ICERM - Home

Organizing Committee

Lexin Li
University of California, Berkeley

Youssef Marzouk
Massachusetts Institute of Technology

Shari Moskow
Drexel University

Benjamin Peherstorfer
Courant Institute, New York University

Abel Rodriguez
University of California, Santa Cruz

Daniele Venturi
University of California, Santa Cruz

Rachel Ward
University of Texas at Austin

Abstract

The advancement in computing and storage capabilities of modern computational clusters fosters use of novel statistical techniques in machine learning and deep networks. Such data-driven techniques allow one to learn model features and characteristics that are difficult for mathematical methods alone to reveal. Many computational methods achieve model and complexity discovery using methods that lie at the nexus of mathematical, statistical, and computational disciplines. Statistical methods often employ “big data” approaches that glean predictive capability from diverse and enormous databases of information. Emerging methods in machine learning and deep networks can provide impressive results. This workshop gathers researchers at the frontier of large-scale statistical computation, data science, tensor decompositions and approximations, and data-driven model learning, to focus on modern challenges that use data to reduce complexity of models.

VIRTUAL ONLY: Computational Statistics and Data-Driven Models

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Workshop Schedule

Monday, April 20, 2020

9:50 - 10:00 AM EDT

Welcome - ICERM Director

10:00 - 10:45 AM EDT

Fast Discovery of Pairwise Interactions in High Dimensions using Bayes - Tamara Broderick, MIT

11:00 - 11:15 AM EDT

Coffee/Tea Break

11:15 AM - 12:00 PM EDT

Learning Interaction laws in particle- and agent-based systems - Mauro Maggioni, Johns Hopkins University

12:15 - 1:30 PM EDT

Break for Lunch / Free Time

1:30 - 2:15 PM EDT

From Fourier to Koopman - Spectral Methods for Long-term Time Series Prediction - Nathan Kutz, University of Washington

2:30 - 2:45 PM EDT

Coffee/Tea Break

2:45 - 3:30 PM EDT

Kernel Analog Forecasting for Multiscale Problems - Krithika Manohar, California Institute of Technology

3:45 - 4:30 PM EDT

Using dynamical systems ideas to combine in a principled way data-driven models and domain-driven models - Michael Mahoney, ICSI and Department of Statistics, UC Berkeley

4:30 - 4:45 PM EDT

Close the day

Tuesday, April 21, 2020

10:00 - 10:45 AM EDT

Understanding and mitigating gradient flow pathologies in physics-informed neural networks - Paris Perdikaris, University of Pennsylvania

11:00 - 11:15 AM EDT

Coffee/Tea Break

Week

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Poster Session Gallery

Learning context-aware surrogate models for multifidelity importance sampling and Bayesian inverse problems

Terrence Alsup

Differentiating between single particle transport types

Keisha Cook

emgr - EMpirical GRamian Framework

Christian Himpe

Incorporating physical constraints in deep probabilistic models of coarse-grained dynamics

Sebastian Kaltenbach

System-Theoretic Model Reduction in pyMOR

Petar Mlinarić

Density Tracking by Quadrature for High Dimensional Stochastic Differential Equations

Ryleigh Moore

Machine Learning and Data-Driven Approaches in Spatial Statistics

Sarah Soleiman

Data Driven Models for Solving Partial Differential Equations

THOMAS Torku

Analysis of the Ratio of L1 and L2 Norms in Compressed Sensing

Yiming Xu

Hinčin's theorem for additive free convolutions of R-diagonal *-distributions

Cong Zhou

Fast and Accurate Algorithms for Cosmic Microwave Background Radiation Data on HEALPix Points

Kathryn Drake

Lecture Videos

Welcome and Fast Discovery of Pairwise Interactions in High Dimensions usin...

