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Event Series Event Series: Special Seminar Series

Algebraic vision: A gentle introduction | Jessie Loucks-Tavitas

February 5 @ 2:00 pm - 3:30 pm

My talk will be broken into three parts:
Part I: Meet Jessie.
Part II: Assessing Deep Learning Models. A short lesson on assessment criteria for deep learning models, such as LLMs and image segmentation models.
Part III: Algebraic Vision, a Gentle Introduction. Algebraic vision, lying in the intersection of computer vision and projective geometry, is the study of 3D objects being photographed by multiple cameras, using techniques found in computational algebraic geometry. Two natural questions arise: (1) Given a 3D object and multiple images of it, can we determine the relative camera positions? And, (2) given multiple images as well as relative camera locations, can we reconstruct the object being photographed? Carlsson and Weinshall showed in 1998 that the algorithms to solve these problems are intrinsically connected. A beneficial corollary of recent joint work with Erin Connelly and Timothy Duff is a formalization of this “duality” mechanism. We will discuss this formalization, along with some future directions that we hope to venture down.
Bio: Jessie Loucks-Tavitas is currently a 6th-year PhD candidate in mathematics at the University of Washington. She received her MS in mathematics in 2022, following her BA in mathematics in 2018 from California State University, Sacramento. Jessie’s commitment to higher education and supporting underrepresented groups has been acknowledged with the Gloria Hewitt Endowed Fellowship and the Excellence in Teaching Award from the UW mathematics department in 2022. Outside of academic pursuits, Jessie finds joy in drinking black coffee, cozying up with a book and her two cats, and adventuring with her friends and family in her newfound love for skiing.


February 5
2:00 pm - 3:30 pm
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HDSI General


Jessie Loucks-Tavitas
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