Classifying Tissue Samples using Topological Data Analysis – Dr. Ross Sweet
Data science is a discipline that draws on a wide range of mathematical, statistical, and computational techniques, with the goal of applying them broadly in science, technology, industry. One class of techniques, topological data analysis, relies on the mathematical field of topology. In topology, we study geometric features that persist despite deformations to the original shape. Since data science problems can involve noisy or otherwise messy spatial data, topology provides a natural connection to data analysis.
In this project, we will investigate if one topological data analysis tool, persistent homology, can be used to classify microscopy images of tissue samples. In particular, we will determine if persistent homology can be an effective tool for classifying cancer tissues. Students will work in the R programming language to implement a classification algorithm while also learning the underlying theoretical math that drives persistent homology.
Using Data Science to Investigate Speech Therapy – Drs. Mark and Carolyn Brodie
Speech language pathologists (more colloquially known as speech therapists) use a variety of treatment techniques and strategies when they help patients to recover from stroke, brain injury, or other conditions. This project will explore the use of data science methods to try to discover which techniques are effective for which patients at what stage. We will use different algorithms to analyze data, provided by speech therapists, to see if we can discover patterns in the data that can predict which techniques are proving successful or unsuccessful, and when.
We will work with data gathered by speech therapists at On-With-Life (OWL) in Ankeny. We will document which patient variables they consider, which assessments instruments they use, and how they take into account which strategies have already been used with that patient (or “similar” patients), and how they fared. We will try as many data science algorithms as time allows, compare them, and study what can be learned from the data.
The project offers the possibility of doing research which is not only intellectually interesting and will stand out to future employers or graduate schools, it also might beneficially impact other peoples' lives (both patients and therapists). Prior programming experience will be helpful (e.g. either R or Python, or other languages).
Representation Counts: Latinx Numbers of Iowa – Dr. Molly Tun
This research experience will introduce students to the field of Ethnomathematics – the study of mathematics and culture. Students will consider the ways that math practices are products of their cultural frameworks and the ways that numbers transform and structure societies. Together, we will collect, interpret, and display numeric data and statistics of social importance regarding the Latinx experience in Iowa, in relation to national data trends. Students will receive training of the US Census Bureau database in order to locate and interpret specific data fields from the American Community Survey; this survey released new information this past semester regarding census information regarding identity factors.
In connection with this database, we would identify variables of interest (such as language, education, salary) and work to collect and create visual representations of the target data. This numeric analysis would be a useful resource for public policy makers and those who work on issues of identity, representation, and underserved populations. Our project will help put students in conversation with prominent community members and advocacy groups. This research would reflect on numeric discrepancies between different social groups in our communities in the hopes of working towards more equity in higher education and increased representation of minoritized groups in various sectors of society.