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Address: | Makai Ocean Engineering, Inc., P.O. Box 1206, Kailua, Hawaii 96734 [Map] [Panorama] |
| Email: | or | |
| Phone: | 1.808.259.8871 | |
| Facsimile: | 1.808.259.8238 |
I am a Computer Scientist at Makai Ocean Engineering, Inc. on the windward coast of Oahu in Hawaii. Makai provides ocean engineering services worldwide and is a major supplier of submarine cable installation and planning software. At Makai, I work on scientific visualization and analysis software targeting ocean and atmospheric scientists.
Before joining Makai in 2009, I finished my Ph.D. in Computer Science with the Visualization and Graphics Research Group of the Institute for Data Analysis and Visualization (IDAV) at University of California, Davis. Broadly, my area of research was scientific visualization — designing better algorithms and tools for visual data analysis; my dissertation work focused on surface extraction, and the visualization of high-dimensional, multi-variate data.
I’m also an avid golfer, but discussing that line of research would take yet another webpage!
John C. Anderson, “Scientific Visualization Techniques for Volume Fraction Data and Function Fields,” Ph.D. Dissertation, Computer Science Department, University of California Davis, June 2009. [PDF] [BibTeX]
Abstract While the scientific visualization community is comfortable with isosurfacing and volume rendering of scalar fields, data from simulations and sensors often have additional constraints or dimensions that are not easily handled by these algorithms. In the first part of this dissertation we consider volume fraction data and the material interface reconstruction problem, for which existing isosurfacing and segmentation methods do not produce satisfactory results. Optimization-based methods are introduced that produce accurate multi-material segmenting surfaces through volume fraction data. In the second part, we discuss visualization techniques for function fields. A dimension reduction approach based upon probing and range-space segmentation is introduced, allowing function fields to be analyzed with traditional visualization algorithms. Finally, queries are considered for explicit feature extraction.
Below is a list of my research publications (in reverse chronological order):
John C. Anderson, Christoph Garth, Mark A. Duchaineau, and Kenneth I Joy, “Smooth, Volume-Accurate Material Interface Reconstruction,” IEEE Trans. on Visualization and Computer Graphics, To appear.
Abstract A new material interface reconstruction method for volume fraction data is presented. Our method is comprised of two components: first, we generate initial interface topology; then, using a combination of smoothing and volumetric forces within an active interface model, we iteratively transform the initial material interfaces into high-quality surfaces that accurately approximate the problem’s volume fractions. Unlike all previous work, our new method produces material interfaces that are smooth, continuous across cell boundaries, and segment cells into regions with proper volume. These properties are critical during visualization and analysis. Generating high-quality mesh representations of material interfaces is required for accurate calculations of interface statistics, and dramatically increases the utility of material boundary visualizations.
Luke J. Gosink, Christoph Garth, John C. Anderson, E. Wes Bethel, and Kenneth I Joy, “An Application of Multivariate Statistical Analysis for Query-Driven Visualization,” IEEE Trans. on Visualization and Computer Graphics, To appear.
Abstract Driven by the ability to generate ever-larger, increasingly complex data, there is an urgent need in the scientific community for scalable analysis methods that can rapidly identify salient trends in scientific data. Query-Driven Visualization (QDV) strategies are among the small subset of techniques that can address both large and highly complex datasets. This paper extends the utility of QDV strategies with a statistics-based framework that integrates non-parametric distribution estimation techniques with a new segmentation strategy to visually identify statistically significant trends and features within the solution space of a query. In this framework, query distribution estimates help users to interactively explore their query’s solution and visually identify the regions where the combined behavior of constrained variables is most important, statistically, to their inquiry. Our new segmentation strategy extends the distribution estimation analysis by visually conveying the individual importance of each variable to these regions of high statistical significance. We demonstrate the analysis benefits these two strategies provide and show how they may be used to facilitate the refinement of constraints over variables expressed in a user’s query. We apply our method to datasets from two different scientific domains to demonstrate its broad applicability.
Jose M. Andres, McKay Davis, Kayo Fujiwara, John C. Anderson, Tie Fang, and Michael Nedbal, “A Geospatially Enabled, PC-based, Software to Fuse and Interactively Visualize Large 4D/5D Data Sets,” OCEANS Conference, Marine Technical Society/American Institute of Electrical Engineers, Oct. 2009.
