CAREER: An Integrated Framework for Data-Adaptive Representations and Algorithms in Visual Computing

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This is the first year funding of a four-year continuing award. The goal of this CAREER project is to develop the theoretical framework, algorithms, and computational tools for improved data representation, analysis, and visualization. To that end, we propose to develop data-adaptive algorithms for both low-level signal processing tasks (e.g., interpolation) and high-level data interrogation tasks. For the high-level tasks, we propose to develop hybrid representations for volumetric data (multiresolution volumetric deformable models) that will be useful for both detection and simulation. The proposed representations and algorithms will be tested on 3D MR data (breast cancer detection and surgery planning) and seismic data (detection of sequence stratigraphy derived objects). The educational component seeks to address some important challenges in the effective teaching and training of students in the Visual Computing field. In particular, the educational goals of this CAREER plan are the following: (1) to promote an interdisciplinary Visual Computing education at the University of Houston by fostering cross-departmental teaching activities, (ii) to provide a practitioner's perspective in Visual Computing education and to address "real-world" problems in the Visual Computing Curriculum, and (iv) to attract incoming students into Visual Computing and to motivate high-school students towards the sciences. A project on a prototype minimal invasive heart surgery simulator will be used as a demonstration paradigm for the interdisciplinary teaching of Visual Computing. Domain expertise and domain data will be provided through close collaborations with the M.D. Anderson Cancer Center and Veritas Exploration Services.