Earth Imaging & Machine Learning
EES XXX (Fall, 24)
Computational imaging is a rapidly growing field that borrows from computer vision & machine learning. We explore computational methods for Earth imaging under two broad headings: the classical & modern framework. The latter surveys deep neural networks, e.g, convolutional neural networks & generative adversarial networks. In class exercises and projects will be team competitions to solve blind-imaging problems.
Seismic Signals & Noise
EES 225/ECE 248 (Spring, '25)
Research frontiers in earth imaging & quake detection require extraction of seismic signals buried in noise. Both are vibrations generated in the solid earth or its coupling with the atmosphere and oceans. Topics covered include fourier analysis, z-transforms, poles-zeros and instrument design. In-class exercises include applications in geotechnical engineering as well as forensic, exploration, glacial, submarine and planetary seismology.
EES 211 (Spring, 25)
We live on a dynamic planet. The tranquil, unchanging landscape of the Earth’s surface is often interrupted by abrupt, catastrophic events. Earthquakes and tsunamis lay waste to buildings and entire cities. Dormant volcanoes come to life in explosions of lava and gases, with implications for climate change. In this course, we learn how these hazards are violent manifestations of plate tectonics.
Earth Science Data Analysis
EES 214 (TBD)
A course on modern methods for estimating models, and their uncertainty, from observational data in the Earth sciences. The course emphasizes concepts in parameter estimation, time series analysis, and statistics, using matrix inverse methods. Problem sets and weekly computer exercises provide theoretical foundations and adequate practice needed for proficiency in data analysis.
Stochastic Methods in Geophysics
EES 410 (TBD)
Seminar on computational methods for making inferences from inaccurate, incomplete, or inconsistent geophysical data, e.g., nonlinear least-squares, local, and global optimization methods. Readings emphasize applied Monte Carlo (MC) methods and their uses: data fitting, inversion, optimization, and Bayesian sampling.
Seismology & Earth Structure
EES 215 (TBD)
This course introduces the theoretical foundations of seismology: the study of Earth’s elastic vibrations, the sources that generate them, and the structures through which they propagate. The course will cover fundamental concepts of elasticity, stress, strain, seismometer design, the derivation of the seismic wave equation, and its application to describing the full seismic spectrum.
My teaching emphasizes the creation of memorable learning experiences and the inculcation of scientific attitudes and skills. This mirrors the primary role of the University, which is the creation and transfer of scientific knowledge from one generation to the other (see link below for more). To set up an appointment, see my calendar in G+ link below.