Annual MSU High School Mathematics Contest

The 47th Annual MSU High School Mathematics Contest was held virtually on Monday, April 19, 2021 and a virtual award ceremony took place on Wednesday, April 21, 2021. About 30 students competed, representing 6 high schools from Minnesota and Iowa. The contest is an individual and team competition. Awards are given to the top finishing teams and top individual scorers.

Team Awards
First Place Mankato West High School Lane Alfstad
Noah Gersich
Jack Roering
Jonah Zhao
Raymond Zhao
Second Place Mounds View High School Gavin Chang
Cynthia Dong
Elisa Guo
Sarvesh Sakthivel
Third Place Iowa City West High School Bill Chen
Miles Clark
Edward Li
 
Individual Awards
First Place Edward Li Iowa City West High School
Second Place Cynthia Dong Mounds View High School
Third Place Noah Gersich Mankato West High School
 
Top Scorers by Grade
12th Grade Noah Gersich Mankato West High School
11th Grade Cynthia Dong Mounds View High School
10th Grade Raymond Zhao Mankato West High School
7-9th Grade Edward Li Iowa City West High School
 
Team Top Scorer(s)
Edward Li Iowa City West High School
Kaitlyn Kirchner Lake Crystal Wellcome Memorial High School
Alex Emery Mankato East High School
Noah Gersich and Raymond Zhao Mankato West High School
Cynthia Dong Mounds View High School
Emerson Bengtson Zumbrota-Mazeppa Middle/High School
 

2021 Department of Mathematics and Statistics Research Month Activities

Conformal Deformation of Surfaces by the Extrinsic Dirac Operator

Wednesday, April 21, 9:00-9:50 AM

Location: https://minnstate.zoom.us/j/99847764630

Speaker: Katelyn LaPorte

Abstract:

The purpose of the APP is to survey the methods used by Crane and others to create conformal deformations of surfaces in 3-dimensional Euclidean space. His goal was to utilize this for applications in image processing. Here we will go into more detail of the mathematical theory behind his method including the not so familiar Quaternion-Valued Extrinsic Dirac Operator. We will also explain the integrability conditions of the conformal deformation problem, which can be reduced to an eigenvalue problem related to this Dirac operator. As it is a first order linear operator, it has high efficiency in discretization and surface curvature editing.


Sequential Probability Ratio Test and Experiment

Wednesday, April 21, 10:00-10:50 AM

Location: https://minnstate.zoom.us/j/966 4794 7613

Speaker: Brianna Klapoetke

Abstract:

The Sequential Probability Ratio Test (SPRT) is a method of testing simple hypotheses where the sample size is not determined in advance. In this talk I will describe the general process of using the SPRT, overview the theory that supports it, and describe how I applied it to data I collected to determine what alpha values people used to make their decisions in a simple game I designed.


Prediction of Heart Disease Using Bayesian Logistic Regression by Polya-Gamma Data Augmentation

Wednesday, April 21, 11:00-11:50 AM

Location: https://minnstate.zoom.us/j/939 3476 3132

Speaker: Zhenhan Fang

Abstract:

Heart disease is one of the most common diseases nowadays, due to number of contributing factors, such as high blood pressure, high blood cholesterol, and smoking. About half of Americans (47%) have at least one of these three risk factors. To reduce the risk of heart disease, healthcare industries generate enormous amount of data, and have been seeking an early diagnosis of such disease for many years. Many data analytics tools have also been applied to help health care providers to identify some of the early signs of heart disease. Many tests can be performed on potential patients to take the extra precautions measures to reduce the effect of having such a disease, and reliable methods to predict early stages of heart disease. In this study, Logistic Regression and Bayesian Logistic Regression are used to establish models to predict heart disease. We apply the Polya-Gamma data augmentation to our Bayesian Logistic model. We found that Bayesian Logistic model can provide a better performance, although it is more expensive than general Logistic model.


CLASSIFICATION OF CHESS GAMES

An exploration of classifiers for anomaly detection in chess

Wednesday, April 21, 12:00-12:50 PM

Location: https://minnstate.zoom.us/j/5074676277

Speaker: Masudul Hoque

Abstract:

Chess is a strategy board game with its inception dating back to the 15th century. The Covid-19 pandemic has led to a chess boom online with 95,853,038 chess games being played on January 2021 on one online chess site (lichess.com) alone. Along with the chess boom, instances of cheating have also become more rampant. Classifications have been used for anomaly detection in fields such as network security and online games and thus it is a natural idea to develop classifiers to detect cheating. However, there are no such prior examples of this, and it is difficult to obtain data where cheating has occurred. So in this paper, we develop 4 machine learning classifiers, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Multinomial Logistic Regression, and K Nearest Neighbour classifiers to predict chess game results and explore predictors that produce the best accuracy performance. We use Confusion Matrix, K Fold Cross Validation, and Leave One Out Cross Validation methods to find the accuracy metrics.

