33 citations · 56 across the 7 of their papers we have counts for
7 papers
Managing Project Teams in an Online Class of 1000+ Students
Nazanin Tabatabaei Anaraki, Taneisha Ng, Gaurav Verma +6
Team projects in Computer Science (CS) help students build collaboration skills, apply theory, and prepare for real-world software development. Online classes present unique opport…
Navigating the Safety Landscape: Measuring Risks in Finetuning Large Language Models
ShengYun Peng, Pin-Yu Chen, Matthew Hull +1
Safety alignment is crucial to ensure that large language models (LLMs) behave in ways that align with human preferences and prevent harmful actions during inference. However, rece…
VisGrader: Automatic Grading of D3 Visualizations
Matthew Hull, Vivian Pednekar, Hannah Murray +9
Manually grading D3 data visualizations is a challenging endeavor, and is especially difficult for large classes with hundreds of students. Grading an interactive visualization req…
REVAMP: Automated Simulations of Adversarial Attacks on Arbitrary Objects in Realistic Scenes
Matthew Hull, Zijie J. Wang, Duen Horng Chau
Deep Learning models, such as those used in an autonomous vehicle are vulnerable to adversarial attacks where an attacker could place an adversarial object in the environment, lead…
Robust Principles: Architectural Design Principles for Adversarially Robust CNNs
ShengYun Peng, Weilin Xu, Cory Cornelius +6
Our research aims to unify existing works' diverging opinions on how architectural components affect the adversarial robustness of CNNs. To accomplish our goal, we synthesize a sui…
LLM Self Defense: By Self Examination, LLMs Know They Are Being Tricked
Mansi Phute, Alec Helbling, Matthew Hull +4
Large language models (LLMs) are popular for high-quality text generation but can produce harmful content, even when aligned with human values through reinforcement learning. Adver…