BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Halıcıoğlu Data Science Institute - UC San Diego - ECPv6.16.2//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Halıcıoğlu Data Science Institute - UC San Diego
X-ORIGINAL-URL:https://datascience.ucsd.edu
X-WR-CALDESC:Events for Halıcıoğlu Data Science Institute - UC San Diego
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/Los_Angeles
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20230312T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20231105T090000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20240310T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20241103T090000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20250309T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20251102T090000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240208T130000
DTEND;TZID=America/Los_Angeles:20240208T143000
DTSTAMP:20260531T163453
CREATED:20240121T235126Z
LAST-MODIFIED:20240205T165821Z
UID:10000427-1707397200-1707402600@datascience.ucsd.edu
SUMMARY:Inference in context: Statistical theory and thinking | Jeffrey Bye
DESCRIPTION: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. \nBio: Jeffrey K. Bye is an interdisciplinary teacher and researcher who received his Ph.D. from UCLA in Cognitive Psychology with a specialization in computational modeling and minor in quantitative methods. He has years of experience teaching undergraduate- and graduate-level statistics and programming at UCLA and University of Minnesota. His research blends cognitive and learning science approaches to understand how people learn and think about data\, math\, statistics\, and programming. He is passionate about making science more collaborative\, inclusive\, and understandable to the public.
URL:https://datascience.ucsd.edu/event/special-seminar-jeffrey-bye/
LOCATION:3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://datascience.ucsd.edu/wp-content/uploads/2024/01/HDSI-UCSD-Image_Dark-blue-e1710178042629.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240205T140000
DTEND;TZID=America/Los_Angeles:20240205T153000
DTSTAMP:20260531T163453
CREATED:20240121T234846Z
LAST-MODIFIED:20240202T204856Z
UID:10000426-1707141600-1707147000@datascience.ucsd.edu
SUMMARY:Algebraic vision: A gentle introduction | Jessie Loucks-Tavitas
DESCRIPTION:Abstract:\n\nMy talk will be broken into three parts:\nPart I: Meet Jessie.\nPart II: Assessing Deep Learning Models. A short lesson on assessment criteria for deep learning models\, such as LLMs and image segmentation models.\nPart 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.\n\n \nBio: 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.
URL:https://datascience.ucsd.edu/event/special-seminar-jessie-loucks-tavitas/
LOCATION:3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://datascience.ucsd.edu/wp-content/uploads/2024/01/HDSI-UCSD-Image_Dark-blue-e1710178042629.png
END:VEVENT
END:VCALENDAR