Organizing Committee
Abstract

This workshop is concerned with sampling challenges, modeling and simulation for data-rich applications in high dimensions. It brings together mathematicians, statisticians and computational scientists to explore the interplay between computational applied mathematics and data science. On the agenda will be novel developments in the study of complex phenomena based on data-analytic techniques, such as efficient calculation of ergodic (long term) averages and statistical inference under a wide range of geometric, physical and analytical constraints.

In applied mathematics and computational science, in particular in molecular modeling, image analysis and geosciences, among others, many objects of interest are high-dimensional and stochastic, and a wide variety of techniques have been developed for sampling and approximating the quantities of interest. Similar issues arise in the area of data science and statistical modeling, where learning problems in the presence of high-dimensional data require efficient computational algorithms for sampling and approximation.

The workshop will focus on recent advances in the design of rigorous discrete-dynamics based sampling approaches, algorithms development for large-scale data analysis and stochastic dynamical systems, scalable and rigorous numerical methods for stochastic differential equations and sampling from high-dimensional distributions, and exploitation of low-dimensional structures in high-dimensional data and stochastic dynamical systems for model reduction and efficient Monte-Carlo schemes. The meeting will foster the interchange and deployment of the latest methodologies for sampling and approximation.

Image for "Stochastic numerical algorithms, multiscale modeling and high-dimensional data analytics"
A measure induced by a stochastic differential equation whose drift induces a limit cycle, presenting a challenge to local simulation schemes. [Photo credit: Mark Girolami]

Confirmed Speakers & Participants

Talks will be presented virtually or in-person as indicated in the schedule below.

