Dr. Renee Henson examines the challenges of insuring AI-related harms, which evade traditional risk pricing and limit commercial insurance coverage options. Drawing parallels to nuclear energy, Dr. Henson proposes a government-backed insurance model similar to the Price-Anderson Act to ensure compensation for victims while fostering AI innovation. The framework outlines risk assessment, pricing strategies, and regulatory oversight to balance technological advancement with societal protection.
Professor Renee Henson is a Visiting Assistant Professor at the University of Missouri School of Law. Her scholarship focuses on the intersection of artificial intelligence and tort law, with articles in the Georgia State University Law Review (forthcoming) and the Missouri Law Review... Read More →
Monday March 10, 2025 10:00am - 11:00am CDT
201 ABC
Dr. Lindsey Jarrett is the Vice President of Ethical AI Services at The Center for Practical Bioethics. Her team focuses on ethical decision making in technology development and how it impacts healthcare organizations, those they serve, and society at large. Dr. Jarrett has extensive experience in program management, research, and executive consultation across the non-profit, academic, for-profit, and government sectors. As a social scientist she has worked to provide evidence-based interventions, practices, and processes to underserved communities as they interact with the numerous systems (e.g., healthcare, education, community programs) across their life span. Her work as a clinical researcher has also improved clinical decision making for providers in the areas of opioid safety, infection control, readmission prevention, maternal mortality, social determinants of health, and post-acute care. Her work across various sectors has created several collaborative initiatives and projects to positively impact the field of clinical research and assist stakeholders in implementing evidence-based practices across healthcare, education, and program development. Dr. Jarrett holds a PhD in Therapeutic Science from the University of Kansas and currently resides in Kansas City, Missouri.
This presentation draws upon survey data from 923 literacy teachers and identifies their perceived benefits and concerns of AI in literacy and language arts education. Benefits include time efficiency, creating and modifying content, brainstorming, evaluation and feedback, and preparing for the future. Concerns include unreliability, cheating and plagiarism, limiting student development, dependency, and a diminished human experience. Recommendations for pre-service and in-service teacher education programming and professional development will be provided.
Assistant Professor, Department of Learning, Teaching, and Curriculum
Dr. Sam von Gillern is an Assistant Professor of Literacy Education in the Department of Learning, Teaching, and Curriculum. His research focuses on digital literacies and how technology influences teaching and learning, including through artificial intelligence and video games.
This presentation explores the use of Convolutional Neural Networks (CNNs) for stock market prediction, focusing on how image conversion of OHLC data, model complexity, and label types impact forecasting accuracy. The study highlights the benefits of input normalization, deeper CNN architectures, and the superior performance of categorized labels over continuous return rates, offering insights into improving AI-driven stock forecasting.
Kihyung Kim, PhD is an Assistant Teaching Professor in the Management Department at the Trulaske College of Business, University of Missouri. He holds a PhD in Industrial Engineering from Purdue University and has extensive expertise in artificial intelligence, deep learning, business analytics, and supply chain management. Dr. Kim has published in journals like Clothing and Textiles Research Journal and Resources Policy, presented at conferences such as INFORMS Annual Meetings, and integrates AI tools like ChatGPT into business education to enhance analytics and decision-making skills.
Dr. Zhao's presentation explores how data analytics and AI are transforming the fashion industry. Using techniques like data mining and computer vision, computational approaches tackle challenges and provide actionable insights across the fashion value chain—from material sourcing to customer feedback. The talk introduces different types of data used in the fashion industry and highlights key research advancements related to fashion studies. It also emphasizes how integrating computational techniques with traditional fashion research can improve daily operations and support more sustainable practices. Additionally, the presentation offers valuable insights for fashion scholars and practitioners, guiding them on how to address these challenges and leverage these innovative methods to enhance their research and professional work within their unique contexts.
Dr. Zhao is an Associate Professor and Director of Graduate Studies in the Department of Textile and Apparel Management at the University of Missouri. Her research focuses on digital innovation and big data analytics, examining the impact of digital transformation in the global fashion supply chain. She has authored over 90 research papers published in international journals, book chapters, and conference proceedings, covering topics such as digital innovation, fashion data analytics, and sustainability. Her contributions to the field have been recognized with numerous accolades from international conferences and journals, including the 2022 Rising Star Award from ITAA, three Paper of Distinction Awards, the Most Cited Paper Award, the Best Impact Award, the Best Paper Award from Fashion and Textiles, and the HES teaching Award, among others.