Tamara Broderick

Massachusetts Institute of Technology

April 20, 2020 • 12:00 AM

VIRTUAL ONLY: Computational Statistics and Data-Driven Models

VIRTUAL ONLY: Computational Statistics and Data-Driven Models

Apr 20 - 24, 2020

Organizing Committee

Abstract

Workshop Schedule

Monday, April 20, 2020

Tuesday, April 21, 2020

Poster Session Gallery

Learning context-aware surrogate models for multifidelity importance sampling and Bayesian inverse problems

Differentiating between single particle transport types

emgr - EMpirical GRamian Framework

Incorporating physical constraints in deep probabilistic models of coarse-grained dynamics

System-Theoretic Model Reduction in pyMOR

Density Tracking by Quadrature for High Dimensional Stochastic Differential Equations

Machine Learning and Data-Driven Approaches in Spatial Statistics

Data Driven Models for Solving Partial Differential Equations

Analysis of the Ratio of L1 and L2 Norms in Compressed Sensing

Hinčin's theorem for additive free convolutions of R-diagonal *-distributions

Fast and Accurate Algorithms for Cosmic Microwave Background Radiation Data on HEALPix Points

Lecture Videos

Welcome and Fast Discovery of Pairwise Interactions in High Dimensions usin...

From Fourier to Koopman - Spectral Methods for Long-term Time Series Predic...

Kernel Analog Forecasting for Multiscale Problems

Understanding and mitigating gradient flow pathologies in physics-informed ...

Metric representations - Algorithms and Geometry

Optimal experimental design for the quantification of model uncertainty

Modeling dynamical systems from data

Reduced Order Model Approach to Inverse Scattering

Transport and Multilevel Approaches for Large-Scale PDE-Constrained Bayesia...

Output-weighted optimal sampling for Bayesian regression and rare event sta...

Extreme event probability estimation using large-deviation theory and PDE-c...

Characterizing Markov process dynamics from trajectory data

History Matching for Inverse Modelling in Physical and Biological Systems

Data-Driven Mechanistic Models -- Design and Inference

Operator-split reduced order models for problems with moving interfaces

Associated Semester Workshops

Model and dimension reduction in uncertain and dynamic systems

Mathematics of Reduced Order Models

VIRTUAL ONLY: Algorithms for Dimension and Complexity Reduction

VIRTUAL ONLY: Computational Statistics and Data-Driven Models

Apr 20 - 24, 2020

Organizing Committee

Abstract

Confirmed Speakers & Participants

Workshop Schedule

Monday, April 20, 2020

Tuesday, April 21, 2020

Poster Session Gallery

Learning context-aware surrogate models for multifidelity importance sampling and Bayesian inverse problems

Differentiating between single particle transport types

emgr - EMpirical GRamian Framework

Incorporating physical constraints in deep probabilistic models of coarse-grained dynamics

System-Theoretic Model Reduction in pyMOR

Density Tracking by Quadrature for High Dimensional Stochastic Differential Equations

Machine Learning and Data-Driven Approaches in Spatial Statistics

Data Driven Models for Solving Partial Differential Equations

Analysis of the Ratio of L1 and L2 Norms in Compressed Sensing

Hinčin's theorem for additive free convolutions of R-diagonal *-distributions

Fast and Accurate Algorithms for Cosmic Microwave Background Radiation Data on HEALPix Points

Lecture Videos

Welcome and Fast Discovery of Pairwise Interactions in High Dimensions usin...

From Fourier to Koopman - Spectral Methods for Long-term Time Series Predic...

Kernel Analog Forecasting for Multiscale Problems

Understanding and mitigating gradient flow pathologies in physics-informed ...

Metric representations - Algorithms and Geometry

Optimal experimental design for the quantification of model uncertainty

Modeling dynamical systems from data

Reduced Order Model Approach to Inverse Scattering

Transport and Multilevel Approaches for Large-Scale PDE-Constrained Bayesia...

Output-weighted optimal sampling for Bayesian regression and rare event sta...

Extreme event probability estimation using large-deviation theory and PDE-c...

Characterizing Markov process dynamics from trajectory data

History Matching for Inverse Modelling in Physical and Biological Systems

Data-Driven Mechanistic Models -- Design and Inference

Operator-split reduced order models for problems with moving interfaces

Associated Semester Workshops

Model and dimension reduction in uncertain and dynamic systems

Mathematics of Reduced Order Models

VIRTUAL ONLY: Algorithms for Dimension and Complexity Reduction

Confirmed Speakers & Participants