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Klaus Mueller
Professor

Director, Visual Analytics and Imaging (VAI) Lab
Liaison, SUNY Korea CS Program

Center for Visual Computing
Computer Science Department
Stony Brook University - State University of New York

mueller{remove_this}@cs.stonybrook.edu

- News - News - News -

 

 
Activities at VIS 2016 in Baltimore:
  Insightful tutorial "Visual Analytics for High-Dimensional Data" given by S. Cheng and K. Mueller
  Controversial panel "On the Death of Scientific Visualization" with K. Mueller as speaker/participant (I don't think it's dead -- far from it)
 

Google Glass for Personalized Augmentations of Data Visualizations by S. Zhang, D. Coelho, and K. Mueller

  Extending Scatterplots to Scalar Fields by S. Cheng, P. Cui, and K. Mueller (won an Honorary Mention Award)
  A Data-Driven Approach for Mapping Multivariate Data to Color by S. Cheng, W. Xu. W. Zhong, and K. Mueller
  Analyzing Hillary Clinton’s Emails by V. Dehiya and K. Mueller
This IEEE TVCG paper won an Honorary Mention (1 of 2) Best Paper Award at VAST 2016:
  A Visual Analytics Approach for Categorical Joint Distribution Reconstruction from Marginal Projections by C. Xie, W. Zhong, K.Mueller
Gave a talk recently on How to Successfully Apply to US Universities.
  Curious how to do it? Click here.
Also, check out our recent medical imaging paper presented at the Intern' Conference on Image Formation in X-Ray Computed Tomography in Bamberg, Germany
  Efficient Area-Based Ray Integration Using Summed Area Tables and Regression Models by S. Ha. H. Li, and K. Mueller

Current or Recent Courses

 

 
CSE 332 Introduction to Visualization (undergraduate level)
CSE 377 Introduction to Medical Imaging (undergraduate level)
CSE 323 Human Computer Interaction (co-taught at graduate level)
CSE 564 Visualization and Visual Analytics
CSE 590 Data Science Fundamentals (graduate level)
CSE 591 GPU Programming (Special Topics course)
CSE 577 Medical Imaging (graduate level)
CSE 523 Master's Projects (continued as CSE 524)
CSE 648 Visual Analytics Seminar (every semester)
More... Complete set of courses
   
Research

My areas of interest are visualization, visual analytics,data science, big data, human-computer interaction, medical imaging, computer graphics, viirtual and augmented reality, and high performance computing on GPUs. 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).

 

Medical imaging. Here we use programmable commodity graphics hardware boards (GPUs) to accelerate a wide variety of 3D computer tomographic (CT) reconstruction algorithms. So far we have achieved speed-ups of 1-2 orders of magnitude, without significant loss in reconstruction quality. Our RapidCT system enables fast, accelerated 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 iterative 3D reconstruction from data acquired with X-ray CT scanners at low radiation doses (known as low-dose CT), with transmission ultrasound for breast mammography, data obtained from MV-CT and proton-CT scanners for the treatment of cancer, projections obtained via mobile X-ray source/detector pairs, as well as functional imaging applications, including MRI, functional MRI, SPECT, and PET.


     

 

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 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.

     
  Color, texture, details, points. Current projects include the example-based colorization of images and volumes, rule-based (expert) systems for color design, 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.
     

  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 the analysis/classification of large, high-dimensional data streams and for the interactive specification of data models, a paradigm we call model-drive visual analytics. In another application 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.
     

 

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 a 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 GPU 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.

     
  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.
   

Also known as Klaus Müller (German spelling)
http://www3.cs.stonybrook.edu/~mueller