Stanford University
Showing 61-70 of 1,540 Results
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Jonathan Abel
Affiliate, Music
BioJonathan S. Abel is a Consulting Professor at the Center for Computer Research in Music and Acoustics (CCRMA) in the Music Department at Stanford University, working in music and audio applications of signal and array processing, parameter estimation and acoustics. He is also a co-founder of Seismic Innovations, LLC, and Seismic Services, LLC, companies specializing in microseismic signal processing. Abel was a co-founder and Chief Technology Officer of the GRAMMY Award winning Universal Audio, Inc. He was previously a researcher at NASA/Ames Research Center, Chief Scientist of Crystal River Engineering, Inc., and a Lecturer in the Department of Electrical Engineering at Yale University. As an industry consultant, Abel has worked with Apple, Dolby, FDNY, LSI Logic, L3 Technologies, LR Baggs, Native Instruments, SAIC, Sennheiser, Sigma Cubed, Triple Ring, and the U.S. NRL on projects in professional audio, GPS, fire department siting and deployment, medical imaging, room acoustics measurement, audio effects processing, passive sonar, and microsiesmic signal processing. He holds Ph.D. and M.S. degrees from Stanford University, and an S.B. from MIT, all in electrical engineering. Abel is a Fellow of the Audio Engineering Society for contributions to audio effects processing.
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Tom Abel
Professor of Particle Physics and Astrophysics and of Physics
BioWhat were the first objects that formed in the Universe, what is it made of, how does it work? Prof. Abel's group explores all of cosmic history using ab initio supercomputer calculations. He has shown from first principles that the very first luminous objects are very massive stars and has developed novel numerical algorithms using adaptive-mesh-refinement simulations that capture over 14 orders of magnitude in length and time scales. He has shown how the first stars galaxies form and affect everything that follows later. He has been pioneering novel numerical algorithms to study collisionless fluids such as dark matter as well as astrophysical and terrestrial plasmas. He has designed bespoke summary statistics to have interpretable, robust, efficient, summary statistics to describe spatial clustering based on fast nearest neighbor searches. His recent work is on creating digital twins of astronomical objects and the Universe as a whole in the context of the Center for Decoding the Universe. This Center leverages advances in machine learning and artificial intelligence to make sense of our Universe. He was the director of the Kavli Institute for Particle Astrophysics and Cosmology and Division Director at SLAC 2013-2018.
