Cupper Scholars

Are you interested in furthering your knowledge of computer science by conducting collaborative research with a faculty member at Allegheny College? If yes, then please consider applying for a fellowship supported by the Cupper Scholars program! Recognizing the noteworthy contributions of the late Dr. Robert D. Cupper, the founder of the Department of Computer Science at Allegheny College and a ground-breaking and innovative teacher and scholar in the field of computer science, this program provides students with mentoring, learning, and research opportunities. To find out more about the Cupper Scholars Program, please talk with the Chair of the Department of Computer Science. To see Professor Cupper teaching in Alden Hall, you can watch several segments in the above video!

Requirements

  • A minimum overall GPA of 3.0

  • A copy of your resume

  • A declared major or minor in computer science

  • Transcripts (a version from Self-Service is acceptable)

  • A one page personal interest statement that explains what type of research you would like to conduct and how it will benefit both the department and the discipline of computer science

Benefits Afforded to a Cupper Scholar

  • Experience conducting research with a faculty member, often resulting in released software

  • Mentoring from alumni who support the Cupper Scholars program

  • Compensation for three to four weeks of full-time work (up to 35 hours per week in April, May, and/or June)

  • Research and career advice from mentors during the spring and/or summer academic semesters

Submission Details

Please submit all of the required materials to the department via this form. Students who have questions about the Cupper Scholars program are also encouraged to contact the Chair of the Department of Computer Science. Applicants should submit all of their materials no later than March 26, 2021.

Cupper Scholars

Teona Bagashvili

Teona Bagashvili
2020

Topic: Automated relationship finding in COVID-19 research articles.
Outcomes: Developed and released a tool that uses machine learning techniques to find entity relationship pairs based on the automatically generated summaries of over 70,000 COVID-19 research articles.
Mentor: Janyl Jumadinova
Enpu You

Enpu You
2020

Topic: Automated experimental prediction of the likely worst-case time complexity of Python functions.
Outcomes: Developed and released a tool that performs automated doubling experiments to characterize the performance of a Python function.
Mentor: Gregory Kapfhammer
Lancaster Wu

Lancaster Wu
2020

Topic: Automated prediction of fault locations in Python programs.
Outcomes: Developed and released a tool that performs automated fault localization.
Mentor: Gregory Kapfhammer
Saejin Mahlau-Heinert

Saejin Mahlau-Heinert
2019

Topic: Automated assessment of source code and technical writing
Outcomes: Developed and released a tool that automatically grades source code and writing
Mentor: Gregory Kapfhammer
Carson Quigley

Carson Quigley
2018

Topic: Collecting YouTube data and performing sentiment analysis
Outcomes: A research survey on the BHEFT scheduling algorithm to efficiently perform YouTube data analysis
Mentor: Aravind Mohan
Xingbang Liu

Xingbang Liu
2018

Topic: Intelligent text extraction and summarization for an improved community initiative
Outcomes: Python system for automatically learning and extracting knowledge from MyMeadville interviews
Mentor: Janyl Jumadinova
Colton McCurdy

Colton McCurdy
2017

Topic: Mutation analysis of relational database schemas
Outcomes: Published a journal article and contributed to an open-source mutation testing tool
Mentor: Gregory Kapfhammer
Hanzhong Zheng

Hanzhong Zheng
2016

Topic: Text mining with reinforcement learning software agents using clustering approach
Outcomes: Distributed clustering system implemented in the Java language for extracting knowledge from data
Mentor: Janyl Jumadinova
Cody Kinneer

Cody Kinneer
2015

Topic: Empirically assessing the complexity of search-based test data generation
Outcomes: Open-source tool, freely available data, and two published papers
Mentor: Gregory Kapfhammer
Brandon Ginoza

Brandon Ginoza
2015

Topic: Modelling and predicting the efficiency of mutation testing techniques
Outcomes: R language package for predicting the speed of mutation testing
Mentor: Gregory Kapfhammer