CMX is a research group aimed at the development and analysis of novel algorithmic ideas underlying emerging applications in the physical, biological, social and information sciences.  We are distinguished by a shared value system built on the development of foundational mathematical understanding, and the deployment of this understanding to impact on emerging key scientific and technological challenges.


Venkat Chandrasekaran
Mathieu Desbrun
Thomas Hou
Houman Owhadi
Peter Schröder
Andrew Stuart
Joel Tropp

Von Karman

Franca Hoffmann
Ka Chun Lam


Alfredo Garbuno-Inigo
Bamdad Hosseini
Pengfei Liu
Krithika Manohar
Melike Sirlanci

Grad Students

Max Budninskiy
Utkan Candogan
JiaJie Chen
De Huang
Nikola Kovachki
Matt Levine
Riley Murray
Florian Schaefer
Yong Shen Soh
Yousuf Soliman
Armeen Taeb
Gene R. Yoo
Shumao Zhang

Lunch Seminars

(Will be held at 12 noon in Annenberg 213, unless otherwise specified.)

February 20, 2019
Oscar Bruno
Fast spectral time-domain PDE solvers for complex structures: the Fourier-Continuation method
     We present fast spectral solvers for time-domain Partial Differential Equations. Based on a novel Fourier-Continuation (FC) method for the resolution of the Gibbs phenomenon, these methodologies give rise to time-domain solvers for PDEs for general engineering problems and structures. The methods enjoy a number of desirable properties, including spectral time evolution essentially free of pollution or dispersion errors for general PDEs in the time domain, with conditional/unconditional stability for explicit/alternating-direction methods and high order of temporal accuracy. A variety of applications to linear and nonlinear PDE problems will be presented, including the diffusion and wave equations, the Navier-Stokes equations and the elastic wave equation, demonstrating the significant improvements the new algorithms can provide over the accuracy and speed resulting from other approaches.

February 27, 2019
Tapio Schneider
Clouds, Climate Predictions, and Possible Surprises in Warm Climates
     While climate change is certain, precisely how climate will change is less clear. Uncertainties arise from the representation of small-scale processes such as clouds and turbulence. Moreover, these uncertainties are poorly quantified; the ensemble of existing climate models may not span the range of possible climate outcomes. I will illustrate this with an example from the dynamics of stratocumulus clouds, which are crucial for Earth’s energy balance. Large-eddy simulations of stratocumulus clouds show that they can exhibit an instability that leads to dramatic global warming under high greenhouse gas concentrations—an instability that does not seem to be captured by current climate models and whose probability of occurrence cannot be assessed with current models.
     Breakthroughs in the accuracy of climate projections and in the quantification of their uncertainties are now within reach, thanks to advances in the computational and data sciences and in the availability of Earth observations from space and from the ground. To achieve a step change in accuracy of climate projections, we are developing a new Earth system modeling platform. Developed by a university consortium dubbed the Climate Modeling Alliance (CliMA), it will fuse an Earth system model (ESM) with global observations and targeted local high-resolution simulations of clouds and other elements of the Earth system. CliMA capitalizes on advances in data assimilation and machine learning to develop an ESM that automatically learns from diverse data sources, be they observations from space or data generated computationally in high-resolution simulations.

Other Seminars

January 31, 2019
• CMX Seminar Series: Optimal Transport •

Location tba

Wuchen Li
Learning via Wasserstein information geometry  ▦

     In this talk, I review several differential structures from optimal transport (Wasserstein metric). Based on it, I will introduce the Wasserstein natural gradient in parametric models. The L2-Wasserstein metric tensor in probability density space is pulled back to the one on parameter space, under which the parameter space forms a Riemannian manifold. The Wasserstein gradient flows and proximal operator in parameter space are derived. We demonstrate that the Wasserstein natural gradient works efficiently in several machine learning examples, including Boltzmann machine, generative adversary models (GANs) and variational Bayesian statistics.

February 19, 2019
• CMX Special Seminar •

Annenberg 213

Omiros Papaspiliopoulos


February 28, 2019
• CMX Seminar Series: Optimal Transport •

Location tba

Chenchen Mou
Mean field games on graphs ▦

     Mean field game theory is the study of the limit of Nash equilibria of differential games when the number of players tends to infinity. It was introduced by J.-M. Lasry and P.-L. Lions, and independently by P. Caines, M. Huang and R. Malhame. A fundamental object in the theory is the master equation, which fully characterizes the limit equilibrium. In this talk, we will introduce Mean field game and master equations on graphs. We will construct solutions to both equations and link them to the solution to a Hamilton-Jacobi equation on graphs.

March 7, 2019
• CMX Special Seminar •

Annenberg 213

Gitta Kutyniok
Deep Learning and Modeling: Taking the Best out of Both Worlds ▦

     Inverse problems in imaging such as denoising, recovery of missing data, or the inverse scattering problem appear in numerous applications. However, due to their increasing complexity, model-based methods are often today not sufficient anymore. At the same time, we witness the tremendous success of data-based methodologies, in particular, deep neural networks for such problems. However, at the same time, pure deep learning approaches often neglect known and valuable information from the modeling world. In this talk, we will provide an introduction to this problem complex and then focus on the inverse problem of computed tomography, where one of the key issues is the limited angle problem. For this problem, we will demonstrate the success of hybrid approaches. We will develop a solver for this severely ill-posed inverse problem by combining the model-based method of sparse regularization by shearlets with the data-driven method of deep learning. Our approach is faithful in the sense that we only learn the part which cannot be handled by model-based methods, while applying the theoretically controllable sparse regularization technique to all other parts. We further show that our algorithm significantly outperforms previous methodologies, including methods entirely based on deep learning.

March 7, 2019
• CMX Special Seminar •

Annenberg 314

Zbigniew J. Jurek


date tba
• CMX Seminar Series: Optimal Transport •

Annenberg 314
Alpar Meszaros
topic tba ▦


date tba
• CMX Seminar Series: Optimal Transport •

Location tba
Flavien Leger
topic tba ▦


Meetings and Workshops

Past Events

Lunch Seminars Other Seminars Meetings & Workshops