Abstract This paper describes the development and main operational capabilities of a PC-based, geospatially enabled software that can fuse and visualize large, multi-variable data sets that change in space (x,y,z) and time (t). The new software has the ability to simultaneously visualize imagery, bathymetry/terrain, and true volumetric (voxel) data in a fully interactive geo-referenced mode. In addition to providing global coverage, a key feature of this software is the capability to interactively visualize large data sets while operating on a desktop PC. This is achieved by using tiling and Level Of Detail (LOD) technology for terrain, imagery, and volumetric data as well as compression techniques and the multithreading capabilities of modern PCs.
John C. Anderson, Luke J. Gosink, Mark A. Duchaineau, and Kenneth I Joy, “Interactive Visualization of Function Fields by Range-Space Segmentation,” Computer Graphics Forum (Proc. of EuroVis), June 2009.
Abstract We present a dimension reduction and feature extraction method for the visualization and analysis of function field data. Function fields are a class of high-dimensional, multi-variate data in which data samples are one-dimensional scalar functions. Our approach focuses upon the creation of high-dimensional range-space segmentations, from which we can generate meaningful visualizations and extract separating surfaces between features. We demonstrate our approach on high-dimensional spectral imagery, and particulate pollution data from air quality simulations.
Luke J. Gosink, John C. Anderson, E. Wes Bethel, and Kenneth I. Joy, “Query-Driven Visualization of Time-Varying Adaptive Mesh Refinement Data,” IEEE Trans. on Visualization and Computer Graphics (Proc. of IEEE Visualization), Nov./Dec. 2008. [PDF] [BibTeX]
Abstract The visualization and analysis of AMR-based simulations is integral to the process of obtaining new insight in scientific research. We present a new method for performing query-driven visualization and analysis on AMR data, with specific emphasis on time-varying AMR data. Our work introduces a new method that directly addresses the dynamic spatial and temporal proper ties of AMR grids which challenge existing visualization methods. Further, we present the first implementation of query-driven visualization on the GPU that uses a GPU-based indexing structure to answer queries and efficiently utilize GPU memory. We apply our method to two different science domains to demonstrate its broad applicability.
Brian C. Budge, John C. Anderson, and Kenneth I. Joy, “Caustic Forecasting: Unbiased Estimation of Caustic Lighting for Global Illumination,” Computer Graphics Forum (Proc. of Pacific Graphics), Oct. 2008. [PDF] [BibTeX]
Abstract We present an unbiased method for generating caustic lighting using importance sampled Path Tracing with Caustic Forecasting. Our technique is part of a straightforward rendering scheme which extends the Illumination by Weak Singularities method to allow for fully unbiased global illumination with rapid convergence. A photon shooting preprocess, similar to that used in Photon Mapping, generates photons that interact with specular geometry. These photons are then clustered, effectively dividing the scene into regions which will contribute similar amounts of caustic lighting to the image. Finally, the photons are stored into spatial data structures associated with each cluster, and the clusters themselves are organized into a spatial data structure for fast searching. During rendering we use clusters to decide the caustic energy importance of a region, and use the local photons to aid in importance sampling, effectively reducing the number of samples required to capture caustic lighting.
Brian C. Budge, John C. Anderson, Christoph Garth, and Kenneth I. Joy, “A Hybrid CPU-GPU Implementation for Interactive Ray-Tracing of Dynamic Scenes,” Tech. Rep. CSE–2008–9, University of California, Davis, July 2008. [BibTeX]
Also presented as a poster at IEEE Symposium on Interactive Ray Tracing 2008.
Abstract In recent years, applying the powerful computational resources delivered by modern GPUs to ray tracing has resulted in a number of ray tracing implementations that allow rendering of moderately sized scenes at interactive speeds. For n on-static scenes, besides ray tracing performance, fast construction of acceleration data structures such as kd-trees is of primary concern. In this paper, we present a novel implementation for the ray tracing of both static and dynami c scenes. We first describe an optimized GPU-based ray tracing approach within the CUDA framework that does not explicitly make use of ray coherency or architectural specifics and is therefore simple to implement, while still exceeding performance of previously presented approaches. Optimal performance is achieved by empirically tuning the ray tracing kernel to the executing hardware. Furthermore, we describe a straightforward parallel approach for approximate quality kd-tree construction, aimed at multi-core CPUs. The resulting hybrid ray tracer is able to render fully dynamic scenes with hundreds of thousands of triangles at interactive speeds. We describe our implementation in detail and provi de a performance analysis and comparison to prior work.
