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Professor Silverstein conducts research in theoretical physics, particularly in gravitation and cosmology. She develops new methods and applications of machine learning in this field. Her work includes investigating the fundamental degrees of freedom and interactions underlying gravitational particle physics, examining mechanisms for the initial seeds of structure in the universe, and utilizing cosmological observations to test these theories. She explores the holographic framework for cosmology and accounts for emergent effects, horizons, and singularities. Professor Silverstein's research addresses critical problems in theoretical physics, offering concrete mechanisms for cosmic inflation that consider sensitivity to high-energy physics. She has contributed significantly to cosmic microwave background research and has analyzed observable phenomenology systematically. Her investigations touch upon stabilizing extra dimensions in string theory and methods to tackle questions surrounding quantum gravity and singularity resolution. Her focus areas include optimization algorithms derived from physical dynamics, UV complete mechanisms for cosmic inflation, long-range interactions in black hole physics, and holographic models aimed at enhancing the AdS/CFT correspondence in cosmology. Additionally, she explores mechanisms by which extra degrees of freedom in string theory can induce transitions related to duality symmetries in spaces of varying topology and dimensionality.
Stanford University, Department of Physics • Stanford, California
Conducts research in theoretical physics, focusing on gravitation and cosmology.
Stanford University, SLAC Physics Department • Stanford, California
Taught and conducted research in physics.
Stanford Linear Accelerator Center, Stanford University • Stanford, California
Engaged in research and education in theoretical physics.
Rutgers University • New Brunswick, New Jersey
Conducted research in theoretical physics.
The Computer Science department emphasizes research potential. GRE General is currently optional but recommended for some tracks.