There is three phases of analysis. In phase I, we train classifiers using 1.94 million over the board game as training data and 20 thousand online games as testing data and obtain accuracy metrics. In Phase II, we select a smaller pool of 212 games, pick 8 additional predictor variables from chess engine evaluation of the moves played in those games and check whether the inclusion of the variables improve performance. Finally, in Phase III, we shall investigate for patterns in misclassified cases to define anomalous values.

From Phase I, the models are not performing at a utilizable level of accuracy (44-63%). For all classifiers, it is no better than deciding the class with a coin toss. K Nearest Neighbour with K = 7 was the best model. In Phase II, adding the new predictors improved the performance of all the classifiers significantly across all validation methods. In fact, using only significant variables as predictors produced highly accurate classifiers. Finally, from Phase III, we could not find any patterns or significant differences between the predictors for both correct classifications and misclassifications.

In conclusion, Machine learning classification is only one useful tool to spot instances that indicates anomalies. However, we cannot simply judge for anomalous games using only one method.


Count regression models for Covid-19 related deaths and overall deaths.

Wednesday, April 21, 1:00-1:50 AM

Location: https://minnstate.zoom.us/j/94358910903

Speaker: Manori Ampe Mohottige Dona

Abstract:

With the start of the ongoing Covid-19 pandemic, the number of deaths worldwide has increased in a considerable amount. Confirmed coronavirus cases in the United States increased significantly in the third week of March in 2020 as testing was made more rapid and overtook China’s on the 26th of March 2020, making the US the world’s most affected country by the coronavirus.

This study aims to determine the relationship of overall death counts and Covid-19 related death counts of five main states in the United States to the different age groups and gender over the period of one year. The data were collected from the government data repository, data.gov.

Poisson Regression analysis and Negative Binomial Regression analysis were used for model building purposes and total death count prediction. The k fold cross-validation and leave-one-out cross-validation were used to identify the best model.

The Negative Binomial regression model was identified as the best model compared to the Poisson regression model. According to the model, the most significant factor for total deaths and covid-19 deaths is gender. Texas has the highest significant contribution to the Covid-19 model and the most significant age group is 84 years or over.


Nomination for Membership

The following students are nominated by faculty of Department of Mathematics and Statistics for membership of American Mathematical Society for 2020-2021:
Moussa Abdoulaye Aboubacar, Eric Adu, Afrah Alhammad, Manori Ampe Mohottige Dona, Kayode Ayorinde, Aroni Basak, Shangyi Bi, Huyen Cao, Taeyoung Choi, Zhenhan Fang, Rachel Holmes, Masudul Hoque, Hans Kapend, Sujin Kim, Brianna Klapoetke, Abimbola Kolebaje, Katelyn LaPorte, Changhong Li, Bishal Maharjan, Charlie Moe, Tracy Morrison, Jasson Motzko, Ammishaddai Ogyiri, Franck Arnaud Olilo, Dong Young Park, Deanna Pautzke, Aninda Roy, Michael Schaefer, Nicholas Wagner, Erin Watt, Austin Whitcombm.

Congratulations!


Nomination for Membership

The following student is nominated by faculty of Department of Mathematics and Statistics for membership of American Statistical Assosciation for 2020-2021:
Zhenhan Fang

Congratulations!


Nomination for Membership

The following students are nominated by faculty of Department of Mathematics and Statistics for membership of Association for Women in Mathematics for 2020-2021:
Alison Millerbernd, Fatuma Abdulkadir, Serida Zosse, Morgan Olson, Sonja Kohout, Samantha Banwell, Erica Johnson, Joan Fuhrman, Samantha Doom, and Taylor Burke

Congratulations!


Student News

Congratulations to MSU team on winning second place in advanced Data Derby 2020.

Team members:Tania Hasanpoor, Abdelrahman Elkenawy, Nishchint Upadhyaya, Arlton Cox, Shuk-ping Wong

Advisor: Dr. Cyrus Azarbod

Special congratulations to Shuk-ping Wong, Applied Statistics graduate student and Vice president of Stats Club.


Student News

Congratulations to MSU team on winning second place in Data Visualization at 2020 MUDAC.

Team members:Taisuke Usumi, Nusrat Chaity, Lindsay Miller, Alison Millerbernd, Junsoo Seo

Advisor: Dr. Soumya Banerjee