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  • January 21, 2024
  • HDSIComm

Inference in context: Statistical theory and thinking | Jeffrey Bye

Abstract: The likelihood function plays a foundational role in statistical theory. I will demonstrate my teaching philosophy and approach through a lesson on maximum likelihood estimation and its connection to Neyman-Pearson, Bayesian, and other approaches to statistical and scientific inference. I will then expand on the role of context in statistical thinking, particularly how it informs my scholarship on how people learn about data, math, statistics, and programming.

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  • January 21, 2024
  • HDSIComm

Algebraic vision: A gentle introduction | Jessie Loucks-Tavitas

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.

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