9:30am CDT N201ABC Capstone OC2-Team2 Title: Exercise-Based Prediction of Hyper- and Hypo-glycemia in Youth with Type 1 Diabetes Project Description: Managing blood glucose levels in youth with Type 1 Diabetes (T1D) is challenging due to the complex relationships between glucose levels, exercise, insulin, heart rate, physiological attributes, and other factors. Many youth with T1D fear hypoglycemia after exercise and may overconsume calories beforehand, increasing the risk of hyperglycemia. These glucose fluctuations often discourage them from engaging in physical activity. Our project aims to develop a predictive model to classify the occurrence of hyperglycemia and hypoglycemia events within two hours after exercise. Our findings are primarily targeted toward clinicians and researchers to enhance understanding of glucose regulation in relation to exercise, with the potential to later assist youth and their parents in making informed decisions about safe physical activity. Master Students: Chun-Chih Chin, Xiling Guo, Jeong Wook Lee, Aakash Unnam 10:00am CDT N201ABC Capstone OL-Team3 Project Title:Born to Be Data-Driven: Pushing the boundaries on our understanding of preterm birth Project Description: Childbirth and infant health are critical areas of study that impact both individual families and our society. This project focuses on understanding factors associated with preterm birth using 2022 U.S. birth data. It aims to improve maternal and infant health outcomes by reaching healthcare professionals and the general public. Master Students: April Oga, Katarina Salcedo, Ramyaa Manoharan, Fatema Cheema, Germaine Knox
Thursday March 13, 2025 9:30am - 10:30am CDT
201 ABC
9:30am CDT N214B Capstone OL-Team2 Title:So you want to be a restaurant owner? Project Description: Our project aims to provide valuable insights into the restaurant industry by analyzing key business attributes across four major cities including Austin, Texas; Chicago, Illinois; New York City, New York; and Los Angeles, California. We will provide insights through the analysis of key factors including health inspection scores, location, customer reviews, time series analysis spanning the past five years, and additional information collected from census data. This data will be collected through web scraping, official city resources, and the Census Bureau. Our goal is to identify patterns for successful restaurant creation or expansion and offer actionable insights for restaurant owners to make informed business decisions regarding improvements. Our analysis will culminate in a business-focused dashboard which will serve as a tool to observe our findings and help restaurant owners optimize their strategies. Master Students: Reid Lawson, Kevin Sherer, Ryan Russell, Daniel Bassett 10:00am CDT N214B Capstone OC1-Team1 Title: Detecting Bias in Missouri News Data Using NLP and Machine Learning Project Description: This project focuses on detecting implicit bias in Missouri news articles using advanced Natural Language Processing (NLP) and machine learning techniques. The dataset consists of ~100,000 news articles provided from various sources, supplemented with bias-labeled datasets from AllSides and NewsMediaBias-Plus. Our approach involves comprehensive text preprocessing (tokenization, lemmatization, and POS tagging), feature engineering (TF-IDF, n-grams, sentiment scores), and embedding-based representations using transformer models (BERT, RoBERTa, and Sentence-BERT). Bias detection is formulated as a classification and clustering problem, leveraging DBSCAN, HDBSCAN, and K-Means for clustering, alongside dimensionality reduction techniques (UMAP, t-SNE, PCA). We fine-tune large language models (Claude, LLaMA 3.2, Mistral-7B) for classification, with model performance evaluated using precision, recall, and F1-score. Explainability methods ensure interpretability of bias indicators. This work aims to provide industry-relevant insights into algorithmic bias detection and media transparency at scale. Master Students: Gulli Atakishiyeva, Piyusha Modhave, Tarun Kumar
11:00am CDT N201ABC Capstone OL-Team4 Title: Student Success and Struggles: A Data-Driven Look at UMSL Project Description: This project aims to identify roadblocks and challenges faced by students at UMSL that may impact program retention and graduation rates. By analyzing institutional data alongside census data from the counties where students attended high school, the study will provide a more comprehensive view of the socioeconomic factors influencing student success. Machine learning models and statistical analysis will be used to detect at-risk students early, allowing for targeted interventions. The goal is to provide actionable insights to improve student support services and overall academic success. Master Students: Brooke Dustman, Emma Krummenacher, Val Bostick 11:30am CDT N201ABC Capstone OL-Team5 Title: PRISM: Platform for Real-Time Insights & Strategic Marketing Project