  • Speaker
  • Poster Presenter
  • Attendee
  • Virtual Attendee

Workshop Schedule

Monday, July 18, 2016
TimeEventLocationMaterials
8:30 - 8:55am EDTRegistration11th Floor Collaborative Space 
8:55 - 9:00am EDTWelcome - ICERM Director11th Floor Lecture Hall 
9:00 - 9:35am EDTRevisiting Monte Carlo Estimation - Mark Girolami, University of Warwick11th Floor Lecture Hall 
9:45 - 10:20am EDTSequential matrix completion - Sergio Bacallado, Universiy of Cambridge11th Floor Lecture Hall
10:30 - 11:00am EDTCoffee/Tea Break11th Floor Collaborative Space 
11:00 - 11:35am EDTFast Robustness Quantification with Variational Bayes - Tamara Broderick, Massachusetts Institute of Technology11th Floor Lecture Hall
11:45 - 12:20pm EDTAdaptive sparse quadrature for high-dimensional integration with Gaussian distribution- application to Bayesian inverse problems. - Peng Chen, The University of Texas at Austin11th Floor Lecture Hall
12:30 - 3:00pm EDTBreak for Lunch / Free Time  
3:00 - 3:35pm EDTData-driven stochastic model reduction - Fei Lu, University of California, Berkeley11th Floor Lecture Hall
3:45 - 4:20pm EDTMathematics for data-driven modeling - The science of crystal balls - Yannis Kevrekides, Princeton University11th Floor Lecture Hall
4:30 - 6:00pm EDTWelcome Reception11th Floor Collaborative Space 
Tuesday, July 19, 2016
TimeEventLocationMaterials
9:00 - 9:35am EDTMulti-fidelity information fusion algorithms for high dimensional systems and massive data-sets - George Karniadakis, Brown University11th Floor Lecture Hall
9:45 - 10:20am EDTProduct form stationary distributions of stochastic reaction networks with non-mass action kinetics, and application to constrained averaging of multiscale systems - Simon Cotter, University of Manchester11th Floor Lecture Hall
10:30 - 11:00am EDTCoffee/Tea Break11th Floor Collaborative Space 
11:00 - 11:35am EDTStatistical inference of stochastic single-cell and single-molecule dynamics - Heinz Koeppl, Technische Universität Darmstadt11th Floor Lecture Hall
11:45 - 12:20pm EDTMeasure transport, variational inference, and low-dimensional maps - Youssef Marzouk, Massachusetts Institute of Technology11th Floor Lecture Hall
12:30 - 2:15pm EDTBreak for Lunch / Free Time  
2:15 - 2:50pm EDTStochastic integrators for multiscale and ergodic dynamical systems - Assyr Abdulle, École Polytechnique Fédérale de Lausanne11th Floor Lecture Hall
3:00 - 3:35pm EDTStratification for Markov chain Monte Carlo sampling - Jonathan Weare, University of Chicago11th Floor Lecture Hall
3:45 - 4:15pm EDTCoffee/Tea Break11th Floor Collaborative Space 
4:15 - 5:00pm EDTLightning Talks11th Floor Lecture Hall 
Wednesday, July 20, 2016
TimeEventLocationMaterials
9:00 - 9:35am EDTEfficient methods for estimating stochastic gradients and sensitivity indices in complex stochastic dynamics - Markos Katsoulakis, University of Massachusetts, Amherst11th Floor Lecture Hall
9:45 - 10:20am EDTQuasi-Reliable Estimates of Effective Sample Size - Robert Skeel, Purdue University11th Floor Lecture Hall
10:20 - 10:30am EDTGroup Photo in Lecture Hall11th Floor Lecture Hall 
10:30 - 11:00am EDTCoffee/Tea Break11th Floor Collaborative Space 
11:00 - 11:35am EDTErgodic Stochastic Differential Equations and Sampling- A numerical analysis perspective - Konstantinos Zygalakis, University of Edinburgh11th Floor Lecture Hall
11:45 - 1:50pm EDTBreak for Lunch / Free Time  
2:00 - 2:35pm EDTEnsemble preconditioning schemes for Bayesian inverse problems - Charles Matthews, University of Chicago11th Floor Lecture Hall
2:45 - 3:20pm EDTAccurate sampling algorithms for constraints and noisy gradients - Benedict Leimkuhler, University of Edinburgh11th Floor Lecture Hall 
3:30 - 5:00pm EDTPoster Session and Coffee/Tea Break11th Floor Collaborative Space 
Thursday, July 21, 2016
TimeEventLocationMaterials
9:00 - 9:35am EDTVariational Hamiltonian Monte Carlo - Babak Shababa, The Center for Statistical Consulting11th Floor Lecture Hall
9:45 - 10:20am EDTScalable and Efficient MCMC Algorithms for Complex Posteriors - Yian Ma, University of Washington11th Floor Lecture Hall
10:30 - 11:00am EDTCoffee/Tea Break11th Floor Collaborative Space 
11:00 - 11:35am EDTSub-Gaussian estimators of the mean of a random matrix with entries possessing only two moments - Stanislav Minsker, University of Southern California11th Floor Lecture Hall
11:45 - 12:20pm EDTAdaptive two-stage integrators for sampling algorithms based on Hamiltonian dynamics - Elena Akhmatskaya, Basque Center for Applied Mathematics - BCAM11th Floor Lecture Hall
12:30 - 2:15pm EDTBreak for Lunch / Free Time  
2:15 - 2:50pm EDTScalable Bayesian Inference with Hamiltonian Monte Carlo - Michael Betancourt, University of Warwick11th Floor Lecture Hall
3:00 - 3:35pm EDTComputing ergodic limits for SDEs - Michael Tretyakov, The University of Nottingham11th Floor Lecture Hall
3:45 - 4:15pm EDTCoffee/Tea Break11th Floor Collaborative Space 
4:15 - 5:00pm EDTPanel Discussion11th Floor Lecture Hall 
Friday, July 22, 2016
TimeEventLocationMaterials
9:00 - 9:35am EDTGeometric Methods for the Approximation of High-dimensional Dynamical Systems - Mauro Maggioni, Johns Hopkins University11th Floor Lecture Hall
9:45 - 10:20am EDTScalable algorithms for Markov process parameter inference - Darren Wilkinson, Newcastle University11th Floor Lecture Hall
10:30 - 11:00am EDTCoffee/Tea Break11th Floor Collaborative Space 
11:00 - 11:35am EDTCovariance-controlled adaptive Langevin thermostat for large-scale Bayesian sampling - Xiaocheng Shang, Brown University11th Floor Lecture Hall
11:45 - 12:20pm EDTIrreversible langevin samplers and variance reduction- a large deviations approach and diffusion on graphs - Konstantinos Spiliopoulos, Boston University11th Floor Lecture Hall
12:30 - 2:15pm EDTBreak for Lunch / Free Time  
2:15 - 2:50pm EDTSimple ergodic variants of the Hamiltonian Monte Carlo method - Jesus Sanz-Serna, Universidada Carlos III de Madrid11th Floor Lecture Hall
3:00 - 3:45pm EDTWrapup Q&A11th Floor Lecture Hall 

