One World Mathematics of INformation, Data, and Signals (1W-MINDS) Seminar
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 either at 2:30 pm New York time or at 10:00 am Paris /4:00 pm summer Shanghai time/ 5:00 pm winter Shanghai time. To find and join the 1W-MINDS slack channel, please click here.
Current Organizers (September 2024 - May 2025): Axel Flinth (Principal Organizer for Europe/Asia, Umeå University), Christian Parkinson (Principal Organizer for The Americas, Michigan State University), Rima Alaifari (ETH Zürich), Alex Cloninger (UC San Diego), Longxiu Huang (Michigan State University), Mark Iwen (Michigan State University), Weilin Li (City College of New York), Siting Liu (UC Riverside), Kevin Miller (Brigham Young University), and Yong Sheng Soh (National University of Singapore).
Most previous talks are on the seminar YouTube channel. You can catch up there, or even subscribe if you like.
To sign up to receive email announcements about upcoming talks, click here.
To join MINDS slack channel, click here.
The organizers would like to acknowledge support from the Michigan State University Department of Mathematics. Thank you.
Passcode: the smallest prime > 100
Zoom Link for all 10:00 am Paris/4:00 pm Summer Shanghai/5 pm Winter Shanghai time Talks: Paris/Shanghai link
Passcode: The integer part and first five decimals of e (Eulers number)
FUTURE TALKS
January 23: Joe Kileel (University of Texas-Austin) - 1:30PM New York time (Note: one hour earlier than usual)
Title: Covering numbers of real algebraic varieties and applications to data science
Abstract: In this talk I will discuss covering numbers of real algebraic varieties and applications to data science. Specifically, we control the number of ell_2 balls of radius epsilon needed to cover a real variety, image of a polynomial map, and semialgebraic set in Euclidean space, in terms of the degrees of the relevant polynomials and number of variables. The bound significantly improves the best known general bound, and its proof is much more straightforward. On the applications side, I will give consequences for CP tensor decomposition, randomized optimization and neural network theory. Joint work with Yifan Zhang at UT Austin (arXiv:2311.05116).