Probabilistic Scientific Computing: Statistical inference approaches to numerical analysis and algorithm design
(June 5 – 9, 2017)

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Description

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.

Organizing Committee


= speaker    = poster presenter

Monday June 5, 2017
Time Description Speaker Location Abstracts Slides
8:30 - 8:55Registration121 South Main Street Providence RI 11th Floor Collaborative Space
8:55 - 9:00WelcomeICERM 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 computationPhilipp 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 computationPhilipp 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
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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
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3:30 - 4:00Coffee/Tea Break11th Floor Collaborative Space
4:00 - 4:45Gaussian processes: A hands-on tutorialParis Perdikaris, Massachusetts Institute of Technology11th Floor Lecture Hall
5:00 - 6:30Welcome Reception11th Floor Collaborative Space

Tuesday June 6, 2017
Time Description Speaker Location Abstracts Slides
9:00 - 9:45Probabilistic Dimensionality ReductionNeil Lawrence, University of Sheffield and Amazon Research Cambridge11th Floor Lecture Hall
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10:00 - 10:30Coffee Break11th Floor Collaborative Space
10:30 - 11:15TBARoman Garnett, Washington University in St. Louis11th Floor Lecture Hall
11:30 - 12:15TBATamara 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 equationsTim Sullivan, FU Berlin Zuse Institute Berlin11th Floor Lecture Hall
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3:30 - 4:00Coffee/Tea Break11th Floor Collaborative Space
4:00 - 4:45Bayesian and Game theoretical numerical method for multiscale PDEsLei Zhang, Shanghai Jiao Tong University11th Floor Lecture Hall
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Wednesday June 7, 2017
Time Description Speaker Location Abstracts Slides
9:00 - 9:45Bayesian Calibration of Simulators with Structured Discretization UncertaintyOksana Chkrebtii, The Ohio State University11th Floor Lecture Hall
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10:00 - 10:30Coffee Break11th Floor Collaborative Space
10:30 - 11:15TBAMaziar Raissi, Brown University11th Floor Lecture Hall
11:30 - 12:15Variational Reformulation of the Uncertainty Propagation Problem in Linear Partial Differential EquationsIlias 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
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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
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Thursday June 8, 2017
Time Description Speaker Location Abstracts Slides
9:00 - 9:45Compression, inversion, and approximate PCA of dense kernel matrices at near-linear computational complexityFlorian Schaefer, California Institute Of Technology11th Floor Lecture Hall
10:00 - 10:30Coffee Break11th Floor Collaborative Space
10:30 - 11:15Inference using partial informationJeff Miller, Harvard School of Public Health11th Floor Lecture Hall
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11:30 - 12:15Exact solutions in Burgers turbulence and the equation free methodGovind Menon, Brown University11th Floor Lecture Hall
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12:30 - 2:30Break for Lunch / Free Time
2:30 - 3:15Numerical analysis and random matrix theoryTom Trogdon, University of California Irvine11th Floor Lecture Hall
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3:30 - 4:00Coffee/Tea Break11th Floor Collaborative Space
4:00 - 4:45Panel DiscussionIlse Ipsen, North Carolina State University11th Floor Lecture Hall

Friday June 9, 2017
Time Description Speaker Location Abstracts Slides
9:00 - 9:45TBAYannis Kevrekidis, Princeton University11th Floor Lecture Hall
10:00 - 10:30Coffee Break11th Floor Collaborative Space
10:30 - 11:15Infinitely- Variate IntegrationGreg Wasilkowski, University of Kentucky11th Floor Lecture Hall
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11:30 - 12:15Probabilistic ODE SolversHans Kersting, Max Planck Institute for Intelligent Systems11th Floor Lecture Hall
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12:30 - 2:30Break for Lunch / Free Time
3:30 - 4:00Coffee/Tea Break11th Floor Collaborative Space