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3rd Annual Data Science Day

3rd Annual Data Science Day

April 11th, 2023 | 1:30PM - 5:15PM

Snell Hall 1102 & Ogden College Hall 1006

Schedule Overview

1:30PM - 4:00PM

General Session

Snell Hall 1102

For a more detailed overview of the General Session, please scroll below.

4:15PM - 5:15PM

Plenary Talk

Ogden College Hall 1006

Presenter: Dr. Mahdi Yazdanpour, Assistant Professor, Northern Kentucky University. For more information, please click the following link: https://www.linkedin.com/in/mahdi-yazdanpour/.

Title: AI-Based Neuroprosthetics: Mind-Controlled Prosthetic Arm with Hybrid Brain Computer Interface

Abstract: The rapid advancements in the fields of neurotechnology and robotics have paved the way for the development of brain-controlled systems. This emerging technology utilizes the power of the human brain to directly interface with electromechanical devices, enabling remarkable levels of control and interaction. Dr. Yazdanpour and his research team at the Mechatronics Research Lab have developed a Hybrid Electroencephalography (EEG)-Based Brain Computer Interface (H-BCI) using multiple deep learning techniques, alongside a Mind-Controlled Prosthetic Arm. 


Detailed General Session

Location: Snell Hall 1102

Presenters: Davaughn Johnson

Faculty Advisor: Dr. Julia Shaedon

Abstract: Data Mining and Scarping are principles in Data Science that have created important tools. Some of these tools are user data scarping, extraction of data, and putting data into classes. In this presentation, we will go over how these tools have affected the world market and the potential impact the tools could have on companies' ethics.

Presenters: Jack Galloway

Faculty Advisor: Dr. Jason Polk, Department of Earth, Environmental, and Atmospheric Sciences

Abstract: This project focuses on the development of an advanced water resources monitoring dashboard, emphasizing the creation of an integrated, interactive platform for real-time data analysis and visualization. Situated in Bowling Green, Kentucky, where karst features present unique challenges, the dashboard serves as a vital tool for understanding and managing water resources. Leveraging NexSens/Onset Data Loggers and cloud-based platforms like WQDataLive and Hobolink, extensive data on water levels, rainfall, and water quality is collected. An API facilitates seamless integration of this data into the dashboard, which is further enhanced through Python scripting in ArcOnline for automated data analysis. Individual dashboards for each monitoring site are then integrated into a unified interface using ArcGIS Experience, ensuring accessibility and user engagement. Through this project, advancements in water resource monitoring and management are achieved, highlighting the pivotal role of the comprehensive dashboard in addressing critical challenges in karst areas. This innovative approach contributes to improved decision-making and community resilience in the face of water-related hazards.

Presenters: Nima Esmaeilzadeh

Faculty Advisor: Dr. Nahid Gani, Department of Earth, Environmental, and Atmospheric Sciences

Abstract: Geological mapping is crucial for mineral exploration, particularly in identifying lithological units, alterations, and critical minerals. Traditional methods are often limited by accessibility, cost, and environmental factors. This research demonstrates the effectiveness of leveraging multi-sensor remote sensing satellite imagery as a cost-efficient alternative for detailed mapping of mineral alteration zones. Focusing on the Mountain Pass District in California's eastern Mojave Desert, the research addresses a gap in comprehensive multi-satellite multispectral remote sensing analysis of the geologic framework related to carbonatite ore deposits and hydrothermal alteration zones. Using data from Landsat-9, Sentinel-2, and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), the study successfully mapped iron-oxide and ferrous-bearing minerals, as well as hydrothermally altered rocks like clay minerals. Advanced machine learning algorithms such as Support Vector Machine and Random Forest significantly improved the classification and mapping of hydrothermal alteration minerals, demonstrating their effectiveness in geological exploration. As this research enhances our understanding of multispectral satellite data in mineral exploration, it establishes a robust framework for the future identification and characterization of mineral-rich zones. More broadly, the advancements heralded by this study are instrumental in reducing the United States' dependency on foreign mineral sources, thereby enhancing national security and supporting the sustainable development of crucial technologies and industries.

Presenters: Beth Rogers, Senior Manager of Data Science, Bath & Body Works

Abstract: Join Beth Rogers, head of Data Science at Bath & Body Works, as she takes us through her unique career journey. Learn what it's like to be a scientist in a top marine biology lab, brain imaging research center and cave research program. See how Beth translated her academia and scientific skillsets to a successful career as a data scientist and leader in corporate industry. Find out what types of business questions are being asked and techniques data scientists are using every day to answer them. Discover how you can start your own data science journey and begin your career path ideation.

Presenters: Shake Ibna Abir

Faculty Advisor: Dr. Richard Schugart

Abstract: Acute ischemic stroke, originating from cerebral artery blockage, often culminates in long-term disability or death. Identifying abnormal, or at-risk tissue in patients typically involves employing Computed Tomography Perfusion (CTP) imaging in patient-specific assessments. Perfusion images help identify areas of abnormality, including irreversibly-damaged tissue and tissue at risk, for acute brain stroke patients. The most significant physiologic and pathophysiologic estimated perfusion parameters are Cerebral Blood Volume (CBV), Cerebral Blood Flow (CBF), Time to Peak (TTP) and Mean Transit Time (MTT). The recent advancements in medical imaging have been propelled by the breakthroughs in Deep Learning (DL), particularly through the advent of Temporal Convolution Neural Networks (TCNN). This study presents an investigation into the calculated perfusion parameters extracted from Time Attenuation Curve (TAC) data for an initial patient using TCNN, Convolution Neural Networks (CNN), and Recurrent Neural Networks (RNN). A comparison of the methods will be presented as the corresponding root mean square errors (RMSE) for TCNN, CNN, and RNN are 0.05, 0.07, and 0.38, respectively.

