Category: Student Successes

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:

Danish Bokhari, ’24:

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.

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.

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.

Professor Taha Khan:

Jack Bosco ’24:

Jack is working on better understanding how Internet users perceive what should happen to their data post bereavement. Jack is developing a user study over the summer.

Mohamed Elhussiny’24:

Mohamed  is working on a project that involves analyzing at GitHub repositories to understand the significance of class methods are semantically similar and investigate their security and usability implications. 

Abdelrahman AboEitta ’23, CS Major
Abdel developed the latest version of ChemTutor (https://chemtutor.wlu.edu/), an online tutorial to help prepare students for college-level chemistry.  ChemTutor was developed by faculty and students at 4 small, liberal arts institutions and is funded by the Associated Colleges of the South.  Beyond developing new functionality for instructors, Abdel used Docker to deploy ChemTutor on Amazon Web Services so that it is easier for other institutions to deploy.

Sujana Basnet, ’23, CS Major
Irina Koleva, ’22, Neuroscience Major
Sujana and Irina completed a survey of academic research on video game experience categories, designed and conducted a user study to collect data based on the survey, and ran statistical analysis of the user study data. Also, they began work on an academic paper to be submitted to an appropriate venue in the Fall.

William Xue ’24
Will’s project name was:  Cloudsweeper: A Tool for Personal Cloud Management. The personal cloud is a convenient and affordable way to retain and share files over time. However, as time passes, some files lose their relevance. Crucially, some files that are no longer useful may still contain sensitive information, creating risks due to data breaches, lost devices, and account takeovers.  During the Summer of 2021, Will used his Python and web design skills to work both on the front and backend of the design of Cloudsweeper, a web application which incorporates machine learning to highlight potentially sensitive and useless files in individuals’ cloud accounts. The current version is live at: https://cloudsweeper.app.  Currently, the app only supports the Google Drive cloud platform. Will plans to continue work with the team to integrate the app with support across multiple cloud platforms and conduct future research studies by using the app as a data collection tool.

Billy Tobin ’24
Billy’s project name was : An Empirical Evaluation of Method Signature Similarity in Codebases.  Modern programming languages have revolutionized the way in which software developers design and develop computer programs. These languages provide individuals with user-friendly capabilities that enhance the productivity of developers while ensuring minimal code redundancy. One such feature of programming languages is method overloading.Billy spent the summer of 2021 empirically evaluating the pervasiveness of overloaded methods in large-scale repositories understanding their relationship over time, as repositories grow. Based on the initial results, Billy plans to extend his work to understand the usability aspects of overloaded methods and determine where they may have correctness, security, performance, and complexity implications.