Vice Provost and Dean of Research
Showing 31-40 of 150 Results
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Odette Harris, MD, MPH
Paralyzed Veterans of America Professor of Spinal Cord Injury Medicine
Current Research and Scholarly InterestsTraumatic brain injury with a focus on epidemiology and outcomes.
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Bard Harstad
David S. Lobel Professor in Business and Sustainability, Professor of Environmental Social Sciences, Senior Fellow at the Stanford Institute for Economic Policy Research and Professor, by courtesy, of Economics
BioWith a PhD from Stockholm University, Harstad taught at Kellogg School of Management, Northwestern University, 2004-2012, and then at the University of Oslo 2012-2023, before joining the GSB in 2023. His fields include political economics, environmental economics, and applied theory. Specific research projects include the design of international agreements, trade agreements and climate agreements, supply-side environmental policies, and policies that motivate environmental conservation and reducing deforestation.
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Sean Hartnoll
Principal Investigator, Stanford Institute for Materials and Energy Sciences
BioI am a theorist working on problems in gravitational, high energy and condensed matter physics. In recent years the holographic correspondence, the physics of quantum entanglement and quantum field theory more generally have led to strong connections between central concerns in these different fields.
For example, I am interested in understanding the emergence of spacetime from large N matrix quantum mechanics models. These can be thought of as the simplest models of holographic duality, and will likely hold the key to understanding the emergence of local physics as well as black holes. The most basic object in these theories is the ground state wavefunction. Understanding this wavefunction is a many-body problem and I am interested in using modern ideas from condensed matter theory -- such as topological order -- to characterize it.
Another example has to do with dissipation. How quickly can a quantum mechanical system thermalize itself? From this perspective, there are remarkable similarities between strongly quantum mechanical systems such as the quark-gluon plasma and high temperature superconductors and the dynamics of black holes in classical gravity. This may suggest that a fundamental limitation imposed by quantum statistical mechanics is at work in these systems. I have pursued this possibility from many angles, including variational principles for entropy production, the Lieb-Robinson bound on velocities in quantum systems and bounds on the magnitude of quantum fluctuations near thermal equilibrium.
In parallel to a ''bird's eye'' approach to quantum statistical mechanics, I am also increasingly interested in specific scattering mechanisms in unconventional materials that may give a relatively simple explanation of transport behavior that has otherwise been considered anomalous --- using this approach my collaborators and I have 'demystified' aspects of transport in quantum critical ruthenate materials. I am currently interested, for example, in the role of phonons in strongly correlated electronic systems.
I have recently worked on black hole interiors in classical gravity. Black hole interiors are extremely rich mathematically, but their physical interpretation -- for example in a holographic context -- remains obscure. To start to address this question I have shown how important dynamics of the interior, such as the instability of the singularity and of Cauchy horizons, can be triggered in a relatively simple holographic setting.
Lists of my publications and of recorded talks and lectures can be found following the links on the right. -
Trevor Hastie
John A. Overdeck Professor, Professor of Statistics and of Biomedical Data Sciences, Emeritus
Current Research and Scholarly InterestsFlexible statistical modeling for prediction and representation of data arising in biology, medicine, science or industry. Statistical and machine learning tools have gained importance over the years. Part of Hastie's work has been to bridge the gap between traditional statistical methodology and the achievements made in machine learning.