Presenters: JC Watkins, Kentucky APEX Accelerator, Kentucky Science & Technology Corporation

Abstract: This non-research talk is designed to assist data scientists in considering what to do with the skills and abilities that have been developed while attending and working at WKU.  Graduation and post-graduation opportunities abound for data scientists. As a data scientist, you'll have a plethora of job opportunities across various industries such as tech, healthcare, finance, retail, etc. You could work as a data analyst, machine learning engineer, AI researcher, or data engineer, depending on your skills and interests. 

You'll have the chance to grow in your career by gaining experience, acquiring new skills, and taking on more challenging projects. This could lead to promotions, higher salaries, and leadership roles within organizations or you may create your own organizations. I am here today to let you know that job opportunities exist inside the federal government and with firms that service the various departments of the federal government. I am not a recruiter but I do want to let you know about some of these opportunities.
Why? I have a job that assists small companies (many of which are one-person firms) respond and try to secure contracts with Federal, State, and Local governments. I see a lot of need and  “dataholics”  such as yourselves are the supply.  I am going to briefly describe parts of the government and some of their interests and ways you can further your research interests.

Presenters: Charlie Yielding, Data4All Podcaster

Abstract: Data will be trusted in the workforce of the future, but what about now? In this non research presentation, Charlie Yielding will share what it's like in the workforce in 2024 while giving examples through data storytelling.  At the end of this conversation, you will understand the dilemma you currently face, what the data says, and will leave with actions to take in order to prepare for your dream job.

Presenters: Luna Asbell, Katie Isaacs

Faculty Advisor: Dr. Jeremy Maddox, Department of Chemistry

Abstract: GaussianTools is a Mathematica package for processing data produced by the electronic structure software package Gaussian.  In this presentation we demonstrate an application of GaussianTools for visualizing the point group symmetry elements and operations of molecular structures.  We envision that instructors and students will be able to design interactive Mathematica notebooks to enhance their understanding of molecular symmetry.

Presenters: Cameron White, Dollar General Corp, WKU alum

Abstract: Often in academia, the size of datasets under study are “just right”: large enough to glean many insights from, yet small enough to be worked on with a single desktop PC. In this talk, experienced data scientist Cameron White will discuss various approaches on how to handle those situations when the datasets are not just right. At some firms, the datasets aren’t really that large and comprehensive, thus hampering some of the questions that can be asked. At others, there is too much data, which can be very difficult to wrangle on a single machine. 

Presenters: Cheyla Ferguson

Faculty Advisor: Dr. Amy Brausch, Department of Psychological Sciences

Abstract: Introduction/problem: Individuals with disordered eating behaviors face a higher risk of suicidal ideation, compared to the general population (Munick, 2023). Previous research has shown certain facets of eating disorders can predict suicidal ideation (Joiner et al., 2022). The Eating Disorder Inventory (EDI) includes multiple traits common in eating disorders such as low self-esteem, interpersonal alienation, and perfectionism. Considering the EDI incorporates questions about food but also about general thoughts and feelings, this study aims to uncover which subscale of the EDI is the best predictor of suicidal ideation. Based on previous research, we hypothesized that a subscale specific to eating disorders (i.e., interoceptive deficits, body dissatisfaction, or bulimia) will best predict suicidal ideation (Perkins & Brausch, 2019; Perkins et al., 2021; Rufino et al., 2018). 

Procedure: The data for this study was collected from a longitudinal study of high school and middle school students in Kentucky from 2015 to 2017. However, for the purposes of this study, we will only be using data gathered at baseline (2015). Of the 456 middle and high school students (ages 11-16) assessed, 54% were in middle school, 53% identified as female, and 85% identified as White/Caucasian. Participants were asked about eating disorder traits (EDI), suicidal ideation (SIQ), and demographics.
Results: A forward stepwise linear regression was run in SPSS and used to identify the best predictors of suicide ideation using the EDI. Emotion dysregulation and body dissatisfaction were shown to be significant predictors of suicidal ideation in this sample.
Conclusions and implications: Contrary to our hypothesis, emotion dysregulation (not specific to eating disorders but a more general mental health symptom) was the best predictor of suicidal ideation. This is consistent with previous research on youth (Brausch, 2018). Body dissatisfaction is a symptom more specific to those with eating disorders and is also shown in previous research to predict SI in adolescents (Kim, 2009). Therefore, adolescents with eating disorder traits are at greater risk, especially individuals who score high on these two subscales. Results show suicide is a complex phenomenon as multiple facets are associated. Future research should investigate why these traits occur in adolescents in the hopes of reducing the likelihood of development of both disordered eating behaviors and suicidal ideation.


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 Last Modified 4/10/24