Category: CMS-Mathematics & Statistics

GirlsGetMath@UCO

In Fall 2019, the Department of Mathematics and Statistics was awarded a grant to run a summer program called GirlsGetMath@UCO, mirroring a well-established program at The Institute for Computational and Experimental Research at Brown University. However, COVID-19 hit, and all summer camps were put on hold. We were hopeful for Summer 2021, but during the planning phase, we found that vaccination rates weren’t at a level high enough to safely run the program. But this time we were able to modify our plans and shift our goals a bit in order to introduce local high school students to math in a way they may not have previously imagined. One way we did this was by creating a summer virtual book club. The book club was facilitated by UCO mathematics education major Amanda Tingler. She met (virtually) with students four Sundays during the summer to discuss the book “Power in Numbers: Rebel Women of Mathematics” by Talithia Williams, Ph.D. During one of the book club meetings, Amanda also held a college Q&A session. In addition to the book club, Amanda and UCO mathematics major Chase Compton began developing interactive, online math modules for high school students. Their work, which is still in progress, can be found here: https://bit.ly/3xCewCE.

Adeola Obembe conducts undergraduate research with Dr. Hendryx

Senior undergraduate student, Adeola Obembe, and Dr. Emily Hendryx have teamed up with cardiologist Dr. Stavros Stavrakis at OUHSC to conduct research at the interface of mathematics/statistics and medicine. The goal of their work is to develop models predictive of patient response to a non-invasive treatment for paroxysmal atrial fibrillation (PAF) involving electrical nerve stimulation through an exterior part of the ear. Since Dr. Stavrakis has found that some, but not all, PAF patients see improvement given this nerve stimulation, Adeola and Dr. Hendryx are applying mathematical and statistical techniques to look for patterns in PAF patients’ electrocardiogram (ECG) data that may indicate whether a patient will actually respond to the treatment. With Dr. Stavrakis’ clinical expertise as a guide, Adeola is currently using computational algorithms to derive features from the ECG for use as model input—no small task when working with real patient data that can contain a variety of artifacts. The interdisciplinary team will continue to work on model construction over the months to come, in an effort to identify predictive ECG features and offer clinical decision support for future PAF treatment.

Alumni Highlight- Stephanie Walker

Stephanie with her poster at the 2020 Nebraska Conference for Undergraduate Women in Mathematics

Stephanie Walker graduated from UCO in May 2020, with a Bachelor’s degree in Mathematics. She started working on a research project with Dr. Britney Hopkins in the spring of 2019, that morphed into a group project that included Dr. Mike Fulkerson, Dr. Erin Williams, and two fellow undergraduates. Stephanie started the project by learning and teaching basic difference equations to the group. She then moved forward adapting a technique for determining the existence of solutions for even-ordered differential equations to even-ordered difference equations using techniques drawn from functional analysis.  Throughout the 2019-2020 academic year, she refined her work under a student Research, Creative, and Scholarly Activities (RCSA) grant. She has presented her work both regionally and nationally at Oklahoma Research Day, the Joint Mathematics Meetings, and the Nebraska Conference for Undergraduate Women in Mathematics. Stephanie is currently a graduate student and teaching assistant in Mathematics at the University of Kansas.

Stephanie pictured with the four UCO students who attended the 2020 Nebraska Conference for Undergraduate Women in Mathematics (from left to right: Shannon Yeakley, Ashlynd Heatherington, Stephanie Walker, Amber Young)

UCO Professors Collaborate on U. of Kansas NSF EPSCoR Grant

Drs. Robert Brennan and Sean Laverty are part of a multi-institutional NSF EPSCoR grant to research tick-borne diseases including Lyme disease and Rocky Mountain spotted fever. The four-year $3,921,229 grant, “Marshalling Diverse Big Data Streams to Understand Complexity of Tick-borne Diseases in the Southern Great Plains,”  is a collaboration among six universities in Kansas and Oklahoma, with the University of Kansas (KU) serving as the lead institution. Along with KU and UCO, the consortium includes Kansas State University, Pittsburgh State University, Oklahoma State University and the University of Oklahoma. According to the project abstract, major components of the research include assembling detailed large-scale datasets on the occurrences of different tick species, genomes of the ticks and pathogens, and environmental variation across the region. Dr. Brennan, biology professor, director of the Center for Interdisciplinary Biomedical Education and Research (CIBER) and associate dean of the UCO College of Mathematics and Science, serves as a Co-Principal Investigator on the grant. Dr. Laverty, associate professor of mathematics and statistics and CIBER member, will provide data analysis.  

 

Mathematics & Statistics Student Spotlight: Leif Nevener

Leif NevenerLeif Nevener is working on a second Bachelor’s degree, double majoring in Software Engineering and Data Science at the University of Central Oklahoma. Data Science is a new undergraduate degree program at UCO that is co-sponsored by the Department of Mathematics and Statistics and the Department of Computer Science. Leif is a member of the inaugural class of Data Science majors. For the past year, Leif has been working with Dr. Tracy Morris and a team of other students in Project SCHOLAR. SCHOLAR students complete statistical consulting projects for clients from both on and off-campus. Leif’s team worked on a project submitted by Deion Christophe who is a Firearms and Toolmark Examiner with the Plano Police Department. Mr. Christophe submitted data concerning casework completed by his department from 2017 to 2019. The students cleaned and summarized the data, searching for trends over time with respect to the number of cases and time to complete casework by submitting agency and case type. The students have also begun creating a Shiny app that the Plano PD can use to track and explore casework. The students presented their work in March at Oklahoma Research Day and were accepted to present at the National Conference for Undergraduate Research. Simultaneously, Leif was working on a research project with Dr. Jicheng Fu from the Department of Computer Science. Pictured above is Leif presenting his research at Oklahoma Research Day. Currently, Leif is working as a Computer Science Intern at Tinker Air Force Base and is on track to graduate in May of 2021.

Thomas Dunn Undergraduate Researcher in Mathematics

Thomas Dunn with research poster.Thomas Dunn is a sophomore at the University of Central Oklahoma majoring in mathematics. He started working on research with Dr. Tyler Cook and Dr. Emily Hendryx in the spring semester of his freshman year. Interested in applications of deep learning, Thomas has been rapidly learning the Python coding language to put these methods into practice. During his sophomore year, Thomas has been particularly focused on using autoencoders to differentiate between normal and anomalous electrocardiogram (ECG) beats. ECG data can provide a wealth of information to physicians about the health of a patient. However, even experienced clinicians may struggle to distinguish normal from anomalous ECGs in cases when the differences are subtle or distributed over long periods of time. Thomas’s autoencoder model takes steps toward developing an initial screening tool for ECG beats. An autoencoder (AE) is a machine learning model which learns to compress input data into a low dimensional vector representation and then reconstruct an approximation. The goal for such a model is to learn to reconstruct the input data as well as possible, which requires learning an effective representation for the data. An AE trained to reconstruct one class of data (normal ECG beats) will have a high error when trying to reconstruct data from another class (anomalous beats). His model uses reconstruction error to discriminate between different beat types which can then be assessed by a clinician or further classified using additional models. He investigates whether AE-based anomaly detection methods are viable tools in application to real ECG data, comparing the performance of the autoencoder against traditional classification methods. Thomas presented his results at this year’s Oklahoma Research Day at Southwestern Oklahoma State University. He also hopes to continue his research on deep learning by exploring the use of generative adversarial networks for 3D image generation.