Student Capstone Presentations

12:00 - 12:50 PM, Wednesday, April 22, via ZOOM
(contact Dr. Kerby for Zoom link)

Methods of Data Imputation Carson Howells
Imputation is the process of filling in missing values in data sets. This project is exploring which method of data imputation is best for different data types.
Deep Learning Classification of Retina Images: Detecting Diabetic Retinopathy Mikolaj Wieczorek
	Diabetic retinopathy (DR) is a common eye disease that causes vision loss for people with diabetes. It is one of the fastest growing causes of preventable blindness, and about 45% of Americans diagnosed with diabetes have some stage of DR. As the disease has no symptoms in early stages and treatments begin after the vision is already somewhat affected, early detection is of pivotal value to diabetic patients. The aim of this project is to build a deep learning classification model that could assist in early detection and severity grading of DR. The final results are obtained by implementing a Dense Convolutional Network Architecture model that yields 98% classification accuracy. Further study is being considered with application of larger training data and to deploy the final model as a web app.

Student Capstone Presentations

12:00 - 12:50 PM, Wednesday, April 15, via ZOOM
(contact Dr. Kerby for Zoom link)

Using Natural Language Processing to Analyze News Bias Bradley Erickson
As we live in the information age, finding reliable information becomes more and more difficult. All news sources contain bias, thus anything someone reads could be misinformation or interpreted differently. This research scrapes thousands of articles from many sources relating to 5 controversial topics: the impeachment, abortion, immigration, gun control, and climate change. We use unsupervised learning techniques to rank and filter out the top sentences by giving more weight to unbiased, more credible sources. This information will form an extractive-based summary. Finally, we compare the language used among 3 bias groups: left bias, no bias, and right bias. The research provides insights into the current bias state of news reporting.
Social Influence and the Freshman Experience Adam Funk
	We investigated the relationship between time at a university and who freshmen are most influenced by at that given time. The study took a subset of 1000 freshmen from the Winona State University class of 2023 and asked about different influence factors and changes in behavior over the course of 10 weeks. Different surveys were sent out at 1 week, 3 weeks, 6 weeks, and 10 weeks. Questions on who they were influenced by, both negatively and positively, were asked, as well as groups they were a part of and whether they felt they had changed as a person. We also attempted to monitor the change over time of specific individuals through an identification code, which was unsuccessful. However, summary statistics of each time point were successfully collected and reported.

Student Capstone Presentations

12:00 - 12:50 PM, Wednesday, April 8, via ZOOM
(contact Dr. Kerby for Zoom link)

Identifying Pulsar Stars Alexander Phillips
Pulsar stars, neutron stars that appear as rhythmic pulses of radio waves, are of great importance to astronomers as measuring devices. Scientists from two observatories in Australia and Germany compiled a dataset of 17,898 potential pulsar signals, recording data on each signal’s integrated profile and dispersion measure. This study considers variations on a logistic regression model to classify radio signals as pulsar stars or noise. The optimal predictive model considers the mean, standard deviation, skewness, and kurtosis of both the integrated profile and the dispersion measure, though a model with only the four best predictors also reaches a high level of accuracy.
Analyzing Filings Sentiment for Applications in Finance Marshall Will
	There has been increasing use of using sentiment in financial reports to create a better understanding of what is being read and how that can affect market prices. Analyzing sentiment for financial research has been around for a while, but over the past decades, there has been an increasing interest in looking at how changes in sentiment can affect market prices. Past research has shown that measuring sentiment can be used to generate Alpha and gauge a firm’s fundamentals. For my capstone, I looked at the sentiment of 10-K and 10-Q filings for publicly traded securities in the NASDAQ and NYSE from 1993 to 2018 to see if it is possible to predict if a security will rise in price based on the change in word sentiment in these public filings.

Student Capstone Presentations

12:00 - 12:50 PM, Wednesday, April 1, via ZOOM
(contact Dr. Kerby for Zoom link)

Data Analysis of the Arbitration Process in the MLB Alex Riles
The arbitration process in Major League Baseball is used to prevent holdouts and prolonged disputes. The ability to predict the outcome of the hearing will help the players and teams know where they stand. This could change the amount a player or team submits, so the outcome could turn in their favor. This project used Neural Network and Random Forests models to investigate whether a predictive model is worth using to predict the outcome of arbitration. Results indicated that Neural Network and Random Forests models can be used to predict the outcome of the arbitration process with a low misclassification rate.
Modelling Baseball Players’ Likelihood of Being Inducted into the Hall of Fame Connor Demorest
	I used logistic regression to model the probability a player will be inducted into the Hall of Fame. I found that my model has 96% accuracy when cross validating on players already in the Hall of Fame, and I discuss the implications of players who are still playing or not yet eligible. I determined what characteristics were most important to Hall of Fame voters and which were not. I identified several players who are not in the Hall of Fame yet and make a case for them to be inducted.

Department Colloquium

12:00 - 12:50 PM, Friday, February 28, Gildemeister 155

Refreshments served beforehand Gildemeister 135. 

Two Research Projects Birthed from

Curiosity, Recreation, and Joy

Dr. aBa Mbirika

Univ. Wisconsin—Eau Claire

 

This talk will center around two undergraduate research projects born from recreational math topics. The first project emerged from a connection between the Fibonacci sequence modulo 10 and astrology. The second project arose from noticing the magical and mystic golden ratio appearing as an eigenvalue of a certain tridiagonal real symmetric matrix. A cute connection between the two topics will be revealed at the end of the talk.

Student/Faculty Social/Trivia Night

Internship Presentations

12:00 - 12:50 PM, Wednesday, February 19, Gildemeister 155

Refreshments served beforehand Gildemeister 135. 

 

Data Science Internship: Tuohy Furniture Adam Clemens
I will describe the tasks that I performed and what I learned from my internship experience. I will also talk about the software and skills that I used on the job.
Internship at Fastenal: Fastbin Research Nina Horabik
	I worked on various projects to gain more insight about how Fastenal’s Fastbins work, and to help Fastenal improve Fastbin technology in future versions.