Winner of the Richard P. Feynman Prize for Excellence in Teaching Jehoshua "Shuki" Bruck , Caltech's Gordon and Betty Moore Professor of Computation and Neural Systems and Electrical Engineering, talks about the art and science of sharing information.
Read More ... 12.18.09
Hareem T. Maune, a graduate student studying carbon nanotube physics, and Si-ping Han, a graduate student investigating the interactions between carbon nanotubes have developed DNA origami nanoscale breadboards for carbon nanotube circuits. "This collaborative research project is evidence of how we at Caltech select the top students in science and engineering and place them in an environment where their creativity and imagination can thrive," says Ares Rosakis, chair of the Division of Engineering and Applied Science at Caltech and Theodore von Kármán Professor of Aeronautics and Professor of Mechanical Engineering. The work of these students was supervised by: Erik Winfree, Associate Professor of Computer Science, Computation and Neural Systems, and Bioengineering; William A. Goddard III, Charles and Mary Ferkel Professor of Chemistry, Materials Science, and Applied Physics; Paul W.K. Rothemund, Senior Research Associate, and Marc Bockrath, Associate Professor of Physics at University of California Riverside. Read More... 11.10.2009
David MacKay (CNS Phd '92), Professor in the Department of Physics at
Cambridge University and author of the influential book Sustainable
Energy - Without the Hot Air has been appointed Chief Scientific Advisor to
the Department of Energy and Climate Change, UK. He is internationally known
for his research in machine learning, information theory, and communication
systems, including the invention of Dasher, a software interface that
enables efficient communication in any language with any muscle. He has
taught Physics in Cambridge since 1995. Since 2005, he has devoted
increasing amounts of time to public teaching about energy. Read More... 09.21.2009
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How does the brain compute? Can we endow machines with brain-like computational capability? Faculty and students in the CNS program ask these questions with the goal of understanding the brain and designing systems that show the same degree of autonomy and adaptability as biological systems. Disciplines such as neurobiology, electrical engineering, computer science, physics, statistical machine learning, control and dynamical systems analysis, and psychophysics contribute to this understanding. The unifying theme is the relationship between the physical structure of a computational system (molecular, neuronal, or electronic hardware), the dynamics of its operation, and the computational problems that it can efficiently solve.
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