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DTSTAMP:20241030T201737
DTSTART;TZID=America/Detroit:20241114T150000
DTEND;TZID=America/Detroit:20241114T160000
SUMMARY:Lecture / Discussion:CMENAS and Georgia State University Lecture Series. Bridging the Gulf: Patterns in Contemporary story from Kuwait to Oman
DESCRIPTION:This presentation focuses on the importance of studying contemporary Gulf states. The definition of contemporary in this context considers the period starting from the early 1990s. The reason for this has to do with the collapse of the Soviet Union\, regional changes like the invasion of Kuwait and its consequences\, and the wider Middle Eastern regional consequences. The presentation also looks at the patterns that emerged in all Gulf states at the same time in different contexts: political\, economic\, and social.\n\nMahjoob Zweiri is a professor of contemporary politics and Middle Eastern history with a focus on Iran and the Gulf region. He was the director of the Gulf Studies Center at Qatar University from 2018 to 2024 and the head of the humanities department from 2011 to 2016. Before joining Qatar University in 2010\, Zweiri was a senior researcher in Middle Eastern politics and Iran at the Center for Strategic Studies\, University of Jordan. From March 2003 to December 2006\, he was a research fellow and then director of the Centre for Iranian Studies in the Institute for Middle Eastern and Islamic Studies at Durham University. Zweiri has more than 95 publications in the areas of Iran\, contemporary Middle Eastern history and politics\, Gulf studies\, social sciences in universities of the future\, and artificial intelligence’s role in social sciences. In addition to Arabic\, he is fluent in Farsi and English. Zweiri is the founder and editor of the *Journal of Gulf Studies*\, published by Intellect\, and editor of the book series *Contemporary Gulf States*. Currently\, he is a visiting professor at the University of Michigan.\n\nRegister and attend over Zoom: https://myumi.ch/pkJWz.
UID:128550-21861163@events.umich.edu
URL:https://events.umich.edu/event/128550
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Lecture,cmenas,center for middle eastern and north african studies
LOCATION:Off Campus Location
CONTACT:
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BEGIN:VEVENT
DTSTAMP:20250123T125001
DTSTART;TZID=America/Detroit:20241114T150000
DTEND;TZID=America/Detroit:20241114T170000
SUMMARY:Social / Informal Gathering:Hopwood Tea
DESCRIPTION:All are welcome to tea\, coffee\, light refreshments\, and conviviality in a beautiful\, historic setting.
UID:124348-21852934@events.umich.edu
URL:https://events.umich.edu/event/124348
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:The Helen Zell Writers' Program,Contemporary Literature,Undergraduate Students,Writing,Books,Creative Writing,Department Of English Language And Literature,Faculty,Food,Free,Graduate Students,Hopwood Program,Literary Arts
LOCATION:Angell Hall - 1176 (Hopwood Room)
CONTACT:
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BEGIN:VEVENT
DTSTAMP:20241107T101036
DTSTART;TZID=America/Detroit:20241114T150000
DTEND;TZID=America/Detroit:20241114T160000
SUMMARY:Workshop / Seminar:IOE 899: High-dimensional Optimization with Applications to Compute-Optimal Neural Scaling Laws
DESCRIPTION:About the speaker: Courtney Paquette is an assistant professor at McGill University and a CIFAR Canada AI chair\, MILA. She was awarded a Sloan Research Fellowship in Computer Science in 2024. Paquette’s research broadly focuses on designing and analyzing algorithms for large-scale optimization problems\, motivated by applications in data science. She is also interested in scaling limits of stochastic learning algorithms. She received her PhD from the mathematics department at the University of Washington (2017)\, held postdoctoral positions at Lehigh University (2017-2018) and the University of Waterloo (NSF postdoctoral fellowship\, 2018-2019)\, and works 20% as a research scientist at Google DeepMind\, Montreal.\n\n\nAbstract: Given the massive scale of modern ML models\, we now only get a single shot to train them effectively. This restricts our ability to test multiple architectures and hyper-parameter configurations. Instead\, we need to understand how these models scale\, allowing us to experiment with smaller problems and then apply those insights to larger-scale models. In this talk\, I will present a framework for analyzing scaling laws in stochastic learning algorithms using a power-law random features model\, leveraging high-dimensional probability and random matrix theory. I will then use this scaling law to address the compute-optimal question: How should we choose model size and hyper-parameters to achieve the best possible performance in the most compute-efficient manner?
UID:128786-21861628@events.umich.edu
URL:https://events.umich.edu/event/128786
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:899 Seminar Series,Industrial And Operations Engineering,Michigan Engineering,seminar
LOCATION:Industrial and Operations Engineering Building - 1680
CONTACT:
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