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DTSTAMP:20240103T115038
DTSTART;TZID=America/Detroit:20240112T093000
DTEND;TZID=America/Detroit:20240112T103000
SUMMARY:Workshop / Seminar:WISE and Shine: Lin van Nieuwstadt
DESCRIPTION:Welcome back to campus with another WISE and Shine brunch! We are having Dr. Lin van Nieuwstadt as our special guest as we eat\, chat\, and learn more about life in industry from Lin. This event is open to all\, although registration is required.Lin van Nieuwstadt is a research engineer with SRI and an electrical engineer by training. Over the decades she worked on many challenging projects\, from building the communication systems for the Sojourner Mars Rover to measuring ice thickness in Lake Superior. Her doctoral dissertation is in microwave imaging system for breast cancer detection It is her passion to support and motivate the next generation of engineers!
UID:116442-21836827@events.umich.edu
URL:https://events.umich.edu/event/116442
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Sessions
LOCATION:WISE Office, 3236 Undergraduate Science Building
CONTACT:
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BEGIN:VEVENT
DTSTAMP:20240104T093049
DTSTART;TZID=America/Detroit:20240112T100000
DTEND;TZID=America/Detroit:20240112T110000
SUMMARY:Workshop / Seminar:Department Seminar Seminar Series: Xinwei Shen\, Postdoctoral Researcher\, Seminar for Statistics ETH Zürich
DESCRIPTION:Abstract: Estimating the full (conditional) distribution is crucial to applications that require uncertainty quantification. However\, existing methods such as quantile regression typically fall short for high-dimensional response variables\, such as spatial fields of climate variables. To address this\, distributional learning aims to estimate a generative model to describe the target distribution\, which enables inference by sampling. In this talk\, we introduce a distributional learning method called engression. We discuss the potential of distributional learning for problems that persist even with access to the population of the training data\, including extrapolation\, distribution shifts\, and causal inference. Specifically\, we demonstrate that the reliability of engression does not break down immediately at the boundary of the training support\, in contrast to neural network regression or tree ensembles. In addition\, we present variants of engression for multi-environment data or under an instrumental variable setting to achieve robust prediction against distribution shifts and causal effect estimation. Engression has also been shown to be effective in modeling distributions of high-dimensional climate data. Finally\, we point out several outlooks of engression in smoothing\, classification\, and dimensionality reduction. \n\n\nBio: Xinwei Shen is a postdoctoral researcher at the Seminar for Statistics at ETH Zürich working with Peter Bühlmann and Nicolai Meinshausen. In 2022\, she obtained her PhD at Hong Kong University of Science and Technology advised by Tong Zhang. Her research interests include distributional learning\, causality\, robustness\, and applications in climate science.
UID:116284-21836558@events.umich.edu
URL:https://events.umich.edu/event/116284
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:seminar
LOCATION:West Hall - 340
CONTACT:
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BEGIN:VEVENT
DTSTAMP:20240112T105317
DTSTART;TZID=America/Detroit:20240112T100000
DTEND;TZID=America/Detroit:20240112T110000
SUMMARY:Social / Informal Gathering:La Tertulia
DESCRIPTION:*Practice your Spanish speaking skills with fellow students and instructors in a welcoming and relaxed environment\n*Free coffee\, tea\, light snacks\, and baked goods\n*Get advice on courses and discuss study abroad\n\nFridays\, January 12th - April 19th\n\nAll levels and students are welcome!\n\nFor more information\, please contact Julie Harrell at harrelju@umich.edu
UID:117043-21838506@events.umich.edu
URL:https://events.umich.edu/event/117043
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Free,Language,Social,Spanish Studies
LOCATION:Modern Languages Building - RLL Commons (MLB 4314)
CONTACT:
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