Prof. Dr. Hans Hagen:
March 1982: PhD (Mathematics), University of Dortmund
1983-1986: Assistant Professor at Arizona State University
1986-1988: Professor (C3), TU Braunschweig
since 1988: Professor (C4), University of Kaiserslautern
Hans Hagen is heading the research group for Computer Graphics and Computer Geometry at University of Kaiserslautern, Germany. He is both national and international a pioneer in his research domains geometric modeling and scientific visualization.
Prof. Dr. Subhrajit Guhathakurta:
1987: MCRP, Community and Regional Planning, Iowa State University, Ames, Iowa
1991: Ph. D., City and Regional Planning, University of California, Berkeley
1992-93: Visiting Assistant Professor of Community and Regional Planning, Iowa State University, Ames
1993-94: Research Associate, Center for Real Estate and Urban Economics and Institute of Urban and Regional Development, UC Berkeley
1994-2000: Assistant Professor, School of Planning and Landscape Architecture, Arizona State University
Mar-Jun 2000: Visiting Faculty, School of Geographical Sciences and Planning, University of Queensland, Brisbane, Australia
Oct 2000-Jan 2001: Visiting Faculty, Indian Institute of Information Technology, Bangalore, India
since 2000: Associate Professor, School of Planning and Landscape Architecture, Arizona State University
Prof. Guhathakurta has developed a keen interest in urban modeling since his involvement with Prof. John Landis at UC Berkeley and the California Urban Futures Modeling effort.
Prof. Dr.-Ing. Gerhard Steinebach:
1979: Received ‘Diploma Spatial and Environmental Planning’ (Dipl.-Ing. Raum- und Umweltplanung) at University of Kaiserslautern
1979-1987: Research Assistant, University of Kaiserslautern
1987: PhD (Dr.-Ing. Raum- und Umweltplanung), University of Kaiserslautern
1988-2000: Co-Founder and Managing Director of ‘Forschungs- und Informationsgesellschaft der Raum- und Umweltplanung (FIRU)’
1997-1999: Visiting Professor, University of Kaiserslautern
Since 1999: Professor (C4), University of Kaiserslautern
Gerhard Steinebach is a recognized expert in his research domains: ‘Urban ecology - focusing on the environmental impacts of development’, ‘Conversion of military and industrial brownfields’ and ‘Management of planning procedures’.
• Uncertainty visualization deals with uncertain data from simulations or sampled data, uncertainty due to the mathematical processes operating on the data, and uncertainty in the visual representation,
• Multifield visualization addresses the need to depict multiple data at individual locations and the combination of multiple datasets,
• Biomedical is a vast field with select subtopics addressed from scanning methodologies to structural applications to biological applications,
• Scalability in scientific visualization is critical as data grows and computational devices range from hand-held mobile devices to exascale computational platforms.
Scientific Visualization will be useful to practitioners of scientific visualization, students interested in both overview and advanced topics, and those interested in knowing more about the visualization process.
The first edition of Visualization in Medicine and Life Sciences (VMLS) emerged from a workshop convened to explore the significant data visualization challenges created by emerging technologies in the life sciences. The workshop and the book addressed questions of whether medical data visualization approaches can be devised or improved to meet these challenges, with the promise of ultimately being adopted by medical experts.
Visualization in Medicine and Life Sciences II follows the second international VMLS workshop, held in Bremerhaven, Germany, in July 2009. Internationally renowned experts from the visualization and driving application areas came together for this second workshop.
The book presents peer-reviewed research and survey papers which document and discuss the progress made, explore new approaches to data visualization, and assess new challenges and research directions.
The assembled papers span the frontiers of VMLS, examining these topics:
* Feature Extraction
* Volumes and Shapes
* Tensor Visualization
* Visualizing Genes, Proteins, and Molecules