Alumni Behavior Data Analysis

Exploratory data analysis beginning in the fall semester to help the Miami University Devleopment team understand the donation behaviors and engagement in university-sponsored activities of alumni. Continuing into the spring, an interactive dashboard and heat map was developed as a tool for the team. 

Student Team: Lydia Carter, Ryan Estep, Bretheo Danzy, Aidan Johnson, and Kamil Sacha

Faculty Mentor: Dr. Karsten Maurer

Patient Encounter Forecaster

This project spanned both fall and spring semesters. Advanced forecasting on scheduled patient surgeries on holidays to improve staffing accuracy. 

Student Team: Colin Amy and Elena MacDonald

Faculty Mentor: Dr. Allison Jones-Farmer 

Merchandise Sales Trends

Analysis and data visualizations to help a professional sports team understand trends and increase revenue in the area of merchandise sales at home events.  

Student Team: Abbie Klinker, Alexis Morris, Alex Erisey, Ben Schweitzer

Faculty Mentors: Dr. Tom Fisher

Predicting Risk of Staff Behavior

Using staff behavior data, external event data, and risk scores of a financial institution to create an interactive model to evaluate the true risk of staff behavior. 

Student Team: Darek Davis, Karan Gupta, Cameron Devitt, Kamil Sacha

Faculty Mentors: Dr. Fadel Megahed

Payment Processing Strategy

Using a Cox Proportional Hazards model for a payment processing organization, the team helped create a new recycling strategy for credit/debit payments to mitigate costs and improve the approval rate. 

Student Team: Sophie Armor, Kaelyn McDonald, and Mitch Fairweather 

Faculty Mentor: Dr. Maria Weese

Blockchain-based Management System

The development of a blockchain-based supply chain management system for pharmaceutical drugs that included a front-end, API, and smart contract to verify the tracking of inventory for patients and distributors. 

Student Team: Cameron Devitt, Andre Le, Karan Gupta, and Patrick Liem 

Faculty Mentor: Dr. Arthur Carvalho

Warrenty Claims Analysis

In-depth data analysis and the creation of a data visualization dashboard to help a power equipment company evaluate the probability of a fraudulent claim. 

Student Team: Darek Davis, Abbie Klinker, Alyson Wohlleber, Charlie Yoo

Faculty Mentor: Dr. Peter Nguyen 

Inhibitor Design

Machine learning models to find potential drug compounds to fight antibiotic-resistant secondary infections with reseachers and graduate students in the Chemistry and Biochemistry departments at Miami University. 

Student Team: Mitch Fairweather, Aidan Sturgill, Amy Hu, Caitlyn Thomas

Postdoctoral Fellow: Dr. Zishuo "Toby" Cheng

Faculty Mentors: Dr. Michael Crowder, Dr. Maria Weese, Dr. Waldyn Martinez

Perscription Demand Forecasting

In order to improve planning for warehousing and distribution at a pharmaceutical company, this spring semester student team conducted advanced forecasts at a granular level. 

Student Team: David Black, Palmer Holcomb, and Nikki Weaver

Faculty Mentor: Dr. Allison Jones-Farmer

Grocery Online Ordering

CADS student team in the spring created exploratory data visualizations to help a large grocery store chain improve online order fulfillment accuracy. 

Student Team: Sophie Armor, Zander Korach, Nick Romeo, and Ruoning Wang

Faculty Mentor: Dr. Maria Weese

Consolidating Employee Information

In the spring, a student team created a chatbot for a healthcare-products company's employees to efficiently find company-wide practices. Their work helped employees save time at work and improve standardization throughout the company.

Student Team: Nick Gerard, Feiyu Wang, and Steven Yu

Faculty Mentors: Dr. Vaskar Raychoudhury and Dr. Md Osman Gani

Blight Removal Impact

Three years ago CADS worked on its first experiential learning project with the Butler County Landbank to assess the impacts of the program. CADS is now working to update the findings using additional data and new techniques discovered since that first project.

Student Team:  Joey Hart, William Holmes, Molly O’Donnell and Nathan Soundappan

Faculty Mentors: Dr. Allison Jones-Farmer, Dr. Mark Morris and Dr. Greg Niemesh 

Visualizing Adverse Birth Outcomes

In the fall, a team started building an interactive data visualization application for the Butler County Department of Health using infant mortality data. This project will help the department better disseminate information to all of its constituents. Work on this project will continue into the spring semester.

Student Team: Bri Clements and Nichole Rook

Faculty Mentor: Dr. John Bailer

Part Sales Forecasting

This team analyzed part-sales and whole good-sale data in the fall for a company to improve its parts sales forecast. They were able to build a model that resulted in a 30% improvement upon the current model used by the company.

Student Team: Hannah Baney, Palmer Holcomb, Thomas Lees and Yujin Liu

Faculty Mentor: Dr. Tom Fisher

Customer Segmentation

This project, completed in the fall, centered around creating customer segments using cluster analysis. The team used a large survey that the company had already analyzed to offer fresh perspective on the data.

