrbanVR (C&G'21)
an immersive analytics system for urban design in VR environment.
Context recognition (TIP'21 & '20)
deep learning models for recognizing floor-level lines and building facades.
LassoNet (TVCG'20)
a deep learning model for supporting lasso selection of point clouds.

Visual analytics

Visual analytics is "the science of analytical reasoning facilitated by interactive visual interfaces". Visual analytics research is highly interdisciplinary and combines various related research areas such as visualization, data mining, data management, data fusion, statistics and cognition science. In the era of big data, data visualization is often regarded as a key to big data analytics success. I develop visual analytics to address practical needs in various domains including transportation, smart cities, and explainable AI.
understanding the MAUP effect on deep-learning-based traffic prediction.
Topology density map (TVCG'21)
integrating road network topology in constructing density map for urban analysis and visualization.
balancing aggregations and details of movement data using route-aware edge bundling.
StreetVizor (TVCG'18)
exploring human-scale urban forms based on street views.
VitalVizor (CG&A'18)
a visual analytics system for studying urban vitality.
POI Signature (T-ITS'17)
a visual signature of movement patterns for different POI categories.
Waypoints OD (CGF'16)
visualzing waypoints-constrained OD patterns in movement data.
MobilityVis (TVCG'14)
visualzing mobility patterns in public transportation system.
Interchange Diagram (CGF'13)
visualzing interchange patterns in movement data.