Upcoming Events
Access Populations (1st Gen, LatinX, Prep Academy, etc.) & Key Methods of Support
Conference/Seminar
Lisa Simmons, Director, Student Orientation & Transition Services
Pam Allcott, Coordinator of Programs, LatinX Cultural Center
Melanie Chambers, USU Learning Specialist
Biology Seminar Speaker Series: Dr. Todd Hillhouse
Conference/Seminar
Dr. Todd Hillhouse from University of Wisconsin -- Green Bay will present a talk on "Neuroscience of Pain and Addiction" virtually on Zoom. Join Seminar: https://usu-edu.zoom.us/j/96328887734?pwd=Q0RLY2cvSVF2UTEzNytaai9HS25ZZz09
IOGP's Informed Citizen Series: Kerri Nakamura
Conference/Seminar
This Informed Citizens Series is a virtual discussion on country mandates and directives with Kerri Nakamura. To RSVP e-mail iogp@usu.edu
Chemistry and Biochemistry Department Seminar, Speaker Jeffery Moore
Conference/Seminar
We welcome Dr. Jeffery Moore for seminar with Dr. Shawn Miller as the faculty host. Find the Zoom link at https://chem.usu.edu/seminars.
Applied Mathematics Seminar: Harnessing the Power of Time Series Data
Conference/Seminar
Zoom ID: 945 1753 3441
Passcode: USUAMS
Speaker: Soukaina Filali Boubrahimi, Assistant Professor of computer science at USU.
abstract:
Time series data are ubiquitous in daily life. From our heart rates measurements, credit cards transactions, to daily bitcoins pricing, all discrete-valued data that have a time component are considered time series data. The limitation of having huge datasets is not at the storage level, as Moore's Law predicted it well, but in the ability to extract useful information from it. In this talk, I will expose some ideas on how to harness the power of time series data through some applications such as solar events predictions and spatiotemporal shape interpolation. I will also discuss our recent model, 1DCNN shapelet miner, a novel interpretable motif discovery model inspired from the idea of mining recurring patterns in DNA that are presumed to have a biological function. Finally, I will provide a discussion on some promising directions on multivariate time series classification using shapelets mining algorithms.
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