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Analytical and Parametric Model Fitting for Inverse Problems, Data Reduction, and Pattern Recognition
Date: October 21, 2020 8:00 am
Speaker: Youssef Nashed (ANL, Stats Perform)
Many scientific and engineering challenges can be formulated as fitting a model to existing data. Whether it is comparing a scientific simulation to known experimental observations, finding a continuous representation of sparse/discrete data points, or the values of model parameters which generalize to unforeseen data examples given historical data; all these tasks share a common underlying principle of model fitting, but with different choices made in the model formulation (parametric or analytical) and the assumptions made about the data (acquisition scheme, noise to signal ratio, continuity, or information locality). In this talk I will highlight a few use cases under this framework. Specifically, I will address research conducted at Argonne National Laboratory for X-ray image reconstruction problems, data reduction for scientific simulations, and deep learning approaches for replacing expensive iterative optimization. Additionally, I will present more recent work for sports computer vision applications that enable real time player detection, tracking, and activity prediction from broadcast video.
Superconducting Radio-Frequency Cavity Fault Classification Using Machine Learning at Jefferson Laboratory
Date: October 2223, 2020 1:00 apm
Speaker: Chris Tennant (Jefferson Laboratory)
Many scientific and engineering challenges can be formulated as fitting a model to existing data. Whether it is comparing a scientific simulation to known experimental observations, finding a continuous representation of sparse/discrete data points, or the values of model parameters which generalize to unforeseen data examples given historical data; all these tasks share a common underlying principle of model fitting, but with different choices made in the model formulation (parametric or analytical) and the assumptions made about the data (acquisition scheme, noise to signal ratio, continuity, or information locality). In this talk I will highlight a few use cases under this framework. Specifically, I will address research conducted at Argonne National Laboratory for X-ray image reconstruction problems, data reduction for scientific simulations, and deep learning approaches for replacing expensive iterative optimization. Additionally, I will present more recent work for sports computer vision applications that enable real time player detection, tracking, and activity prediction from broadcast video.
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