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 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.

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.

Zoom Link for all 2:30 pm Detroit-time Talks: zoom link

Passcode: the smallest prime > 100

FUTURE TALKS

Oct. 30: Ethan Epperly (UC Berkeley)

Column subset selection, active learning, and determinantal point processes

Subset selection problems are everywhere in data science: Which subset of products should an e-commerce website recommend? Which subset of training data should I use to fine-tune my model? Which subset of genetic markers predict a disease? This talk concerns a common mathematical formulation of the subset selection problem where one seeks to select representative rows or columns of a data matrix. For this task, the recently proposed adaptive randomized pivoting (ARP) method, due to Cortinovis and Kressner (SIMAX '25), is among the most powerful. This talk will survey the ARP method and reinterpret it by drawing connections to active learning methods for linear regression and the theory of determinantal point processes. Leveraging this connection will yield new analysis of the ARP method and more rapid algorithms based on fast rejection sampling-based determinantal point process samplers. With such implementation, ARP is among the fastest and most accurate algorithms for column and row subset selection.

Nov 6: Bohan Chen (Caltech)

TBD

Nov 13: Bubacarr Bah (London School of Hygeine)

TBD

Nov. 20: Haotian Jiang (University of Chicago)

TBD

Dec. 4: Minxin Zhang (UCLA)

TBD

Dec. 11: Mariia Seleznova (LMU Munich)

TBD

Dec. 18: Noam Razin (Princeton)

TBD

Jan. 8: Stephen Becker (University of Colorado Boulder)

TBD

Jan. 15: Nicholas Dexter (Florida State University)

TBD

Jan. 22: Stephan Wojtowytsch (University of Pittsburgh)

TBD

Jan. 29: Akram Aldroubi (Vanderbilt University)

TBD

Feb. 5: Jonas Latz (University of Manchester)

TBD

Feb. 12: Weilin Li (City University of New York)

TBD

Feb. 26: Matthieu Dolbeault (Brown University)

TBD

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