Upcoming Events

Clear all filters Faculty Mathematics and Statistics

Previous Month

January 2020

Next Month
16
Jan

Some Models for the Interaction of Long and Short Waves in Dispersive Media

Conference/Seminar

The speaker for this colloquium is Dr. Nghiem Nguyen, Assistant Professor in the Mathematics and Statistics department.
Part of the Mathematics and Statistics colloquium series.
Refreshments served at 3:00pm.

3:30 pm - 4:30 pm | Animal Science |
24
Jan

Applied Mathematics Seminar: Extreme First Passage Times of Diffusion

Conference/Seminar

Speaker: Sean D Lawley, Department of Mathematics, University of Utah

Abstract: Why do 300 million sperm cells search for the oocyte in human fertilization when only a single sperm cell is necessary? Why do 1000 calcium ions enter a dendritic spine when only two ions are necessary to activate the relevant receptors? The seeming redundancy in these and many other biological systems can be understood in terms of extreme first passage time (FPT) theory.

While FPT theory is often used to estimate timescales in biology, the overwhelming majority of studies focus on the time it takes a given single searcher to find a target. However, in many scenarios the more relevant timescale is the FPT of the first searcher to find a target from a large group of searchers. This fastest FPT depends on extremely rare events and it is often orders of magnitude faster than the FPT of a given single searcher. In this talk, we will explain recent results in extreme FPT theory and show how they modify traditional notions of diffusion timescales.

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

SUBMIT AN EVENT

Previous

JANUARY 2020

 Next
Sun Mon Tue Wed Thu Fri Sat
29 30 31 1 2 3 4
5 6 7 8 9 10 11
12 13 14 15 16 17 18
19 20 21 22 23 24 25
26 27 28 29 30 31 1

View Today

View By

Day Week Month

  Event Types

Target Audiences

  Departments