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Professor Silverstein conducts research in theoretical physics, focusing particularly on gravitation and cosmology. She develops new methods and applications in machine learning to explore fundamental interactions in gravitational and particle physics, questioning mechanisms behind the initial seeds of the universe's structure. Her work employs cosmological observations within a holographic framework to apply historical contexts to the universe, addressing emergent effects around horizons and singularities. Silverstein rigorously examines basic problems in theoretical physics, including concrete, testable mechanisms for cosmic inflation that account for high-energy physics sensitivity. She has significantly contributed to the field through systematic analyses of observable phenomenology, especially in relation to cosmic microwave background research. Her interests extend to developing mechanisms for stabilizing extra dimensions in string theory, addressing quantum gravity, and understanding the singularity resolutions of black hole physics. Silverstein's areas of focus encompass optimization algorithms derived from physical dynamics, UV-complete mechanisms for cosmic inflation, non-Gaussianity in quantum field theory, and holographic models for de Sitter expansion in string theory.
The Computer Science department emphasizes research potential. GRE General is currently optional but recommended for some tracks.