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Klaus Mueller Stony Brook, NY 11794-4400 |
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Teaching
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| CSE 332 | Introduction to Visualization (undergraduate level) |
| CSE 377 | Introduction to Medical Imaging (undergraduate level) |
| CSE 564 | Visualization |
| CSE 612 | Advanced Visualization |
| CSE 591 | Visual Analytics (Special Topics course) |
| CSE 594 | Medical Imaging (Special Topics course) |
| CSE 690 | GPGPU: General Purpose Computing on Programmable Graphics Hardware |
| CSE 523 | Master's Projects (continued as CSE 524) |
| ITS 102 | Topics in Information Technology Studies |
| CSE 648 | Volume Graphics and Visualization Seminar (every semester) |
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Research
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My areas of interest are visualization, computer graphics, projector-based graphics, augmented reality, virtual reality, medical imaging, face recognition, GPU-acceleration of general purpose computing, visual data mining, and functional brain analysis. I have a BS in Electrical Engineering from the Polytechnic University of Ulm, Germany, and an MS in Biomedical Engineering and a PhD in Computer Science, both from The Ohio State University. Apart from my appointment at the Computer Science department at Stony Brook University, I also hold adjunct faculty positions at the Biomedical Engineering Department and the Radiology Department, and I am an adjunct scientist at the Computational Science Center at Brookhaven National Laboratory. My research is sponsored by NSF (including the Career award in 2001), NIH, DOE, DHS, and private industry and research labs.
Here's a brief overview on some of the projects. For more info, visit the corresponding linked project pages (also accessible from this shortcut page).
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Medical imaging. Some current projects include the use of programmable commodity graphics hardware to accelerate a wide variety of 3D tomographic reconstruction algorithms. So far we have achieved speedups of 1-2 orders of magnitude, without significant losses in reconstruction quality. Our approach enables rapid 3D reconstruction in diagnostic imaging, radio-therapy applications, surgery planning, electron microscopy, and others, at a fraction of the cost of proprietary devices. Related projects are the 3D reconstruction from transmission ultrasound, MV-CT and proton-CT data, projections obtained via mobile X-ray source/detector pairs, and functional imaging applications, including MRI, functional MRI, SPECT, and PET. |
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Scientific,
medical, and information visualization. This area embraces a wide
gamut of projects. In volume
visualization, we are concerned with algorithms and techniques for
volume rendering (point-based, also known as splatting, and ray-based)
on regular as well as irregular grids, fundamental
research on interpolation filters and data grids, GPU accelerated
rendering, rendering of multi-modal and time-varying datasets,
feature-centric and illustrative visualization, intuitive user
interfaces, modeling with volumetric datasets (examples: ablation,
melting), volume graphics and rendering with volumetric effects
(examples: radiosity, shadows), image-based volume rendering, and
others. In information
visualization, we are working on the development of frameworks for
the visualization of large, high-dimensional, multi-valued datasets. |
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Color, texture, details, points. Current projects include the example-based colorization of images and volumes, semantic and infinite zooms enabled by texture synthesis, size- and angle-preserving texturing of arbitrary objects, and point-based surface rendering (adding special effects, such as motion blur/hints), and interactive fly-throughs of realistic, large-scale urban environments (virtual Manhattan) at high-levels of detail (less than an inch resolution). These topics are also relevant in the context of illustrative (expressive) data and volume visualization. | |||
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Visual analytics. Information visualization techniques can be combined with classical and modern data analysis, such as intelligent computing, statistical pattern recognition and machine learning, to yield a more powerful, user-controlled information retrieval. The visual feedback guiding the analytical mining process exploits the unmatched capability of the low-level and high-level human visual system to recognize patterns and derive abstract conclusions from them, possibly setting off another analysis round. We are currently exploiting this relatively new paradigm for volume segmentation and for the analysis/classification of large, high-dimensional data streams. We are also working towards a comprehensive visual data mining environment for neuroscientists, called BrainMiner, that will enable a more targeted and experiential derivation of brain functional models from large collections of knowledge and data. | |||
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Modeling of natural phenomena. We have developed a comprehensive framework for the modeling and simulation of smoke, fire, and general gaseous phenomena, both on surfaces and in 3D space, interacting with static and moving objects. Our approach uses the Lattice-Boltzmann Model (LBM) for fast propagation of these phenomena. The LBM is non-iterative and uses only local operations per grid update of the transient phenomena. It is therefore very attractive for acceleration on GPUs. Current applications are the modeling of gaseous substances in urban (homeland) security scenarios, the simulation of complex heat-originated phenomena for computer graphics, such as heat shimmering, melting, ablation, and smoke, and the simulation of other amorphous phenomena. General purpose computing on programmable graphics hardware (GPGPU). This is topic related to the above, but not confined to it. We have been using GPUs for various costly numerical simulation tasks (in addition to our acceleration of medical imaging algorithms), with speedups of 1-2 orders of magnitudes, while being able to use the GPU also to quickly visualize the (intermediate) results. This gives rise to the notion of visual simulation. |
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Face recognition. We are currently developing a face recognition technique that tracks small detail in a deforming face (for example, a smile) to derive dynamic information that turns out to be very salient for face recognition. Using this technique, we have been able to distinguish even identical twins. | |||
