BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//UM//UM*Events//EN
CALSCALE:GREGORIAN
BEGIN:VTIMEZONE
TZID:America/Detroit
TZURL:http://tzurl.org/zoneinfo/America/Detroit
X-LIC-LOCATION:America/Detroit
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20070311T020000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20071104T020000
RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20250110T153226
DTSTART;TZID=America/Detroit:20250214T100000
DTEND;TZID=America/Detroit:20250214T110000
SUMMARY:Meeting:La Tertulia: Spanish Coffee Hour
DESCRIPTION:Spanish Coffee & Conversation Hours\n\nALL LEVELS AND STUDENTS WELCOME!\n- Practice your Spanish speaking skills with students and instructors in a welcoming and relaxed setting\n- Free coffee\, tea\, light snacks\, and baked goods\n- Get advice on courses and discuss study abroad\n\nEvery Friday\, Winter 2025\nJanuary 10 to April 18\n10:00am - 11:00 am\n4th Floor\, MLB Commons
UID:130925-21867399@events.umich.edu
URL:https://events.umich.edu/event/130925
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Community,Talk,Spanish,Social,Romance Languages And Literatures,multicultural,Language,Interdisciplinary,intercultural,Interactive,In Person,Humanities,Free,Food,Diversity Equity and Inclusion,Discussion,Culture,Coffee,Inclusion
LOCATION:Modern Languages Building - RLL Commons (MLB 4314)
CONTACT:
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20250110T170530
DTSTART;TZID=America/Detroit:20250214T100000
DTEND;TZID=America/Detroit:20250214T163000
SUMMARY:Other:Leaves Under the Lens
DESCRIPTION:The leaf surface is a dynamic landscape where tiny\, specialized structures help plants interact with the world around them. Let’s bring this world into view! Join us for an exhibit that highlights the complex and often beautiful anatomy of leaves from the Matthaei collection. Plants throughout the conservatory will be paired with microscope photographs and micro-CT scans that illustrate the otherwise invisible structures that protect leaves from chewing insects\, absorb (or repel!) water\, and even recruit “bodyguards”. You won’t look at leaves the same way again! \n\nThis project is a collaboration between MBGNA and the Weber and Vasconcelos labs in the Department of Ecology and Evolutionary Biology\, led by PhD student Rosemary Glos.
UID:130943-21867440@events.umich.edu
URL:https://events.umich.edu/event/130943
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Family,science,In Person,Free,eeb,Biology
LOCATION:Matthaei Botanical Gardens
CONTACT:
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20250211T093930
DTSTART;TZID=America/Detroit:20250214T100000
DTEND;TZID=America/Detroit:20250214T110000
SUMMARY:Workshop / Seminar:Statistics Department Seminar Series: Yixin Wang\, Assistant Professor\, Department of Statistics\, University of Michigan.
DESCRIPTION:Abstract: Causal inference traditionally relies on tabular data\, where treatments\, outcomes\, and covariates are manually collected and labeled. However\, many real-world problems involve unstructured data—images\, text\, and videos—where treatments or outcomes are high-dimensional and unstructured\, or all causal variables are hidden within the unstructured observations. This talk explores causal inference in such settings.\n\nWe begin with cases where all causal variables (including treatments\, outcomes\, covariates) are hidden in unstructured observations. These causal problems require a crucial first step\, extracting high-level latent causal factors from raw unstructured inputs. We develop algorithms to identify these factors. While traditional methods often assume statistical independence\, causal factors are often correlated or causally connected. Our key observation is that\, despite correlations\, the causal connections (or the lack of) among factors leave geometric signatures in the latent factors' support - the ranges of values each can take. These signatures allow us to provably identify latent causal factors from passive observations\, interventions\, or multi-domain datasets (up to different transformations).\n\nNext\, we tackle cases where unstructured data itself serves as either the treatment or the outcome. In these cases\, standard causal queries like average treatment effect (ATE) are not suitable—subtracting one text\, image\, or video outcome from another is meaningless. High-dimensional unstructured treatments also challenge the overlap assumption required for causal identification. To address these challenges\, we propose new causal queries: for unstructured outcomes\, we pinpoint outcome features most affected by the treatment\; for unstructured treatments\, we identify influential treatment features driving outcome differences. Finally\, we extend these ideas to decision-making algorithms\, such as optimizing natural language actions for desired outcomes.\n\nhttps://yixinwang.github.io/
UID:132381-21870848@events.umich.edu
URL:https://events.umich.edu/event/132381
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:seminar
LOCATION:West Hall - 340
CONTACT:
END:VEVENT
END:VCALENDAR