Lieyu Shi

About me

I am a Ph.D. candidate in computer science department from University of Houston, TX, USA, under supervision of Dr. Guoning Chen. I'm to graduate in December 2019 and actively seeking software engineer position. Before that, I got the B.S. in Computational Mathematics from Xi'an Jiaotong University in July 2013.

My research primarily centers on flow visualization and analysis, especially the feature estimation from integral curves of input with unsupervised clustering techniques. I've been working in DaViM Lab since 08/2014.

Education

Aug. 2014 -- Dec. 2019 Ph.D. in computer science, University of Houston, TX, USA
Aug. 2009 -- Jul. 2013 B.S. in mathematics, Xi'an Jiaotong University, China

Publications

Estimate Separation-like Behaviors from Sets of Integral Curves
Lieyu Shi and Guoning Chen.
under review for PacificVis 2020
[ paper ] [ source code ]

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Integral Curve Clustering and Simplification for Flow Visualization: A Comparative Evaluation
Lieyu Shi , Robert Laramee and Guoning Chen.
IEEE Transactions on Visualization and Computer Graphics 2019 (accepted)
[ paper ] [ supplemental ] [ video ] [ source code ]

Unsupervised clustering techniques have been widely applied to flow simulation data to alleviate clutter and occlusion in the resulting visualization. However, there is an absence of systematic guidelines for users to evaluate (both quantitatively and visually) the appropriate clustering technique and similarity measures for streamline and pathline curves. In this work, we provide an overview of a number of prevailing curve clustering techniques. We then perform a comprehensive experimental study to qualitatively and quantitatively compare these clustering techniques coupled with popular similarity measures used in the flow visualization literature. Based on our experimental results, we derive empirical guidelines for selecting the appropriate clustering technique and similarity measure given the requirements of the visualization task. We believe our work will inform the task of generating meaningful reduced representations for large-scale flow data and inspire the continuous investigation of a more refined guidance on clustering technique selection.

Metric-based Curve Clustering and Feature Extraction in Flow Visualization
Lieyu Shi and Guoning Chen.
IEEE CAD & Graphics 2017 short paper
[ paper ] [ supplemental ] [ source code ]

We design a number of distance metrics with linear-complexity based on the geometric and statistic properties of 3D curves, and apply them to classify streamlines and the trajectories of particles for flow visualization. The results show that our geometric metrics are more effective than existing metrics (especially spatial-based metrics), enabling the extraction of clusters and representative curves with more insightful and rich information about the flow behaviors..

Analysis Enhanced Particle-based Flow Visualization
Lieyu Shi , Lei Zhang, Wei Cao and Guoning Chen.
Visualization and Data Analysis (VDA 2017),
[ paper ] [ source code ]

Particle-based fluid simulation (PFS), such as Smoothed Particle Hydrodynamics (SPH) and Position-based Fluid (PBF), is a mesh-free method that has been widely used in various fields, including astrophysics, mechanical engineering, and biomedical engineering for the study of liquid behaviors under different circumstances. Due to its meshless nature, most analysis techniques that are developed for mesh-based data need to be adapted for the analysis of PFS data. In this work, we study a number of flow analysis techniques and their extension for PFS data analysis, including the FTLE approach, Jacobian analysis, and an attribute accumlation framework. In particular, we apply these analysis techniques to free surface fluids. We demonstrate that these analyses can reveal some interesting underlying flow patterns that would be hard to see otherwise via a number of PFS simulated flows with different parameters and boundary settings. In addition, we point out that an in-situ analysis framework that performs these analyses can potentially be used to guide the adaptive PFS to allocate the computation and storage power to the regions of interest during the simulation..

Work Experience

Jan. 2015 -- Dec. 2019 Teaching assistant in computer science department in University of Houston
May 2019 -- Aug. 2019 Data analyst in wells technology team, Shell Houston
May 2018 -- Aug. 2018 Data analyst in wells technology team, Shell Houston