Institute for Computational and Mathematical Engineering (ICME)
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Canon USA Professor in the School of Engineering and Professor of Electrical Engineering
BioProfessor Hanrahan's current research involves rendering algorithms, high performance graphics architectures, and systems support for graphical interaction. He also has worked on raster graphics systems, computer animation and modeling and scientific visualization, in particular, volume rendering.
The Cecil H. and Ida M. Green Professor in Geophysics
Current Research and Scholarly InterestsBiographical Information
Jerry M. Harris is the Cecil and Ida Green Professor of Geophysics and Associate Dean for the Office of Multicultural Affairs. He joined Stanford in 1988 following 11 years in private industry. He served five years as Geophysics department chair, was the Founding Director of the Stanford Center for Computational Earth and Environmental Science (CEES), and co-launched Stanford's Global Climate and Energy Project (GCEP). Graduates from Jerry's research group, the Stanford Wave Physics Lab, work in private industry, government labs, and universities.
My research interests address the physics and dynamics of seismic and electromagnetic waves in complex media. My approach to these problems includes theory, numerical simulation, laboratory methods, and the analysis of field data. My group, collectively known as the Stanford Wave Physics Laboratory, specializes on high frequency borehole methods and low frequency labratory methods. We apply this research to the characterization and monitoring of petroleum and CO2 storage reservoirs.
I teach courses on waves phenomena for borehole geophysics and tomography. I recently introduced and co-taught a new course on computational geosciences.
I was the First Vice President of the Society of Exploration Geophysicists in 2003-04, and have served as the Distinguished Lecturer for the SPE, SEG, and AAPG.
John A. Overdeck Professor, Professor of Statistics and of Biomedical Data Sciences
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.
Ryan Alexander Humble
Ph.D. Student in Computational and Mathematical Engineering, admitted Autumn 2018
Current Research and Scholarly InterestsNumerical methods for solving physical systems, Uncertainty quantification, High performance computing