Tomographic Techniques for Distributed Materials Assessment #1
Tracks
Room 407
Wednesday, June 26, 2024 |
8:00 AM - 9:15 AM |
407 |
Overview
Chairs: Ken Loh & John Wertz
Speaker
Prof. Matthew Grayson
Professor
Northwestern University
Sensitivity Volume Method for Maximizing Data Importance in Electrical Impedance Tomography
Biography
Matthew Grayson is a professor of electrical engineering at Northwestern University, and an expert in the design, fabrication, and electrical and thermoelectric characterization of electronic devices and materials. He has developed new methods in electrical impedance tomography for 3D bioimaging as well as 2D artificial skin sensor applications. Earlier work studied magnetotransport in low-dimensional electron systems such as quantum wells, one-dimensional wires, classical and quantum Hall effect, and quantum Hall edges. He also characterizes anisotropic conductors and electrical transients in amorphous materials. He has developed advances in thermoelectric materials with the concept of transverse thermoelectrics for integrated thermal management.
Claire C. Onsager
PhD Candidate
Northwestern University
Data Driven Reconstruction Methods Using Sensitivity Volume Method for Electrical Impedance Tomography
Biography
Claire Onsager is currently a PhD candidate in electrical engineering at Northwestern University. As a member of the Grayson group, she has worked on the development of a mathematically robust figure of merit, the sensitivity volume, for electrical impedance tomography (EIT) measurement selection. She applied her EIT work to NDE applications through a recently completed NSF internship with AFRL’s Materials and Manufacturing Directorate. Claire’s research interests include cross-disciplinary development of non-invasive electrical impedance tomography (EIT) imaging algorithms and experimental characterization systems for conductive composites, thin film devices, and for applications in damage detection, structural health monitoring, robotics, and biomedical imaging.
Chenoa Flournoy
Research Engineer
University Of Dayton Research Institute
Application of Neural Networks to Perform Rapid Inversion using Electrical Impedance Tomography
Biography
Chenoa Flournoy (she/her) is a research engineer with the University of Dayton Research Institute. Her academic background includes physics and applied mathematics. She loves science fiction/fantasy, cats, and is vegetarian.