MATH + X Symposium on Data Science and Inverse Problems in Geophysics

Rice University, January 24-26, 2018

Department of Computational and Applied Mathematics
Department of Earth, Environmental and Planetary Sciences

George R. Brown School of Engineering

Venue: Biosciences Research Collaborative Building (BRC), Room 280, 6500 S. Main St.

Parking available onsite. View all venue locations

Symposium registration is now full. If you wish to be placed on a waitlist, or to receive information about online availability of lectures, please email

Harnessing enormous amounts of data collected by sensors of different types is at the cusp of revolutionizing the earth sciences. The 2018 symposium will focus on the cross fertilization between the analysis of inverse problems, data science and machine learning in geophysics. Learning approaches have revolutionized machine vision, speech recognition, and a host of other domains. However, the development of machine learning algorithms for geophysical problems remains under-explored. The classical way of analyzing sensor data relies directly on partial differential equations, dynamical systems, etc, that are derived from the physics of the sensing scenario, that is, via inverse problems. Over the past decade, however, new machine learning techniques for interrogating data have been proposed and developed that bypass physics and learn models directly from data. The symposium will seek synergies between these points of view.

Deep learning is emerging as an important approach to glean information about processes in Earth’s interior. The cross fertilization between the analysis of inverse problems and machine learning is essentially still wide open, and new models in machine learning put forward exciting opportunities. Beyond their ability to capture, for example, multi-scale geometric aspects of the data, it has been shown that architectures such as deep convolutional networks can approximate certain inverse mappings. Contextual LSTM models show promise in learning ambient noise recordings. Notions of Gaussianization and manifold learning appear regularly in the discussion, while an important role seems to be played by the invariance properties of these models as well as their (unprecedented) ability to encode scale interactions, and the metric. A significant contribution to the representational power of deep networks comes from nonlinearities which give rise to non-convex regularizers, calling in turn for new ideas in optimization. Moreover a tight connection between inverse problems and data analysis leads to strategies for adaptive acquisition based on learning.

This workshop will bring together geophysicists, mathematicians, computer scientists and statisticians to present the state-of-the-art on the one hand, and emerging cross-disciplinary directions of research on the other hand, while enabling new collaborations.

Wednesday, Jan 24 (BRC Room 280):
7:30 am 8:15 am Registration and light breakfast
7:45 am Shuttle pickup (speakers)
8:15 am 12:15 pm Technical Sessions
12:15 pm 1:15 pm Lunch (provided)
1:15 pm 5:00 pm Technical Sessions
5:15 pm 5:30 pm Shuttle pickup
6:00 pm 7:30 pm Reception (Cohen House)
Thursday, Jan 25 (BRC Room 280):
8:00 am 8:30 am Registration and light breakfast
8:00 am Shuttle pickup (speakers)
8:30 am 12:15 pm Technical Sessions
12:15 pm 1:15 pm Lunch (provided)
1:15 pm 5:00 pm Technical Sessions
5:15 pm Shuttle pickup
6:30 pm 9:00 pm Invited Speakers Dinner
Friday, Jan 26 (BRC Room 280):
8:00 am 8:30 am Registration and light breakfast
8:00 am Shuttle pickup (speakers)
8:30 am 12:15 pm Technical Sessions
12:15 pm 1:15 pm Lunch (provided)
1:15 pm 5:00 pm Technical Sessions
5:00 pm Meeting Close
5:15 pm Shuttle pickup

Registration is required for all events

Invited Speakers:

Data Science, Machine Learning

R. Baraniuk (Rice University)
A. Bertozzi (UCLA)
J. Bruna (New York University)
A. Dimakis (UT Austin)
I. Dokmanić (University of Illinois at UC)
N. Kutz (University of Washington)
S. Mallat (Collège de France)
M. McCann (École Polytechnique Fédérale de Lausanne)
A. Moitra (MIT)
E. Mossel (MIT)
R. Nowak (Wisconsin-Madison)
C. Schönlieb (University of Cambridge)
A. Stuart (CalTech)

Inverse Problems, Geophysics

G. Beroza (Stanford University)
M. Campillo (Université Grenoble-Alpes)
P. Hintz (UC Berkeley)
J. Ilmavirta (Unversity of Jyväskylä)
P. Johnson (Los Alamos National Lab)
M. Lassas (University of Helsinki)
L. Oksanen (University College of London)
V. Pankratius (MIT Haystack Observatory)
F. Simons (Princeton University)
A. Vasy (Stanford University)
Y. Wang (University of Washington and HKUST)

Travel Information:

Hotel: Guests are invited to stay at the nearby
Hilton Houston Plaza, 6633 Travis Street.
Airport Transport: SuperShuttle (~$30), Uber (~$45),
and taxi (~$75) are all options.
Venue Parking: is available in the BRC building, rate $12/day.

funded by
simons foundation

MATH + X Program

This workshop is funded by the Simons Foundation under the MATH + X program: encouraging novel collaborations between mathematics and other disciplines in science and engineering.

2017 Math + X Symposium on Seismology and Inverse Problems

Organizing committee:

Maarten de Hoop (Rice University)
Richard Baraniuk (Rice University)
Gunther Uhlmann (University of Washington and HKUST)
Robert van der Hilst  (MIT)