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DTSTART;TZID=America/New_York:20241115T150000
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SUMMARY:(LAVA) Robert Ronan\, VC '15
DESCRIPTION:LAVA Talk on Transitioning From Math To Machine Learning By Mistake (Or Necessity)\, with Robert Ronan (VC ’15)\, NYU Langone Health.  Friday\, November 15\, 2024 at 3PM in Rocky 310. \n \nAbout Robert:\n\nRobert Ronan\, VC ’15\, is a machine learning engineer at the cardiology department of NYU Langone Health\, where he leads the department’s machine learning and data hub efforts. His work involves employing deep learning models to detect patterns in EKGs that clinicians cannot easily identify\, enabling improved clinical decision support\, and predictive utility of EKGs. Robert holds master’s degrees in computer science and mathematics from NYU Tandon School of Engineering and The CUNY Graduate Center\, respectively\, and a bachelor’s in mathematics from Vassar College. In his free time\, he is an amateur mixologist and an extensive collector of vintage amaro\, a style of Italian liqueur.
URL:https://pages.vassar.edu/mathstats/event/lava-robert-ronan-vc-15/
CATEGORIES:LAVA
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DTSTART;TZID=America/New_York:20241115T163000
DTEND;TZID=America/New_York:20241115T173000
DTSTAMP:20241112T224911Z
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SUMMARY:(Colloquium) Andrew Ackerman\, University of North Carolina at Chapel Hill
DESCRIPTION:Colloquium Talk\nAndrew Ackerman\, University of North Carolina at Chapel Hill\nFriday November 15\, 2024 at 4:30PM\nRocky 312 \nTitle: Measures of Fairness and High Dimensional Data Integration \nAbstract:The first component of this talk will present a representative discussion from a novel course\, entitled Moral Machine Learning\, developed at the University of North Carolina at Chapel Hill. In particular\, we motivate and introduce statistical measures of fairness used to assess classification algorithms. This discussion will culminate in an Incompleteness Theorem\, which demonstrates that these measures are\, in some fundamental way\, not totally reconcilable. How to assess fairness despite this incompleteness result will motivate open questions discussed at the conclusion of the second component of this talk. This latter component will primarily be focused on original research. We present completed work for high dimensional data integration for human neuroscience. In particular\, neuroimaging studies\, such as the Human Connectome Project (HCP)\, often collect multifaceted data to study the complex human brain. However\, these data are often analyzed in a pairwise fashion\, which can hinder our understanding of how different brain-related measures interact. In this study\, we analyze the multi-block HCP data using the Data Integration via Analysis of Subspaces (DIVAS) method. We integrate structural and functional brain connectivity\, substance use\, cognition\, and genetics in an exhaustive five-block analysis. This gives rise to the important finding that genetics is the single data modality most predictive of brain connectivity\, outside of brain connectivity itself. Moreover\, investigations of shared space loadings provide interpretable associations between particular brain regions and drivers of variability\, such as alcohol consumption in the substance-use data block. Novel Jackstraw hypothesis tests are developed for the DIVAS framework to establish statistically significant loadings. We conclude by discussing proposed future work\, at both the faculty and undergraduate levels\, in each of data integration and algorithmic fairness.
URL:https://pages.vassar.edu/mathstats/event/colloquium-andrew-ackerman-university-of-north-carolina-at-chapel-hill/
LOCATION:Rockefeller Hall 310
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DTSTART;TZID=America/New_York:20241115T180000
DTEND;TZID=America/New_York:20241115T200000
DTSTAMP:20241114T130312Z
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SUMMARY:Math Jam - Fall 2024
DESCRIPTION:Vassar Students: Run a station at the Math Jam working with 2nd-8th graders.  Sign up here to volunteer. \nLocal community: Sign up here to register your 2nd-8th grader. \nNovember 15\, 2024 from 6-8 pm at the Aula/Ely Hall
URL:https://pages.vassar.edu/mathstats/event/math-jam-fall-2024/
LOCATION:The Aula
CATEGORIES:Student
ORGANIZER;CN="Lisa Lowrance":MAILTO:llowrance@vassar.edu
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