Events for February 5, 2024

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Lessons from the deep: engineering biosensors, workflows, and visualizations for communication and collaboration in comparative medicine and climate science | Jessica Kendall-Bar

Special Seminar Series
Powell-Focht Bioengineering Hall (PFBH), FUNG Auditorium

Abstract: Effective conservation and management relies on an in-depth understanding of the health of marine ecosystems. Dr. Kendall-Bar's interdisciplinary approach combines engineering, visualization, and computation to study ocean resilience in terms of the extreme physiology and behavior of marine animals, establishing eco-physiological baselines to track over time in the face of climate change. This seminar and chalk talk will review her work to create innovative tools to detect, visualize, and analyze the physiology and behavior of animals in extreme environments that showcase their biological resilience to oxygen and sleep deprivation. From individuals to ecosystems, Kendall-Bar conducts multidisciplinary physiological studies that combine basic and applied science with potential to advance conservation and comparative medicine. This seminar reviews Kendall-Bar's dissertation research on sleep in seals and presents some current and ongoing projects to combine high-performance computing, automation, and visualization to assess diving physiology in human freedivers, epilepsy in sea lions, and cardiac performance in some of the largest (blue whales) and smallest (emperor penguins) divers. Kendall-Bar’s newest projects involve novel data visualizations and science communication to inform research as well as international policy in domains ranging from marine mammal conservation to traditional ecological knowledge and coral reef restoration.

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Scaling Data-Constrained Language Model

EnCORE Series
Virtual

Extrapolating scaling trends suggest that training dataset size for LLMs may soon be limited by the amount of text data available on the internet. In this talk we investigate scaling language models in data-constrained regimes. Specifically, we run a set of empirical experiments varying the extent of data repetition and compute budget. From these experiments we propose and empirically validate a scaling law for compute optimality that accounts for the decreasing value of repeated tokens and excess parameters. Finally, we discuss and experiment with approaches for mitigating data scarcity.

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Algebraic vision: A gentle introduction | Jessie Loucks-Tavitas

Special Seminar Series

Abstract:
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.