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Summer Research
There are plenty of opportunities for mathematics and other science students to conduct research over the summer.
Summer Science Research
Each summer, there are opportunities for Beloit College students to work with science faculty on their research or through the Pakula Biomedical Fellowship Program. These opportunities are either 4 weeks or 8 weeks in length. Students receive college credit and a stipend for their work. All participating students live on campus.
The Summer 2023 research opportunities are listed below. All of these projects are part of the Pakula Biomedical Fellowship Program and will run for 8 weeks from June 5 - July 28, 2023. Students will work with a faculty mentor to develop a research question and participate in the weekly 2-hr professional development seminar (1 unit of special project). Fellows will receive a stipend of $5250, to help offset the costs of summer tuition, and room and board. To review the available projects, click on the topics below. For more details, contact the principal investigator for each project directly. For general questions about the Pakula Biomedical Fellowship program, contact Dr. Tawnya Cary (caryt@beloit.edu).
Students interested in working in other STEM disciplines should check the respective discipline website or contact individual faculty members.
Projects
This project is part of the Pakula Biomedical Fellowship Program.
Prediction of heart disease at early stages is considered to be critical to manage and control it. Several techniques in machine learning (ML) and data mining can be employed to predict the presence of heart disease. The main goal of this research is to develop a model based on traditional ML and data mining such as Decision Trees, Support Vector Machines, Artificial Neural networks and Bayesian classifiers in order to predict the presence of heart disease and to assist physicians in maximizing accuracy for identification of the heart disease severity stage. In addition, we will also employ deep learning techniques (such as CNN, DNN, and RNN), Transfer Learning (TL), Federated Learning (FL), and compare their performance with the traditional ML techniques. In this research, we will use the database sets mentioned below for more than a thousand of instances that include important clinical, physical, laboratory and historical patient data: Coronary heart disease historical data | IEEE DataPort (ieee-dataport.org) Heart Disease Dataset (Comprehensive) | IEEE DataPort (ieee-dataport.org)
- Project Duration: 8 weeks (06/05/23 - 07/28/23)
- Prerequisite Courses: CSCI 204
- Preferred Courses: N/A
- Number of Student Positions: 2
Principal Investigator
Eyad Haj Said