Computer Science · Research

Eric Zhang

CS student at Washington University in St. Louis, researching software testing, fuzzing, and applied ML. Building things that dig into how software actually behaves.

01

About

I'm a Computer Science student at Washington University in St. Louis, minoring in Entrepreneurship, expected to graduate in May 2030. My research centers on software testing and static analysis — I've spent several years at UT Dallas's Software Testing and Analysis Lab studying fuzzing internals and mining static analysis repositories for insights on developer practices. Outside of research, I build applied ML projects, from AutoML tooling to hardware-ML systems for diagnosing disease in microgravity.
02

Education

Washington University in St. Louis

Expected May 2030
B.S. Computer Science · Minor in Entrepreneurship

St. Mark's School of Texas

Graduated May 2026
High School Diploma, Cum Laude
GPA 4.44/4.0 (weighted) ACT 36/36 PSAT 1520/1520 National Merit Scholar
03

Research & Work Experience

High School Research Intern

June 2022 – Present
UT Dallas, Software Testing and Analysis Lab

Conduct research on fuzzing and static analysis tools with Prof. Shiyi Wei and graduate students; full-time (9AM–3PM, M–F) during summers.

04

Projects & Publications

Research manuscripts, published work, and hands-on builds spanning software testing, ML, and full-stack development.
Manuscript in prep

Mining Static Analysis Repositories to Inform Development Practices

Applying topic modeling and ML across 11 static analysis repositories to surface development-practice insights.

1st author · UT Dallas & U Paderborn collaboration
Manuscript in prep

Evaluating Fuzzing Internal Mechanisms via Internal State Variables

Analyzed internal state variables across four fuzzers to evaluate effectiveness; found internal mechanisms often diverge from expected behavior.

4th author · UT Dallas collaboration
Published, NHSJS

Smart Model Elimination Machine Learning (SMEML)

An AutoML method that prunes unlikely-best models by data features instead of brute-force evaluation, improving efficiency over exhaustive search.

1st author
NASA HUNCH Finalist

MTF: Diagnosing Disease in Space

An ML/hardware system to diagnose disease under microgravity conditions.

National Finalist
1st Place, United Hacks

Styled

A full-stack platform for clothing-size detection and personalized recommendations.

Hackathon winner
05

Achievements & Awards

NASA HUNCH National Finalist

April 2024

National Finalist, NASA HUNCH Science Fair, for "MTF: Diagnosing Disease in Space" — among 4,167 students from 571 schools nationwide.

UTD SPUR Computer Science Poster Award

July 2024

Awarded at UT Dallas's Summer Platform of Undergraduate Research for "Examining Power Schedules, Search Strategies, and Mutators in Fuzz Testing" (200+ participants annually).

United Hacks Hackathon — 1st Place

August 2023

Awarded first place for the project "Styled."