Description: Businesses struggle to create effective, localized marketing content due to the lack of real-time insights into regional trends, audience behavior, and industry shifts. Our project addresses this gap by developing a marketing recommendation and content generation platform that provides localized, industry-specific insights for technology and healthcare businesses in Seattle, WA and St. Louis, MO. By leveraging real-time data, natural language processing, and machine learning, our platform uncovers emerging trends, consumer sentiment, and key industry developments to guide marketing decisions. These insights support automated content generation through our platform to help businesses create targeted, data-driven marketing materials that remain relevant, localized, and aligned with evolving consumer interests. Master Students: Molly Carmody, Catherine Smith, Yeleny Montero-Lopez
Thursday March 13, 2025 11:00am - 12:00pm CDT
201 ABC
1:00pm CDT N201ABC Capstone OL-Team1 Title:Wind Wise Solutions: Predicting Capacity and Longevity of Wind Turbines Project Description: The impending depletion of non-renewable energy resources threatens U.S. energy security, making the transition to renewable sources urgent; however, optimizing wind energy locations demands a delicate balance to maximizing production and sustainability. Wind Wise Solutions aims to predict wind turbine capacity and estimate the years until retrofitting is required due to physical damage for given additional locations across the contiguous United States, supporting the expansion of wind energy installations. The models will incorporate data from wind installations spanning from 2001 to 2020, along with environmental variables, ecosystem factors, and regulatory and financial constraints, to generate accurate predictions for new installations. This predictive approach will enhance operational efficiency and contribute to the long-term sustainability of wind energy infrastructure. Master Students: Bryce Tognozzi, Catherine Brockert, Jordan Domenick 1:30pm CDT N201ABC Capstone OC4 – Team4 Title: Analyzing Demographic Disparities in Columbia, MO, Police Department Traffic Stop Data Project Description: This study examines the infamous issue of how demographic factors such as age, gender, and geographic location influence the likelihood of being stopped by law enforcement in Columbia PD. By combining block level census data with 911 traffic stop records, we aim to identify patterns that could suggest whether certain groups of people are more likely to experience traffic stops. The study seeks to explore whether factors like time of day, location, or major events (such as sports games or public protests) have an impact on traffic stop rates. Our approach involves using geospatial mapping techniques to visualize traffic stop trends and applying statistical analysis to determine if there are any significant correlations between demographic groups and traffic stop frequency. Additionally, we will explore how local police department practices, such as shift changes or law enforcement policies, might contribute to these patterns. Ultimately, the goal of this research is to provide data science driven insights that can inform local traffic enforcement policies, improve transparency, and promote fairness within the community. Master Students: Syed Ali Hashmi, Karan Karthik, Deshan Wattegama
Thursday March 13, 2025 1:00pm - 2:00pm CDT
201 ABC
1:00pm CDT N214B Capstone OL-Team6 Title:Analyzing HCAHPS Patient Ratings analysis using census data. Project Description: Our aim of this project uses Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey ratings to understand how socioeconomic factors—such as income levels, education attainment, and insurance coverage, ethnicity —influence patient satisfaction and hospital ratings. By integrating U.S. Census data with hospital survey results, we want to identify trends that affect healthcare experiences and provide insights for improving hospital services. Master Students: Anjan Chatterjee, Matt Giguere, Akshat Gandhi 1:30pm CDT N214B Capstone OC3 – Team3 Title: GeoRealEdge: A Geospatial Decision Support System for Real Estate Project Description: GeoRealEdge is a geospatial decision support system that provides personalized property recommendations by considering socio-economic, healthcare, environmental, and other factors often overlooked by traditional real estate platforms like Zillow and MLS. Using Springfield, Missouri, as a pilot location, the platform uses a weighted scoring system that the end user can use to express their real estate priorities and preferences by incorporating accessibility metrics and housing attributes. The system gives tailored recommendations through an interactive interface and a customized property suggestion PDF, helping to better inform real estate decisions based on spatial and statistical analysis. Master Students: Yatish Devapati, Sai Teja Gullapalli, Rachel McMullen, James Simelus
