Profile

Le Liu is a Ph.D. candidate in the School of Computing at Clemson University. His research interests are visualization and computer graphics. He is exploring uncertainty visualization techniques in the SAVAGE Graphics Lab under the direction of Dr. Donald House.

Download Le Liu's Resume

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Visualizing Time-Specific Hurricane Predictions, with Uncertainty, from Storm Path Ensembles

The U.S. National Hurricane Center (NHC) issues advisories every six hours during the life of a hurricane. These advisories describe the current state of the storm, and its predicted path, size, and wind speed over the next five days. However, from these data alone, the question “What is the likelihood that the storm will hit Houston with hurricane strength winds between 12:00 and 14:00 on Saturday?” cannot be directly answered. To address this issue, the NHC has recently begun making an ensemble of potential storm paths available as part of each storm advisory. Since each path is parameterized by time, predicted values such as wind speed associated with the path can be inferred for a specific time period by analyzing the statistics of the ensemble. This paper proposes an approach for generating smooth scalar fields from such a predicted storm path ensemble, allowing the user to examine the predicted state of the storm at any chosen time.

full paper, PPT

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Uncertainty Visualization by Representative Sampling from Prediction Ensembles

Data ensembles are often the foundation for inferring statistics for a summary display of an uncertain prediction. In a spatial context, these summary displays have the serious drawback that the uncertainty usually manifests in spatial spread that increases with time over the period of the prediction. Perceptually, this increase in spread is easily confounded with an increase in the size, or strength of the phenomenon being predicted. Hurricane predictions are a good case in point. The uncertainty in a path prediction, as officially presented by the US National Hurricane Center, is a cone whose apex is at the current storm position, whose centerline follows the predicted storm path over several days, and whose increasing width corresponds with prediction uncertainty over time into the forecast. It is well recognized that viewers tend to confuse the increase in uncertainty with an increase in storm size. In this paper, we present an approach to the presentation of uncertain predictions that attempts to avoid this perceptual problem by displaying a carefully chosen subset of the full ensemble, rather than a statistical summary. Examples from the hurricane prediction domain are used to illustrate the approach, showing how it can be used to greatly decrease the number of ensemble elements displayed, while still correctly conveying ensemble statistics.

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3D Computer Vision

Computer visualizations of 3D medical or geological data often require the simultaneous display of multiple layers. These displays tend to be difficult to interpret visually. This research will seek to improve understanding of the role that texture, applied to two surfaces, plays in helping to locate target features in such displays. A 3D eye-tracking system is also involved. The experiment program is implemented in C++/OpenGL and Python.

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Topology Study

This study focuses on creating a system which can generate evenly spaced 4-way-rotational-symmetry texture on a volume data based on the topology feature of that data, and location of singularities can be controlled. Topology, linear algebra and other mathematical knowledge are involved. The image is a visualization of a vector field which will be used to generate a 4-way-rotational-symmetry field.

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Volume Rendering -- Flyling Horse

This video, which is a three-member-team project, is volume rendered. My major contributions are the three missile trails, which are generated using perlin noise and advection, fall from the sky.

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Volume Rendering -- Missile Trail

This video is a wedge of missile trail.

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Web Site Visualization

-- Visualization

The image is a graph of Clemson website. I implemented a web crawler in Python, and visualized the data from NetworkX using Gephi and Matplotlib module.

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