John C. Anderson, Christoph Garth, Mark A. Duchaineau, and Kenneth I. Joy, “Discrete Multi-Material Interface Reconstruction for Volume Fraction Data,” Computer Graphics Forum (Proc. of EuroVis), vol. 27, pp. 1015—1022, May 2008. [PDF] [BibTeX]
Abstract Material interface reconstruction (MIR) is the task of constructing boundary interfaces between regions of homogeneous material, while satisfying volume constraints, over a structured or unstructured spatial domain. In this paper, we present a discrete approach to MIR based upon optimizing the labeling of fractional volume elements within a discretization of the problem’s original domain. We detail how to construct and initially label a discretization, and introduce a* volume conservative swap *move for optimization. Furthermore, we discuss methods for extracting and visualizing material interfaces from the discretization. Our technique has significant advantages over previous methods: we produce interfaces between multiple materials that are continuous across cell boundaries for time-varying and static data in arbitrary dimension with bounded error.
E. Wes Bethel, Luke J. Gosink, John C. Anderson, and Kenneth I. Joy, “Variable Interactions in Query-Driven Visualization,” Tech. Rep. LBNL–63674, Lawrence Berkeley National Laboratory, Dec. 2007. [PDF] [BibTeX]
Research highlight of query-driven visualization for the SciDAC2 Visualization and Analytics Center for Enabling Technologies.
Luke J. Gosink, John C. Anderson, E. Wes Bethel, and Kenneth I. Joy, “Variable Interactions in Query Driven Visualization,” IEEE Trans. on Visualization and Computer Graphics (Proc. of IEEE Visualization), vol. 13, pp. 1400—1407, Nov./Dec. 2007. [PDF] [BibTeX]
Nominated for Best Paper award.
Abstract Our capability to generate increasingly large and more complex datasets has established the need for scalable methods that can provide insight into important variable trends. Query-driven methods are among the small subset of techniques that are able to address both large and highly complex data sets. This paper presents a new method in which coherent and meaningful visualizations are constructed to convey relational information about the trends that exist between variables in a query. Correlation fields are created between pairs of variables and used in conjunction with the cumulative distribution function of each of the query’s variables to reveal, both visually and statistically, trends in variable behavior and interactions. We illustrate our concepts by discussing interactions between variables in two flame-front simulations.
John C. Anderson, Luke J. Gosink, Mark A. Duchaineau, and Kenneth I. Joy, “Feature Identification and Extraction in Function Fields,” in Proc. of EuroVis, pp. 195—201, May 2007. [PDF] [BibTeX]
Abstract We present interactive techniques for identifying and extracting features in function fields. Function fields map points in n-dimensional Euclidean space to 1-dimensional scalar functions. Visual feature identification is accomplished by interactively rendering scalar distance fields, constructed by applying a function-space distance metric over the function field. Combining visual exploration with feature extraction queries, formulated as a set of function-space constraints, facilitates quantitative analysis and annotation. Numerous application domains give rise to function fields. We present results for two-dimensional hyperspectral images, and a simulated time-varying, three-dimensional air quality dataset.
John C. Anderson, Janine Bennett, and Kenneth I. Joy, “Marching Diamonds for Unstructured Meshes,” in Proc. of IEEE Visualization, pp. 423—429, Oct. 2005. [PDF] [BibTeX]
Abstract We present a higher-order approach to the extraction of isosurfaces from unstructured meshes. Existing methods use linear interpolation along each mesh edge to find isosurface intersections. In contrast, our method determines intersections by performing barycentric interpolation over diamonds formed by the tetrahedra incident to each edge. Our method produces smoother, more accurate isosurfaces. Additionally, interpolating over diamonds, rather than linearly interpolating edge endpoints, enables us to identify up to two isosurface intersections per edge. This paper details how our new technique extracts isopoints, and presents a simple connection strategy for forming a triangle mesh isosurface.