**September 23**: Josh Sparks

**Title**: How to Use Calculus in Your Everyday Lives

**Abstract**: For many undergraduate students, calculus is the first course one takes, and (sadly) often their last. While not the easiest of subjects, the biggest question that boggles course takers is, “How are we going to use this in real life?” This presentation will not only apply calculus to the real world, but to things that actually mean something, like falling in love and eating lots of food. By delving upon the three main aspects of introductory calculus — the limit, the derivative, and the integral — we’ll start off the year with a fun presentation that will actually help you use calculus in your everyday lives.

**September 30**: Charley Conley

**Title**: Epsilon Balls! More Epsilon Balls Than You Can Count

**Abstract**: A lighthearted look at epsilon and infinity. Topics include:

(1) Balls

(2) Counting

(3) Failing to Count Balls

**October 14**: Marcelo Disconzi

**Title**: Elementary notion of curvature.

**Abstract**: We shall discuss how to make intuitive notions of curvature (such as a plane is “flat” but a sphere is “curved”) mathematically precise.

**October 21**: Brian Simanek

**Title**: Big Problems

**Abstract**: Have you ever wondered what math research is? This talk will present some of the major unsolved problems that have guided mathematics research throughout history. Some of them have been solved, some of them are very close to being solved, and some of them are still wide open. The talk will require no advanced mathematics and all audiences are welcome.

**October 28**: Johanna Stromberg

**Title**: Much Ado About Knotting

**Abstract**: “What is your favourite kind of math?”

“Knot theory.”

“Yeah, me either”.

**November 4**: Zach Gaslowitz

**Title**: Voting!

**Abstract**: Learn about the role voting systems play in our government — and the role mathematics plays in voting.

**November 11**: Alexandr Kazda

**Title**: Information Theory

**Abstract**: Information theory studies how to tell apart various possibilities based on limited information. For example: How much information do you need to tell what is in a grainy picture? Or how many lab tests do you need to make to find which of the 100 soil samples contains high levels of mercury? Cn y rd ths sntnce, evn thgh sm lttrs r mssng? How to quantify uncertainty? We will try to answer these questions in the talk.

**November 18**: Professor Philip Crooke

**Title**: Can a Mathematical Model Predict Breast Cancer?

**Abstract**: Breast cancer is a leading cause of death among women in the United States. During this past year, it is estimated that there were 232,340 new cases and 39,620 deaths. In this talk, a mathematical model for individual breast cancer risk is constructed. The model is based on genetic and phenotypic information of the individual woman. The genetic information involves 3 genes (CYP1A1, CYP1B1, and COMT) that code enzymes in the estrogen metabolism pathway reactions. The phenotypic information includes hormone replacement therapy, BMI, family history, etc. The model is constructed from experimental data that has been collected for kinetic parameters along the estrogen metabolism pathway and epidemiological data for breast cancer frequency that was collected in Nashville and Europe. The model uses ordinary differential equations and non-standard maximum likelihood calculations to estimate parameters in the model. The mathematics is simple and straightforward. A web-based version of the model can be found at the URL: http://its-hcwnap48.its.vanderbilt.edu:8080/webMathematica/Math/AUC-N.html.