Leif 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 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.