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Thermodynamics of Computation

Difference between revisions of "Sadasivan Shankar"

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|Related link title=Carnot’s Engine, Bernoulli’s Pump, and Turing’s Machine - Some clues to realization of ideal computing
 
|Related link title=Carnot’s Engine, Bernoulli’s Pump, and Turing’s Machine - Some clues to realization of ideal computing
 
|Related link URL=https://ee.stanford.edu/event/seminar/carnot%E2%80%99s-engine-bernoulli%E2%80%99s-pump-and-turing%E2%80%99s-machine-some-clues-realization-ideal
 
|Related link URL=https://ee.stanford.edu/event/seminar/carnot%E2%80%99s-engine-bernoulli%E2%80%99s-pump-and-turing%E2%80%99s-machine-some-clues-realization-ideal
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|Related link title=Computation from Devices to System Level Thermodynamics
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|Related link URL=http://ecst.ecsdl.org/content/25/7/421.abstract
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|Related link title=Die Stacking (3D) Microarchitecture
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Revision as of 22:20, October 10, 2018

Biography: Sadasivan Shankar is an Associate in the Harvard School of Engineering and Applied Sciences, and was the first Margaret and Will Hearst Visiting Lecturer in Harvard University. He has co-instructed several graduate-level classes on Computational Materials Design, Extreme Computing for Real Applications, and Mitigating Toxicity by Materials Design. He is involved in research in the areas of materials, chemistry, multi-scale and nonequilibrium methods, and large-scale computational methods. Dr. Shankar earned his Ph.D. in Chemical Engineering and Materials Science from University of Minnesota, Minneapolis. He is a co-inventor in over twenty patent filings covering areas in new chemical reactor designs, semiconductor processes, bulk and nano materials, device structures, and algorithms. He is also a co-author in over hundred publications and presentations in measurements, multi-scale and multi-physics methods spanning from quantum scale to macroscopic scales, in the areas of chemical synthesis, plasma chemistry and processing, non-equilibrium electronic, ionic, and atomic transport, energy efficiency of information processing, and machine learning methods for bridging across scales, and estimating complex materials properties and in process control.

Field(s) of Research: Chemical Reaction Networks, General Non-equilibrium Statistical Physics, Logically Reversible Computing

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