The Q-Club, which is short for "Quantitative Club," is the departmental student organization, boasting over fifty members. Students speak on their research or share their internship and summer program experiences. Scheduled events take place approximately every other Tuesday 12:00-12:30pm with pizza and beverages being served. Q-Club will meet in Valentine 205-6.
Faculty members occasionally give talks as well, on topics ranging from "Math and Horror" to "An Outrageously Brief History of Mathematics."
If you know of students interested in giving a talk during the semester, please contact Ivan Ramler or Natasha Komarov. (Q-Club Archive Page)
1/31/17 Gibson Drysdale
Title: Game-Making in Europe
Abstract: Not many off-campus programs offered by SLU give you the opportunity to get Computer Science credit. I was able to study abroad in Copenhagen, Denmark for four months in the spring of 2016, where I was enrolled in a Game Development program. The program teaches the basics of making video games and gives in-depth exposure into the European Video Game industry through company visits and other hands-on experiences. The program also counts for CS 300+ elective credit, which I needed if I wanted to graduate on time. I'll be covering the Denmark program, what it's like to live in Copenhagen, and the experiences I had during while taking this eye-opening and generally awesome course.
11/29/16 Elsa Ficke
Title: Comparing Statistical Methods for Classifying Brain Cancer
Abstract: The purpose of this project is to classify brain cancer subtypes using statistical methods and biological data. The statistical algorithms that are used to classify the cancer subtypes are naïve Bayes, K-Nearest Neighbors, Support Vector Machines and Random Forest. Two types of data are used in classification, including gene expression data and copy number data. The research question is: how can we obtain the best classification accuracy for prediction of brain cancer subtype?
11/15/16 Michelle Gould from St. Lawrence University Career Services will discuss the following topics
What they can do for you, what our majors have done in the past. How to get in touch with alumni, etc. regarding both full time employment and internship opportunities.
11/1/16 Sejla Palic will give a talk on her SLU Fellowship project she worked on with Jessica Chapman last summer
Title: " Pareto Front App: Optimizing Decision Making Process"
Abstract: Pareto Fronts are one way to simultaneously optimize multiple criteria. A choice is eliminated if it is worse than at least one other choice. This is done by considering “all” possible solutions/options and eliminating inferior ones. An option is eliminated from contention if it is worse than at least one other available option. Once a Pareto front is identified, only equally “good” choices are left to choose from. At that stage, the decision about which single option is “best” becomes subjective; however, this app provides some additional tools to guide you to a defensible solution. Even though functions for finding Pareto fronts are written in R language and are available to the public, an average person wouldn’t be able to use them efficiently. The purpose of our project was to make a web app that would allow users without R knowledge to utilize Pareto Fronts functions on large data sets.
10/18/16 Holley James-Grisham will speak on his research
Title: " Using Stylometry to Classify Movie Script Genres with Each Other"
Abstract: Stylometry is the statistical breakdown of deviances in literary style among writers or genres (Burrows, 267). Stylometry can be used in situations where people come across books that they like, and search out the author in hopes that the author has written similar books. After days of searching they might realize that the author of the book is unintentionally unidentified. The book is from ancient Greece and the author’s page got lost over time. However, a statistical algorithm that uses the words from the book to correlate it to one specific author could be used to discover who wrote the book. The theory of stylometry will be tested using movie scripts from the web. The main question is whether or not different stylometry methods can be used to distinguish between different genres. Once the basic steps to filtering through the flooded data is successfully complete using computer coding software in R, the data will then be ready for analysis using a variety of stylometry techniques to represent each data set. Finally, a variety of numerical representations from the stylometry techniques will be used to create cluster plots to see the clusters of similar data sets. This research will allow writers to group different pieces of work to different time periods, authors, and possibly genres. The likeliness that research is successful will be determined by whether or not there is an accurate correlation between different data sets.
10/4/16 speaker Brian Coakley (CFO @ North Country Savings Bank),
Mr. Coakley will talk about his career path from a computer science major to becoming CFO at a bank.
9/20 16 Yuxi Zhang (Abstract Fellowship 2016)
Title: " User Interface and Usability Analysis for Importing Data in Statistics & Data Analysis Tools"
Abstract: As statistical data analysis software tools are used intensively and extensively during research and decision-making in industries, software engineers also never stop seeking more user-friendly interface designs to prevent cases where users fail to import external data into those analysis tools effectively. The format of the data and the size can make data import challenging.
My fellowship project focused on improving the data import process of a web-based tool “StatKey” with a more user-friendly interface. StatKey is used extensively by students taking Applied Statistics courses here at SLU and many other users worldwide.
9/6/2016 Taylor Pellerin (Abstract Fellowship 2016)
Title: "Play Select Like a Champion: Run/Pass Decision Making in FBS College Football"
Abstract: Before every single offensive play in American football, the coach has a choice to make: which set of instructions to give the team. Each play call is a decision that is tactically made. Depending on any number of conditions, a coach will have the team run or pass the ball, with the goal of advancing the field and ultimately scoring. After constraining and cleaning up a very large data set, I built a set of linear models which look at the success of certain factors in predicting efficient play calling. In this session, I will talk about the ways in which the data set was manipulated, the process of developing the models, as well as a change in run/pass decision-making that the results of the models point to.