Ph.D. Candidate, Dept. of Mathematical Sciences, George Mason University
Tuesday, January 23, 2024, 3:00-4:00 PM ET (1:00-2:00 PM MT)
In person at: Gaithersburg Bldg. 101 LR D with VTC to Boulder 1-4072
Online at: ZoomGov
Add this talk to your calendar: https://inet.nist.gov/calendar/ics/2276326
Abstract: We expound on formal principles combining Topological Data Analysis (TDA) and representations of shape—using separable shape tensors (SST)—to motivate novel perspectives on the form and nature of shape in data. Specifically, TDA descriptors extracting persistent topological structures are combined with manifold learning of discrete, separable shapes to offer unique perspectives on imaging data. We demonstrate robust and interpretable feature extraction to augment modern imaging science in applications involving image/signal classification and metrology of materials. More broadly, this framework has significant potential to impact various fields where precise quantification of topology and shape is crucial in determining fundamental properties and characteristics of image.
Bio: Specializing in manifold learning with a specific interest in principled methods of non-linear dimensionality reduction, Jeanie's research focuses on developing tools to enhance our understanding of artificial intelligence or offer alternatives based on more modern approaches to classical data analysis techniques. During her academic journey, Jeanie has navigated the realms of both pure and applied mathematics, demonstrating her versatility and commitment to interdisciplinary approaches. Under the mentorship of Dr. Zach Grey, Jeanie has actively contributed to the exploration of innovative techniques at the intersection of Topology and Shape Analysis. As she continuous to pursue her PhD, Jeanie remains committed to advancing the field of data analysis, with a goal to equip individuals with the tools and knowledge to work confidently with their data.
Host: Zach Grey
Note: This talk will be recorded to provide access to NIST staff and associates who could not be present to the time of the seminar. The recording will be made available in the Math channel on NISTube, which is accessible only on the NIST internal network. This recording could be released to the public through a Freedom of Information Act (FOIA) request. Do not discuss or visually present any sensitive (CUI/PII/BII) material. Ensure that no inappropriate material or any minors are contained within the background of any recording. (To facilitate this, we request that cameras of attendees are muted except when asking questions.)
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