Statistics Undergraduate Students Turned on the Charm at Aggieland Saturday 2019!
An annual campus-wide open house for prospective students and their families. These bright students dazzled potential Aggies while showcasing our Undergraduate Program. Prospects were able to meet current students, attend departmental presentations, tour the dorms, visit libraries and computer labs and see everything that Texas A&M has to offer. Students could also learn more about colleges and majors, get information about admission and pick up some financial aid and scholarship information, too.
Showing their school spirit is what they do best! Pictured here are current undergraduate students. GIG 'EM!
I Am Texas A&M Science - Irina Gaynanova
Even for the most seasoned academic, career inspiration starts somewhere, and it all begins with a story. Here's one in our I Am Texas A&M Science series from Texas A&M statistician Irina Gaynanova, who, despite not exactly loving her academic field as an undergraduate, grew to appreciate working with data enough to make a career out of developing statistical methodology for high dimensional data problems.
Jianhua Huang, a respected researcher and educator in statistical machine learning, computational statistics and statistical methods for big data sets, has been appointed as associate director of the Texas A&M Institute of Data Science.Read More →
Texas A&M statistician Derya Akleman will work closely with each of the college's five departments and across the university in her new administrative role as assistant dean for diversity and college climate in the College of Science.Read More →
11:30 AM / 12:30 PM Blocker Building (BLOC), Room 113 979-845-3141
TAMU Department of Statistics
Avalanches in an Excitable Network
I will discuss a propagation of avalanches in a complex network. “Avalanches” here is a general term describing a cascading spread of a “damage” in a network of linked objects. Examples of avalanches in applications include epidemics, outages in a power grid, rumors in a social network, neural cascades in cortex, viruses in a computer network, forest fire etc. Two types of heuristic approximation are frequently used for models of this type in applications, branching process approximation for cascades of a small size at the beginning of the process and a deterministic dynamical system once the avalanche spreads to a significant fraction of a large network. I am going to present several results concerning the exact relation between the avalanche model and these limits, including rates of convergence and rigorous bounds for common characteristics of the model. For instance, the widely used branching approximation is in essence a linearization, it is monotone in all basic parameters while the original model isn't. Loosely speaking, some of our results can be viewed as a "second order" correction to the branching approximation.
Friday, 3/29/2019, 11:30 AM, BLOC 113