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9:00 am / 3:00 pm
May 25
This in-person three-day short course will introduce participants to the practice of uncertainty quantification (UQ) in computational modeling of physical systems using the Python programming language. Participants will be introduced to a variety of UQ tasks including uncertainty propagation, surrogate modeling, and Bayesian inference using the UQpy toolbox. By the end of the course, it is the goal that students will have the knowledge to begin applying UQ to computational models in their respective fields of study.
In particular, attendees will learn how to:

Link a model to the UQpy software to execute UQ tasks.
Propagate uncertainties through a computational model using simulation-based methods in UQpy
Construct a surrogate model using polynomial chaos expansions and Gaussian process regression
Infer the parameters of a model using Bayesian inference

More information
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Related Institutes
9:00 am / 3:00 pm
May 26
This in-person three-day short course will introduce participants to the practice of uncertainty quantification (UQ) in computational modeling of physical systems using the Python programming language. Participants will be introduced to a variety of UQ tasks including uncertainty propagation, surrogate modeling, and Bayesian inference using the UQpy toolbox. By the end of the course, it is the goal that students will have the knowledge to begin applying UQ to computational models in their respective fields of study.
In particular, attendees will learn how to:

Link a model to the UQpy software to execute UQ tasks.
Propagate uncertainties through a computational model using simulation-based methods in UQpy
Construct a surrogate model using polynomial chaos expansions and Gaussian process regression
Infer the parameters of a model using Bayesian inference

More information
Read More
Related Institutes
9:00 am / 3:00 pm
May 27
This in-person three-day short course will introduce participants to the practice of uncertainty quantification (UQ) in computational modeling of physical systems using the Python programming language. Participants will be introduced to a variety of UQ tasks including uncertainty propagation, surrogate modeling, and Bayesian inference using the UQpy toolbox. By the end of the course, it is the goal that students will have the knowledge to begin applying UQ to computational models in their respective fields of study.
In particular, attendees will learn how to:

Link a model to the UQpy software to execute UQ tasks.
Propagate uncertainties through a computational model using simulation-based methods in UQpy
Construct a surrogate model using polynomial chaos expansions and Gaussian process regression
Infer the parameters of a model using Bayesian inference

More information
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12:00 pm / 5:00 pm
June 3
The theme of this year’s meeting is “AI and Emerging Transformative Areas of Radiotherapy Research”.    Alexander S. Szalay, PhD will be our Keynote Speaker. Dr. Szalay is a Bloomberg Distinguished Professor and Director of the Johns Hopkins Institute for Data Intensive Engineering and Science (IDIES), an interdisciplinary institute tackling cross-cutting challenges in sciences related to big data. In this role, Dr. Szalay is helping to lead the AI-X initiative across the Johns Hopkins enterprise. Moreover, Dr. Szalay is the Alumni Centennial Professor of Astronomy at the Johns Hopkins University, having served as a cosmologist exploring statistical measures of the spatial distribution of galaxies and galaxy formation. More recently, Dr. Szalay has been applying his expertise in computer science, data science, and AI to cancer research.  We will have both oral and poster presentations with discussions.    
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