2022 Computer Science Summer Research Scholars

The Summer Research Scholars (SRS) program supports students participating in collaborative research supervised by W&L faculty. The program aims to encourage the development of research techniques within a particular discipline, to promote the active acquisition of knowledge, and to stimulate student interest in inquiry.

Here are the 2022  Computer Science Department’s SRS students,  their faculty supervisors and descriptions of their projects:

Professor Liz Matthews:

Sarah Martin, ’23:
-Sarah is implementing statistical analysis using nonparametric methods on data collected about gamer skill levels and features.
Danish Bokhari, ’24:≠
-Danish is studying design and data collection about video game enjoyment metrics for games containing procedural generation.

 

 

Professor Simon Levy:

Matt Stock≠ ’23
Matt and Prof. Levy are working on adding a RaspberryPi (“Internet of Things”) project  to enable a RealAnt robot to move about wirelessly and learn some interesting behaviors.

Professor Sara Sprenkle:

≠Grace MacDonald ’23:
Grace is developing new features and functionality for The Ancient Graffiti, a perfect fit for a computer science major with a classics minor!  She is improving AGP’s usability on mobile devices and will work on a variety of projects to make more graffiti available for public viewing.

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Armando Mendez-Anastasio ’24:
Armando is developing ChemTutor, an online chemistry tutorial to help students transition to college-level chemistry.  He will add new functionality to the site and work on making ChemTutor more easily deployed to the cloud.

≠Lakpa Sherpa ’25:
Lakpa is exploring how to automatically identify anomalous behavior in accesses to web applications.  He will be running automated experiments and analyzing lots of data.

 

 

Professor Cody Watson:

≠Abdelrahman AboEitta ’23:
Abdul and Professor Watson are working on a deep learning model that can identify and automatically fix security vulnerabilities in java source code methods.

 

 

Bennett Ehret, ’24:
Bennett and Professor Watson are working on a deep learning solution to automatically generate code documentation, specifically code comments, for source code methods that implement machine learning models.

 

 

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Mohamed Elhussiny

Mohamed Elhussiny’24 and Leyti Ndiaye ’26:
Along with Professor Watson, Mohamed and Leyti are building a variety of machine learning methods to automatically identify negative in-game behavior within the popular video game League of Legends.

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