Upcoming Events

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January 26 - February 1, 2020

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28
Jan

[CANCELED] Department of Physics Colloquium

Conference/Seminar

Weekly presentations on topics in physics.

3:00 pm - 4:00 pm | SER Building |
29
Jan

[CANCELED] Chemistry & Biochemistry Departmental Seminar

Conference/Seminar

Mar 11 - Andrew Gewirth, University of Illinois Urbana-Champaign
Mar 18 - Chunsheng Wang, University of Maryland
Mar 25 - Li Li, University of Nevada, Reno
Apr 1 - Hansen Seminar: Amy Rosenzweig, Northwestern University - Old Main 115
Apr 7 (Tuesday) - Sharon Hammes-Schiffer, Yale University - ESLC 046
Apr 15 - Jeffrey Moore, University of Illinois (Urbana-Champaign)
Apr 22 - Lawrence Que, University of Minnesota
Apr 29 - Yugang Sun, Temple University

4:00 pm - 5:00 pm | Utah State University |
29
Jan

Get Away Special General Team Meeting

Meeting

The Get Away Special Team is an undergraduate, extracurricular, small satellite research team within the Utah State University Department of Physics. This meeting is where sub-teams collaborate to ensure the success of projects such as a NASA CSLI-selectee CubeSat GASPACS and other small satellite research. All majors in any college are welcome to attend!

5:30 pm - 6:30 pm | SER Building |
31
Jan

Applied Mathematics Seminar: Dynamic Fraud Detection via Sequential Modeling

Conference/Seminar

Speaker: Shuhan Yuan, Computer Science Department, USU
Abstract: Due to the openness and anonymity of the Internet, online platforms (e.g., online social media or knowledge bases) attract a large number of malicious users, such as vandals, trolls, and hoaxes. These malicious users impose severe security threats to online platforms and their legitimate participants. For example, the fraudsters on Twitter can easily spread fake information or post harmful links on the platform. Deep learning models have achieved promising results in image, text, and speech recognition. The key ingredient for the success of deep learning is because it learns meaningful representations of inputs. However, it is challenging to develop deep learning models for fraud detection. In this talk, I focus on tackling two challenges, lack of labeled training data and how to model physical time for fraud detection.

3:30 pm - 4:30 pm | Animal Science |
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