## Fibonacci Numbers and Linear Algebra

This week our speaker is Nathan Harman, a graduating senior! His talk “Fibonacci Numbers and Linear Algebra,” will be accompanied by pizza and soda. Come enjoy the last Math Club meeting of the semester Wednesday at 5:30 in LGRT 1634.

Abstract:
The Fibonacci numbers are a sequence defined recursively by F_0=0 F_1=1 and F_n=F_(n-1)+F_(n-2) (i.e. each term is the sum of the two previous terms).  The sequence starts 0,1,1,2,3,5,8,13… and has a number of interesting identities relating the terms to one another. Linear algebra is the study of vector spaces and linear maps between them. Just how are these things related to one another? Come to math club and find out! Hated linear algebra when you took the course? Come to math club and find out why you are WRONG.  Don’t know any linear algebra? Don’t worry about it, I will go over all the relevant terms.

## Next meeting 4/25

Math Club will meet again next Wednesday 4/25 for our last meeting of the semester. See you then!

## Combinatorial rigidity and … unfolding robot arms?

This week our speaker is Stephen Oloo. His talk “Combinatorial rigidity and … unfolding robot arms?”  will be 5:30 on Wednesday in LGRT 1634. Pizza and soda will be served!

Abstract:
In this talk we will use our intuitive, physical understanding of rigidity
to define, mathematically, what it means for a graph to be rigid. We will
then examine the link between the rigidity of a graph and the number of
vertices and edges it contains (running into some high level maths along
the way). Finally we will see how these ideas can be used to prove some
cool theorems.
Don’t know what a graph is? Don’t like the sound of the word combinatorial?
Have no fear; all will be explained.

## Statistical Analysis of Social Networks

This week our speaker is Krista Gile. Her talk “Statistical Analysis of Social Networks,” will  be Wednesday at 5:30 PM in LGRT 1634. Pizza and soda will be provided with the talk. Hope to see you there!

Abstract:

Human populations are often connected by social networks of relations. Such social networks may either be of direct interest to researchers, or useful in designing sampling strategies through which to reach population members. Most existing strategies for statistical inference focus on cases where the full social network is observed. This talk will describe some types of analysis often done using social networks, then talk in detail about an application using a network to sample from a hard-to-reach population: estimating the HIV prevalence among injecting drug users.