Lecture Videos

Simple ergodic variants of the Hamiltonian Monte Carlo method

Jesus Maria Sanz-Serna
Universidad Carlos III de Madrid
July 22, 2016
Stochastic numerical algorithms, multiscale modeling and high-dimensional...
Play     Abstract

Covariance-controlled adaptive Langevin thermostat for large-scale Bayesian sampling

Xiaocheng Shang
Brown University
July 22, 2016
Stochastic numerical algorithms, multiscale modeling and high-dimensional...
Play     Abstract

Computing ergodic limits for SDEs

Michael Tretyakov
University of Nottingham
July 21, 2016
Stochastic numerical algorithms, multiscale modeling and high-dimensional...
Play     Abstract     Slides

Scalable Bayesian Inference with Hamiltonian Monte Carlo

Michael Betancourt
University of Warwick
July 21, 2016
Stochastic numerical algorithms, multiscale modeling and high-dimensional...
Play     Abstract     Slides

Adaptive two-stage integrators for sampling algorithms based on Hamiltonian dynamics

Elena Akhmatskaya
Basque Center for Applied Mathematics - BCAM
July 21, 2016
Stochastic numerical algorithms, multiscale modeling and high-dimensional...
Play     Abstract     Slides

Sub-Gaussian estimators of the mean of a random matrix with entries possessing only two moments

Stanislav Minsker
University of Southern California
July 21, 2016
Stochastic numerical algorithms, multiscale modeling and high-dimensional...
Play     Abstract     Slides

Scalable and Efficient MCMC Algorithms for Complex Posteriors

Yian Ma
University of Washington
July 21, 2016
Stochastic numerical algorithms, multiscale modeling and high-dimensional...
Play     Abstract

Variational Hamiltonian Monte Carlo

Babak Shababa
UCI
July 21, 2016
Stochastic numerical algorithms, multiscale modeling and high-dimensional...
Play     Abstract

Quasi-Reliable Estimates of Effective Sample Size

Robert Skeel
Purdue University
July 20, 2016
Stochastic numerical algorithms, multiscale modeling and high-dimensional...
Play     Abstract     Slides

Measure transport, variational inference, and low-dimensional maps

Youssef Marzouk
Massachusetts Institute of Technology
July 19, 2016
Stochastic numerical algorithms, multiscale modeling and high-dimensional...
Play     Abstract

Product form stationary distributions of stochastic reaction networks with non-mass action kinetics, and application to constrained averaging of multiscale systems

Simon Cotter
University of Manchester
July 19, 2016
Stochastic numerical algorithms, multiscale modeling and high-dimensional...
Play     Abstract     Slides

Data-driven stochastic model reduction

Fei Lu
University of California, Berkeley
July 18, 2016
Stochastic numerical algorithms, multiscale modeling and high-dimensional...
Play     Abstract     Slides

Adaptive sparse quadrature for high-dimensional integration with Gaussian distribution: application to Bayesian inverse problems

Peng Chen
The University of Texas at Austin
July 18, 2016
Stochastic numerical algorithms, multiscale modeling and high-dimensional...
Play     Abstract

Fast Robustness Quantification with Variational Bayes

Tamara Broderick
Massachusetts Institute of Technology
July 18, 2016
Stochastic numerical algorithms, multiscale modeling and high-dimensional...
Play     Abstract     Slides

Stochastic integrators for multiscale and ergodic dynamical systems

Assyr Abdulle
École Polytechnique Fédérale de Lausanne
June 19, 2016
Stochastic numerical algorithms, multiscale modeling and high-dimensional...
Play     Abstract