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X-WR-CALNAME:Vassar Math & Stats Events Page
X-ORIGINAL-URL:https://pages.vassar.edu/mathstats
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20241111T153000
DTEND;TZID=America/New_York:20241111T163000
DTSTAMP:20241114T004320Z
CREATED:20241114T004216Z
LAST-MODIFIED:20241114T004320Z
UID:681-1731339000-1731342600@pages.vassar.edu
SUMMARY:Pre-Registration Q&A
DESCRIPTION:Have questions about which Math/Stats courses to take next semester?  Come enjoy some free cider and donuts and chat with faculty and students! \nMonday\, November 11\, 3:30PM in the Rocky 3rd Floor Hallway
URL:https://pages.vassar.edu/mathstats/event/pre-registration-qa/
CATEGORIES:Student
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20241114T153000
DTEND;TZID=America/New_York:20241114T163000
DTSTAMP:20241113T165503Z
CREATED:20241001T235901Z
LAST-MODIFIED:20241113T165503Z
UID:641-1731598200-1731601800@pages.vassar.edu
SUMMARY:(Colloquium) Joshua Snoke\, RAND Corporation
DESCRIPTION:
URL:https://pages.vassar.edu/mathstats/event/colloquium-joshua-snoke-rand-corporation/
CATEGORIES:Colloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20241115T150000
DTEND;TZID=America/New_York:20241115T160000
DTSTAMP:20241101T004108Z
CREATED:20240908T231641Z
LAST-MODIFIED:20241101T004108Z
UID:616-1731682800-1731686400@pages.vassar.edu
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20241115T163000
DTEND;TZID=America/New_York:20241115T173000
DTSTAMP:20241112T224911Z
CREATED:20241112T224905Z
LAST-MODIFIED:20241112T224911Z
UID:679-1731688200-1731691800@pages.vassar.edu
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20241115T180000
DTEND;TZID=America/New_York:20241115T200000
DTSTAMP:20241114T130312Z
CREATED:20241114T130312Z
LAST-MODIFIED:20241114T130312Z
UID:689-1731693600-1731700800@pages.vassar.edu
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20241118T160000
DTEND;TZID=America/New_York:20241118T170000
DTSTAMP:20241117T034343Z
CREATED:20241117T034318Z
LAST-MODIFIED:20241117T034343Z
UID:703-1731945600-1731949200@pages.vassar.edu
SUMMARY:(Colloquium) Federica Ricci\, University of California\, Irvine
DESCRIPTION:Colloquium Talk\nFederica Ricci\, University of California\, Irvine\nMonday November 18\, 2024 at 4:00PM\nRocky 312 \nTitle: Statistical modeling of sparse networks\n\nAbstract: The study of scientific and social phenomena often requires modeling data in the form of networks\, i.e. the set of interactions between entities like proteins\, neurons or people. Statistical models provide ways to address important questions\, including why some entities are connected but not others\, and whether there are interactions that have not been observed. In this talk\, I will present my work on developing a class of models that can discover an unobserved set of clusters (or communities) among interacting entities and that can learn the number of clusters from data. Unlike previous approaches with those properties\, the proposed framework can model sparse networks. Capturing sparsity is especially important when dealing with large networks: for example\, in online social networks\, someone’s connections grow much slower than linearly with the number of users. I will summarize a posterior-inference method based on Markov Chain Monte Carlo and I will show the advantages of this approach on a set of social and biological networks.
URL:https://pages.vassar.edu/mathstats/event/colloquium-federica-ricci-university-of-california-irvine/
LOCATION:Rockefeller Hall 312
CATEGORIES:Colloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20241122T160000
DTEND;TZID=America/New_York:20241122T170000
DTSTAMP:20250122T212747Z
CREATED:20241010T134315Z
LAST-MODIFIED:20250122T212747Z
UID:647-1732291200-1732294800@pages.vassar.edu
SUMMARY:(Colloquium) Ivan Cheltsov\, University of Edinburgh
DESCRIPTION:
URL:https://pages.vassar.edu/mathstats/event/colloquium-ivan-cheltsov-university-of-edinburgh/
CATEGORIES:Colloquium
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