Student Team: Sophie Armor, Liz Kohn and Beatriz Su

Faculty Mentor: Dr. Maria Weese

Health Impact on Children of Incarcerated Parents

In the fall, this team analyzed health data for a managed care company. The team specifically looked at the health impacts of incarcerated parents on children. Insights provided by the team will help the company make data-driven decisions about future interventions for that population.

Student Team: Julia Bragg and Sarina Sangal

Faculty Mentor: Dr. Analisa Packham

Economics World Bank Visualization Project Part 2

This team continued the work from the previous fall by extending the filtering capabilities of its visualization and compiling a database with data sources such as FRED and World Bank that automatically provide up-to-date data.

Student Team: Dylan Nguyen, Beatrize Su, Alison Tuiyott and Xiaoxiao Zhao

Faculty Mentor: Dr. Fadel Megahed

Call Forecasting Center

In the spring, this team developed a forecasting model to better predict the client’s call center volume from week to week in order to optimize their staffing levels and their marketing budget.

Student Team: Alec Feeman, Brant Imhoff and Nathan Soundappan

Faculty Mentors: Dr. Tom Fisher and Dr. Alison Jones-Farmer

Ecological Data Paper

Nicholas Jerdack worked with Miami’s Ecological Big Data Initiative (MiEBDI) to conduct an in-depth data quality and completeness check of lake temperature data, which will be published in a scientific journal.

Ecological Big Data Archive

Kai Li worked with Miami’s Biology Department to archive long-term research data as part of the Environmental Data Initiative (EDI), a data sharing program funded by the National Science Foundation (NSF).

Advanced Manufacturing Project

In the spring of 2017, the team came together tackle a problem for an Advanced Manufacturing firm. This firm was having difficulties determining the optimal number of employees to schedule. To provide a solution, the team used historical data and regression techniques to predict the number of employees to schedule each day. 

Student Team: Hayden Dygert, Mike Macey, Ryan Miller and Alison Tuiyott

Faculty Mentor: Dr. Waldyn Martinez

Cristo Rey Project

This team was tasked to solve a transportation problem for Cristo Rey High School in the spring of 2017. Every week, Cristo Rey High School sends their students to work at an internship in the Cincinnati area. The team was tasked with minimizing their transportation costs by either optimizing their routes or suggesting a cheaper alternative than the current mean of transportation.

Student Team: Bri Clements

Faculty Mentors: Robbyn Abbitt, Dr. Lee Biggerstaff, Dr. Alison Jones-Farmer and Dr. Fadel Megahed

Phi Delta Theta Project

In fall 2017, this team took on the task of deciphering if the current database system used by Phi Delta Theta's national headquarters met their business needs. From there, the team worked with the fraternity to build specialized reports generated by the database as well as trained the employees to use the more advanced features of the software. 

Student Team: Sarah Armstrong and Wyatt Butcher

Faculty Mentor: Dr. Doug Havelka

Insurance Social Media Project

This team worked to gather tweets about an insurance company by interacting with the Twitter API. Then, the team categorized the tweets by using an algorithm. They were tasked with trying to better understand the public perception of the brand, how customer complaints were handled and responded to by their employees, and in general how often they were being tweeted about. Fall 2017.

Faculty Mentor: Dr. Fadel Megahed

Economics World Bank Visualization Project

This team designed an interactive, exploratory visual of the World Bank data for the Economics Department in the fall.

Student Team: Bri Clements, Adam Levitt and Seth Levitt

Faculty Mentor: Dr. Fadel Megahed

Butler County Land Bank Project

This project saw CADS continue work on the Butler County Land Bank project from the previous semester. The Butler County Land Bank is a nonprofit organization that takes title of abandoned, blighted residential properties for demolition and reutilization. In Fall 2016 this project team worked with data from local government offices to discover what impact the Land Bank had on the local community. The previous year, the team was able to show that the Land Bank has a positive economic effect on local housing. In the fall, work was continued to discover if there is a relationship between local crime rates, 911 calls, and the removal of blighted properties.

Student Team: Riley Cook, Garry Green, Kaan Koseler, Ryan Miller, Nicole Peters, Kevin Schrock and Charlotte Smith

Facility Mentor: Dr. Allison Jones-Farmer, Dr. Mark Morris, Dr. Melissa Thomasson and Dr. Maria Weese

Healthcare Claims Project

This team consulted for one of the largest rehabilitation hospital systems in the country in fall 2016. They conducted a text analysis working with both structured and semi-structured data in R. At the conclusion of the project, the classification method was able to successfully categorize over 90% of the records that were given. The client was delighted at the algorithm and code that helped his team categorize and understand why Healthcare claims were being rejected.

Student Team:

• Hayden Dygert
• Mike Macey
• Ryan Miller
• Alison Tuiyott

Faculty Mentor:

• Dr. Fadel Megahed