We present synthetic aperture radar (SAR) as a computational imaging modality, emphasizing aspects of radar that differentiate it from other imaging problems. In this context, we present samples of work resulting from two related lines of inquiry in our group: (1) sparsity-driven radar imaging, and (2) machine learning for radar imaging. Specifi...
Creator:
Cetin, Mujdat (University of Rochester)
Created:
2019-10-18
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Forensic analyses are often concerned with identifying the spatial source of biological residue. Using recent advances in high-throughput sequencing technologies, dust collected from nearly any object can be shown to harbor DNA fragments from thousands of bacteria and fungi species which may be informative of the source of the dust. We show that...
Creator:
Reich, Brian (North Carolina State University)
Created:
2018-04-25
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Estimating individualized treatment rules is a central task of personalized or precision medicine. In this presentation, we review several new developments in outcome weighted learning for identifying individualized treatment rules for two challenging settings: dose finding and treatment selection under right censoring. In the former, we develop...
Creator:
Kosorok, Michael (University of North Carolina, Chapel Hill)
Created:
2017-09-15
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
We present a general method for privacy-preserving Bayesian inference in Poisson factorization, a broad class of models that includes some of the most widely used models in the social sciences. Our method satisfies limited precision local privacy, a generalization of local differential privacy, which we introduce to formulate privacy guarantees ...
Creator:
Wu, Steven (University of Minnesota, Twin Cities)
Created:
2019-06-21
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Computer vision and image analysis are major application examples of deep learning. While computer vision and image analysis deal with existing images and produce features of these images (images to features), tomographic imaging produces images of multi-dimensional structures from experimentally measured “encoded” data as various tomographic fe...
Creator:
Wang, Ge (Rensselaer Polytechnic Institute)
Created:
2019-10-16
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
We present an overview of how SageMath and Macaulay2 have provided crucial computational help and insight to problems in string theory over the last decade. As concrete examples, we will use the Calabi-Yau landscape. In parallel, we propose a paradigm to machine-learn the ever-expanding databases which have emerged in mathematical physics, algeb...
Creator:
He, Yang-Hui (University of London)
Created:
2019-07-23
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Current guidelines for treatment decision making largely rely on data from ran- domized controlled trials (RCTs) studying average treatment effects. They may be inadequate to make individualized treatment decisions in real-world settings. Large- scale electronic health records (EHR) provide opportunities to fulfill the goals of personalized medi...
Creator:
Wang, Yuanjia (Columbia University)
Created:
2018-11-07
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
We survey some developments in machine learning and data analysis,focusing on those in which optimization is an importantcomponent. Some of these have possible relevance for industrial andenergy applications, for example, constraints and covariances could be'learned' from process data rather than specified a priori. Somepossibilities along these...
Creator:
Wright, Stephen (University of Wisconsin, Madison)
Created:
2016-02-22
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
In this talk, I will provide an introduction to the use of machine learning and convolutional neural networks (CNNs) in the area of MR image reconstruction. Building on a general framework of inverse problems and variational optimization, I will focus on application examples from image reconstruction for accelerated Magnetic Resonance (MR) imagi...
Creator:
Knoll, Florian (NYU Langone Medical Center)
Created:
2019-10-17
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
