The 1W-MINDS Seminar was founded in the early days of the COVID-19 pandemic to mitigate the impossibility of travel. We have chosen to continue the seminar since to help form the basis of an inclusive community interested in mathematical data science, computational harmonic analysis, and related applications by providing free access to high quality talks without the need to travel. In the spirit of environmental and social sustainability, we welcome you to participate in both the seminar, and our slack channel community! Zoom talks are held on Thursdays at 2:30 pm New York time. To find and join the 1W-MINDS slack channel, please click here.
Current Organizers (September 2025 - May 2026): Ben Adcock (Simon Fraser University), March Boedihardjo (Michigan State University), Hung-Hsu Chou (University of Pittsburgh), Diane Guignard (University of Ottawa), Longxiu Huang (Michigan State University), Mark Iwen (Principal Organizer, Michigan State University), Siting Liu (UC Riverside), Kevin Miller (Brigham Young University), and Christian Parkinson (Michigan State University).
Most previous talks are on the seminar YouTube channel. You can catch up there, or even subscribe if you like.
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To join MINDS slack channel, click here.
Passcode: the smallest prime > 100
Matrix concentration inequalities and their recently discovered sharp counterparts provide powerful tools to bound the spectrum of random matrices whose entries are linear functions of independent random variables. However, in many applications in theoretical computer science and in other areas one encounters more general random matrix models, called matrix chaoses, whose entries are polynomials of independent random variables. In this talk, we will discuss how to develop general matrix concentration inequalities for matrix chaoses, allowing these models to be treated in a systematic and unified way.
Joint work with Afonso S. Bandeira, Kevin Lucca, and Ramon van Handel.