Timothy Luciani

A clustering method for identifying regions of interest in turbulent combustion tensor fields

Production of electricity and propulsion systems involve turbulent combustion. Computational modeling of turbulent combustion can improve the efficiency of these processes. However, large tensor datasets are the result of such simulations; these datasets are difficult to visualize and analyze. In this work we present an unsupervised statistical approach for the segmentation, visualization and potentially the tracking of regions of interest in large tensor data. The approach employs a machine learning clustering algorithm to locate and identify areas of interest based on specified parameters such as strain tensor value. Evaluation on two combustion datasets shows this approach can assist in the visual analysis of the combustion tensor field.

FixingTIM: interactive exploration of sequence and structural data to identify functional mutations in protein families

BackgroundKnowledge of the 3D structure and functionality of proteins can lead to insight into the associated cellular processes, speed up the creation of pharmaceutical products, and develop drugs that are more effective in combating disease.MethodsWe present the design and implementation of a visual mining and analysis tool to help identify protein mutations across a family of structural models and to help discover the effect of these mutations on protein function. We integrate 3D structure and sequence information in a common visual interface; multiple linked views and a computational backbone allow comparison at the molecular and atomic levels, while a novel trend-image visual abstraction allows for the sorting and mining of large collections of sequences and of their residues.ResultsWe evaluate our approach on the triosephosphate isomerase (TIM) family structural models and sequence data and show that our tool provides an effective, scalable way to navigate a family of proteins, as well as a means to inspect the structure and sequence of individual proteins.ConclusionsThe TIM application shows that our tool can assist in the navigation of families of proteins, as well as in the exploration of individual protein structures. In conjunction with domain expert knowledge, this interactive tool can help provide biophysical insight into why specific mutations affect function and potentially suggest additional modifications to the protein that could be used to rescue functionality.

PRODIGEN: Visualizing the probability landscape of stochastic gene regulatory networks in state and time space

BackgroundVisualizing the complex probability landscape of stochastic gene regulatory networks can further biologists’ understanding of phenotypic behavior associated with specific genes.ResultsWe present PRODIGEN (PRObability DIstribution of GEne Networks), a web-based visual analysis tool for the systematic exploration of probability distributions over simulation time and state space in such networks. PRODIGEN was designed in collaboration with bioinformaticians who research stochastic gene networks. The analysis tool combines in a novel way existing, expanded, and new visual encodings to capture the time-varying characteristics of probability distributions: spaghetti plots over one dimensional projection, heatmaps of distributions over 2D projections, enhanced with overlaid time curves to display temporal changes, and novel individual glyphs of state information corresponding to particular peaks.ConclusionsWe demonstrate the effectiveness of the tool through two case studies on the computed probabilistic landscape of a gene regulatory network and of a toggle-switch network. Domain expert feedback indicates that our visual approach can help biologists: 1) visualize probabilities of stable states, 2) explore the temporal probability distributions, and 3) discover small peaks in the probability landscape that have potential relation to specific diseases.

Large-Scale Overlays and Trends: Visually Mining, Panning and Zoomingthe Observable Universe

We introduce a web-based computing infrastructure to assist the visual integration, mining and interactive navigation of large-scale astronomy observations. Following an analysis of the application domain, we design a client-server architecture to fetch distributed image data and to partition local data into a spatial index structure that allows prefix-matching of spatial objects. In conjunction with hardware-accelerated pixel-based overlays and an online cross-registration pipeline, this approach allows the fetching, displaying, panning and zooming of gigabit panoramas of the sky in real time. To further facilitate the integration and mining of spatial and non-spatial data, we introduce interactive trend images-compact visual representations for identifying outlier objects and for studying trends within large collections of spatial objects of a given class. In a demonstration, images from three sky surveys (SDSS, FIRST and simulated LSST results) are cross-registered and integrated as overlays, allowing cross-spectrum analysis of astronomy observations. Trend images are interactively generated from catalog data and used to visually mine astronomy observations of similar type. The front-end of the infrastructure uses the web technologies WebGL and HTML5 to enable cross-platform, web-based functionality. Our approach attains interactive rendering framerates; its power and flexibility enables it to serve the needs of the astronomy community. Evaluation on three case studies, as well as feedback from domain experts emphasize the benefits of this visual approach to the observational astronomy field; and its potential benefits to large scale geospatial visualization in general.

Pixel-based overlays for navigating a galaxy of observations

These preliminary results show that pixel-based overlays have the potential to generate scalable, graphical representations of astronomy data. This approach may allow us to overcome bandwidth and screen-space current limitations in astronomy database visualization by following a WebGL - PHP client-server architecture. The advantages of this approach are its versatility and visual scalability (to the pixel level), enabling the visual analysis of large datasets. The resulting versatility allows for flexible control over the visualization and the client-side scripts. Accessing graphics hardware through WebGL further provides the users with a rich, graphics-accelerated web experience. Preliminary feedback from astronomy researchers emphasizes the benefits of visual analysis to this field.

A deep learning approach to identifying shock locations in turbulent combustion tensor fields

We introduce a deep learning approach for the identification of shock locations in large scale tensor field datasets. Such datasets are typically generated by turbulent combustion simulations. In this proof of concept approach, we use deep learning to learn mappings from strain tensors to Schlieren images which serve as labels. The use of neural networks allows for the Schlieren values to be approximated more efficiently than calculating the values from the density gradient. In addition, we show that this approach can be used to predict the Schlieren values for both two-dimensional and three-dimensional tensor fields, potentially allowing for anomaly detection in tensor flows. Results on two shock example datasets show that this approach can assist in the extraction of features from reacting flow tensor fields.

Panning and zooming the observable universe with prefix-matching indices and pixel-based overlays

We introduce a web-based, client-server computing infrastructure to assist the interactive navigation of large-scale astronomy observations. Large image datasets are partitioned into a spatial index structure that allows prefix-matching of spatial objects. In conjunction with pixel-based overlays, this approach allows fetching, displaying, panning and zooming of gigabit panoramas of the sky in real time. Images from three sky surveys (SDSS, FIRST and simulated LSST results) are cross-registered and integrated as overlays, allowing cross-spectrum analysis of astronomy observations. The front-end of the infrastructure uses the web technologies We-bGL and HTML5 to enable cross-platform, web-based functionality. Our approach attains interactive rendering framerates; its power and flexibility enables us to serve the needs of the astronomy community. Evaluation on a galaxy case study, as well as feedback from domain experts emphasize the benefits of this visual approach to the observational astronomy field.