Organizing Committee
Abstract

There is an urgent and unmet need to formally analyze, design, develop and deploy advanced methods and algorithms that can scale in statistical and computational efficiency to the size of modern data sets and the complexity of contemporary mathematical models. Addressing this need will require a holistic approach involving new foundational theory, algorithms, and programming language design.

The emerging research theme of Probabilistic Scientific Computing (PSC) or Probabilistic Numerics lies at the nexus of these overlapping directions. It aims to improve statistical quantification of uncertainty, improve computational efficiency, and build more effective and scalable numerical methods for statistical models by leveraging the natural correspondence between computation and inference.

The primary goal of the workshop is to introduce recent results and new directions in probabilistic scientific computing to the US research communities in statistics and machine learning, in numerical analysis, and in theoretical computer science.

Confirmed Speakers & Participants

Workshop Schedule

Monday, June 5, 2017
TimeEventLocationMaterials
8:30 - 8:55Registration121 South Main Street Providence RI 11th Floor Collaborative Space 
8:55 - 9:00Welcome - ICERM Director11th Floor Lecture Hall 
9:00 - 9:45The computations of acting agents, and the agents acting in computations - a tutorial on the inference view of numerical computation - Philipp Hennig, Max Planck Institute for Intelligent Systems11th Floor Lecture Hall
10:00 - 10:30Coffee Break11th Floor Collaborative Space 
10:30 - 11:15The computations of acting agents, and the agents acting in computations - a tutorial on the inference view of numerical computation - Philipp Hennig, Max Planck Institute for Intelligent Systems11th Floor Lecture Hall
11:30 - 12:15Computational Information Games, a mini-tutorial. (Part I) - Houman Owhadi, California Institute of Technology11th Floor Lecture Hall
12:30 - 2:30Break for Lunch / Free Time  
2:30 - 3:15Computational Information Games, a mini-tutorial. (Part II) - Houman Owhadi, California Institute of Technology11th Floor Lecture Hall
3:30 - 4:00Coffee/Tea Break11th Floor Collaborative Space 
4:00 - 4:45Gaussian processes- A hands-on tutorial - Paris Perdikaris, Massachusetts Institute of Technology11th Floor Lecture Hall
5:00 - 6:30Welcome Reception11th Floor Collaborative Space 
Tuesday, June 6, 2017
TimeEventLocationMaterials
9:00 - 9:45Probabilistic Dimensionality Reduction - Neil Lawrence, University of Sheffield and Amazon Research Cambridge11th Floor Lecture Hall
10:00 - 10:30Coffee Break11th Floor Collaborative Space 
10:30 - 11:15Bayesian optimization for automating model selection - Roman Garnett, Washington University in St. Louis11th Floor Lecture Hall
11:30 - 12:15Fast Quantification of Uncertainty and Robustness with Variational Bayes - Tamara Broderick, Massachusetts Institute of Technology11th Floor Lecture Hall
12:30 - 2:30Break for Lunch / Free Time  
2:30 - 3:15Strong convergence rates of probabilistic integrators for ordinary differential equations - Tim Sullivan, FU Berlin Zuse Institute Berlin11th Floor Lecture Hall
3:30 - 4:00Coffee/Tea Break11th Floor Collaborative Space 
4:00 - 4:45Bayesian and Game theoretical numerical method for multiscale PDEs - Lei Zhang, Shanghai Jiao Tong University11th Floor Lecture Hall
Wednesday, June 7, 2017
TimeEventLocationMaterials
9:00 - 9:45Bayesian Calibration of Simulators with Structured Discretization Uncertainty - Oksana Chkrebtii, The Ohio State University11th Floor Lecture Hall
10:00 - 10:30Coffee Break11th Floor Collaborative Space 
10:30 - 11:15Numerical Gaussian Processes for Time-dependent and Non-linear Partial Differential Equations - Maziar Raissi, Brown University11th Floor Lecture Hall
11:30 - 12:15Variational Reformulation of the Uncertainty Propagation Problem in Linear Partial Differential Equations - Ilias Bilionis, Purdue University11th Floor Lecture Hall
12:20 - 12:20Group Photo11th Floor Lecture Hall 
12:30 - 2:30Break for Lunch / Free Time  
2:30 - 3:15Bayesian Probabilistic Numerical Methods. (Part I) - Chris Oates, Newcastle University11th Floor Lecture Hall
3:30 - 4:00Coffee/Tea Break11th Floor Collaborative Space 
4:00 - 4:45Bayesian Probabilistic Numerical Methods. (Part II) - Jon Cockayne, University of Warwick11th Floor Lecture Hall
Thursday, June 8, 2017
TimeEventLocationMaterials
9:00 - 9:45Compression, inversion, and approximate PCA of dense kernel matrices at near-linear computational complexity - Florian Schaefer, California Institute Of Technology11th Floor Lecture Hall
10:00 - 10:30Coffee Break11th Floor Collaborative Space 
10:30 - 11:15Inference using partial information - Jeff Miller, Harvard School of Public Health11th Floor Lecture Hall
11:30 - 12:15Exact solutions in Burgers turbulence and the equation free method - Govind Menon, Brown University11th Floor Lecture Hall
12:30 - 2:30Break for Lunch / Free Time  
2:30 - 3:15Numerical analysis and random matrix theory - Tom Trogdon, University of California Irvine11th Floor Lecture Hall
3:30 - 4:00Coffee/Tea Break11th Floor Collaborative Space 
4:00 - 4:45Panel Discussion - Ilse Ipsen, North Carolina State University11th Floor Lecture Hall 
Friday, June 9, 2017
TimeEventLocationMaterials
9:00 - 9:45Data and the gauge-invariant observation of dynamical systems - Yannis Kevrekidis, Princeton University11th Floor Lecture Hall
10:00 - 10:30Coffee Break11th Floor Collaborative Space 
10:30 - 11:15Infinitely- Variate Integration - Greg Wasilkowski, University of Kentucky11th Floor Lecture Hall
11:30 - 12:15Probabilistic ODE Solvers - Hans Kersting, Max Planck Institute for Intelligent Systems11th Floor Lecture Hall
12:30 - 2:30Break for Lunch / Free Time  
3:30 - 4:00Coffee/Tea Break11th Floor Collaborative Space 

Lecture Videos

Probabilistic ODE Solvers

Hans Kersting
Max Planck Institute for Intelligent Systems
June 9, 2017