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Special Projects
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Challenges and Opportunities in Magnetic Resonance Fingerprinting
Description:
Magnetic Resonance Fingerprinting (MRF) is a novel approach to collecting quantitative maps of MRI tissue properties in an efficient manner. Instead of focusing on collecting images weighted by specific tissue properties and using them to extract quantitative information, MRF works by extracting these quantitative maps directly from rapidly coll...
Creator:
Seiberlich, Nicole (University of Michigan)
Created:
2019-10-18
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Last Updated:
2022-04-22
Challenges and Opportunities in Magnetic Resonance Fingerprinting
Citizen Science and Machine Learning at Zooniverse
Description:
As researchers gather ever-larger datasets there is an increasing demand for citizen science and reliance on machine learning. We will introduce Zooniverse, the world's largest citizen science platform, and show how citizen scientists are helping researchers extract meaningful information from their data. But the demand for citizen scientists an...
Creator:
Wright, Darryl (University of Minnesota, Twin Cities)
Created:
2019-10-01
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Last Updated:
2022-04-22
Citizen Science and Machine Learning at Zooniverse
Classical knot invariants
Description:
Knots can be distinguished via invariants. Invariants measure different aspects of knottedness. Crossing number, bridge number, tunnel number and unknotting number provide distinct insights. Moreover, the behavior of these invariants under connected sum deserves closer scrutiny.
Creator:
Schultens, Jennifer (University of California, Davis)
Created:
2019-06-18
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Last Updated:
2022-04-22
Classical knot invariants
Clustering High-dimensional Data with Path Metrics: A Balance of Density and Geometry
Description:
This talk discusses multiple methods for clustering high-dimensional data, and explores the delicate balance between utilizing data density and data geometry. I will first present path-based spectral clustering, a novel approach which combines a density-based metric with graph-based clustering. This density-based path metric allows for fast algo...
Creator:
Little, Anna (The University of Utah)
Created:
2020-10-27
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Last Updated:
2022-04-22
Clustering High-dimensional Data with Path Metrics: A Balance of Density and Geometry
Coding and Generative Design for 3D Printing
Description:
3D printing and design allows us to physically experience complex mathematical objects. In this talk we’ll take a 3D-printed tour of mathematical knots, tessellations, fractals, and polyhedra. Using code and generative design we can create parametric models that leverage randomness to achieve structural variety or even organic-looking behavior. ...
Creator:
Taalman, Laura (James Madison University)
Created:
2021-02-09
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Last Updated:
2022-04-22
Coding and Generative Design for 3D Printing
Coherent Optical Processing with Machine Learning
Description:
Coherent sensing of light has the potential to revolutionize optics in much the same way that coherent RF processing revolutionized communications and RADAR. The power of this approach is that once optical measurements are converted to digital form, they can be processed with advanced, nonlinear, highly intelligent algorithms that can far exceed...
Creator:
Bouman, Charles (Purdue University)
Created:
2019-10-16
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Last Updated:
2022-04-22
Coherent Optical Processing with Machine Learning
Community Detection Using Total Variation and Surface Tension
Description:
In the last few years, several papers have leveraged a connection between graph cuts and total variation to derive nonlinear relaxations of combinatorially hard problems, which yield algorithms that have proved to be effective, scalable, and theoretically tractable. In particular, Hu et al. showed that the modularity maximization problem that is...
Creator:
Boyd, Zach (University of North Carolina, Chapel Hill)
Created:
2020-09-17
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Last Updated:
2022-04-22
Community Detection Using Total Variation and Surface Tension
Computational Imaging: Beyond the limits imposed by lenses
Description:
The lens has long been a central element of cameras, since its early use in the mid-nineteenth century by Niepce, Talbot, and Daguerre. The role of the lens, from the Daguerrotype to modern digital cameras, is to refract light to achieve a one-to-one mapping between a point in the scene and a point on the sensor. This effect enables the sensor t...
Creator:
Veeraraghavan, Ashok (Rice University)
Created:
2019-10-17
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Last Updated:
2022-04-22
Computational Imaging: Beyond the limits imposed by lenses
Computational Imaging: From structured low-rank methods to model based deep learning.
Description:
The talk will provide an overview of structured low-rank algorithms and model based deep learning methods, with applications to MR imaging. I will briefly introduce structured low-rank algorithms for super-resolution, blind channel equalization, and inverse problems. The talk will then focus on non-linear generalizations of structured low-rank m...
Creator:
Jacob, Mathews (The University of Iowa)
Created:
2019-10-17
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Last Updated:
2022-04-22
Computational Imaging: From structured low-rank methods to model based deep learning.
Computational Imaging with Deep Learning
Description:
Neural networks have surpassed the performance of virtually any traditional computer vision algorithm thanks to their ability to learn priors directly from the data. The common and relatively simple encoder/decoder architecture, for instance, has pushed the state-of-the-art of a number of tasks, from optical flow estimation, to image deblurring,...
Creator:
Gallo, Orazio (NVIDIA Corporation)
Created:
2019-10-15
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Last Updated:
2022-04-22
Computational Imaging with Deep Learning