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BEGIN:VEVENT
DTSTAMP:20240429T132014
DTSTART;TZID=America/Detroit:20240429T134000
DTEND;TZID=America/Detroit:20240429T135000
SUMMARY:Workshop / Seminar:Public Health Graduation Kickoff &  Photo Celebration
DESCRIPTION:The excitement of graduation is building up\, and we want to celebrate this significant milestone. Join us as we kick off the celebration with professional photo opportunities\, treats\, and a chance to bid farewell to the place that has been the heart of your educational journey.Date: April 29\, 2024  Time: 1:00 pm - 3:00 pm  Location: SPH 1: Professional Photos in the Cafe and Public Health Sign\, celebrations around SPH 1 Highlights:Professional Photos:Capture the magic with snapshots alongside Dean Bowman and Dr. Emily Youatt.Iconic Moment:  Freeze time with a professional photo at the School of Public Health sign.Creative Captures: Have fun with DIY photos using imaginative backdrops and props.Get Informed: Gain insights into graduation resources and a Q&A session to address all your queries.Dress the Part: Wear your regalia or come business casual and showcase your unique style.Bring along your friends\, family\, or that favorite professor who left an indelible mark on your public health journey and document this big occasion. This isn't just a celebration\; it's a golden opportunity to access valuable resources and find answers to all your graduation-related questions ahead of the big day. Secure your spot now to snap a photo with Dean Bowman or Dr. Emily Youatt and make the most of numerous additional photo opportunities. Don't miss out on this fun event that marks the beginning of your graduation festivities!
UID:119775-21843561@events.umich.edu
URL:https://events.umich.edu/event/119775
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Sessions
LOCATION:SPH 1
CONTACT:
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20240429T132014
DTSTART;TZID=America/Detroit:20240429T135000
DTEND;TZID=America/Detroit:20240429T140000
SUMMARY:Workshop / Seminar:Public Health Graduation Kickoff &  Photo Celebration
DESCRIPTION:The excitement of graduation is building up\, and we want to celebrate this significant milestone. Join us as we kick off the celebration with professional photo opportunities\, treats\, and a chance to bid farewell to the place that has been the heart of your educational journey.Date: April 29\, 2024  Time: 1:00 pm - 3:00 pm  Location: SPH 1: Professional Photos in the Cafe and Public Health Sign\, celebrations around SPH 1 Highlights:Professional Photos:Capture the magic with snapshots alongside Dean Bowman and Dr. Emily Youatt.Iconic Moment:  Freeze time with a professional photo at the School of Public Health sign.Creative Captures: Have fun with DIY photos using imaginative backdrops and props.Get Informed: Gain insights into graduation resources and a Q&A session to address all your queries.Dress the Part: Wear your regalia or come business casual and showcase your unique style.Bring along your friends\, family\, or that favorite professor who left an indelible mark on your public health journey and document this big occasion. This isn't just a celebration\; it's a golden opportunity to access valuable resources and find answers to all your graduation-related questions ahead of the big day. Secure your spot now to snap a photo with Dean Bowman or Dr. Emily Youatt and make the most of numerous additional photo opportunities. Don't miss out on this fun event that marks the beginning of your graduation festivities!
UID:119775-21843562@events.umich.edu
URL:https://events.umich.edu/event/119775
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Sessions
LOCATION:SPH 1
CONTACT:
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20240409T154624
DTSTART;TZID=America/Detroit:20240429T140000
DTEND;TZID=America/Detroit:20240429T160000
SUMMARY:Lecture / Discussion:Advances in Machine Learning Safety
DESCRIPTION:Abstract: As more and more machine learning (ML) and artificial intelligence (AI) systems are used in practical settings\, it is essential that they exhibit responsible\, trustworthy\, and safe behavior. This dissertation contributes to machine learning safety by focusing on the key aspects of value alignment\, data quality\, distributional robustness\, and their intersections. It is largely motivated by concerns about discrepancies between societal expectations and the performance of end-to-end ML systems\, data quality degradation caused by privacy protocols or data noise\, and distribution shifts posed by dynamic interactions between ML models and their environments.\n\nThis dissertation zeroes in on three problems that arise in machine learning safety. They are:\n1. calibrated data-dependent constraints: guarantee real-world goals are satisfied with a user-prescribed probability at test time\;\n2. statistical cost of data noise in constrained learning: quantify the effect of sensitive attribute noise on the generalizability of (fairness) constraints\;\n3. model misspecification in performative prediction: design a robust learning framework to better approximate the performative optimum.\n\nBy carefully employing a blend of tools from distributionally robust optimization\, stochastic optimization\, and statistical inference\, this dissertation tackles these pressing issues in ML safety. The methodologies and insights provided herein lay the groundwork toward a more ethical and effective deployment of ML and AI technologies\, contributing to ensuring that these systems align with human values and exhibit reliability against the complexities of real-world applications.
UID:121284-21846312@events.umich.edu
URL:https://events.umich.edu/event/121284
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Dissertation
LOCATION:West Hall - 438
CONTACT:
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