Title of the Talk 

Computational Modeling and Visualization in the Biological Sciences

 Speaker 

 :

Chandrajit Bajaj

Department of Computer Science,
Institute of Computational Engineering & Sciences,
The University of Texas at Austin.

Chandrajit Bajaj is the director of the Center for Computational Visualization, in the Institute for Computational and Engineering Sciences (ICES) and a Professor of Computer Sciences at the University of Texas at Austin. Bajaj holds the Computational Applied Mathematics Chair in Visualization. He is also an affiliate faculty member of Mathematics, Electrical and Bio-medical Engineering, Neurosciences, and a fellow of the Institute of Cell and Molecular Biology. He is on the editorial boards for the International Journal of Computational Geometry and Applications, the ACM Computing Surveys, and the SIAM Journal on Imaging Sciences. He is a fellow of the American Association for the Advancement of Science (AAAS), and a fellow of the Association of Computing Machinery (ACM).

Abstract

:

Discoveries in computational molecular – cell biology and bioinformatics promise to provide new therapeutic interventions to disease. With the rapid growth of sequence and structural information for thousands of proteins,and hundreds of cell types computational processingare a restricting factor in obtaining quantitative understanding of molecular-cellular function. Processing and analysis is necessary both for input data (often from imaging) and simulation results. To make biological conclusions, this data must be input to and combined with results from computational analysis and simulations. Furthermore, as parallelism is increasingly prevalent, utilizing the available processing power is essential to development of scalable solutions needed for realistic scientific inquiry. However, complex image processing and even simulations performed on large clusters, multi-core CPU, GPU-type parallelization means that naïve cache unaware algorithms may not efficiently utilize available hardware. Future gains thus require improvements to a core suite of algorithms underpinning the data processing, simulation, optimization and visualization needed for scientific discovery. In this talk, I shall highlight current progress on these algorithms as well as provide several challenges for the scientific community.