WAISI & XLab

WAISI Technical AI Safety Workshop Program

Most AI Safety communities introduce members who are interested in technical AI safety through the pipeline of Intro Technical Fellowship → Paper Reading Sessions → Alignment Research Engineer Accelerator program (ARENA) → Research Programs (SPAR, XLab SRF, MATS). However, most university groups have struggled with ARENA sessions for a few key reasons: the steep learning curve, significant time commitment, and lack of experienced TA's. The technical workshop program aims to address these issues by creating ARENA-styled workshops on AI Safety topics that focus on shorter, more manageable exercises, while still preserving the rigor of research-style work.

Transferable Adversarial Materials (TAM): Defeating ISR AUASs and LAWSs via Disruptive and Adversarial Material

Within the past decade, small portable Unmanned Aerial Systems (UASs) operated by individual infantry units have been demonstrated to be vital assets on the battlefield in intelligence, surveillance, and reconnaissance (ISR) roles as well as in one-way suicide attacks (loitering munition) and reusable bomb-dropping UASs. Many countries are attempting to integrate AI vision models into these systems to automate navigation and target identification and reduce vulnerability to jamming. We aim to demonstrate the effectiveness of a Transferable Adversarial Material (TAM), a deformable material which could be deployed in a variety of settings and deceive military-purpose computer vision models analogous to those being deployed in AUASs.

Our Research Catalog

Faculty Collaborators

Assistant Professor
Learning (robust) representations and generative modeling
Assistant Professor in the Department of Computer Sciences
Reinforcement learning and autonomous agents
Assistant Professor in the Department of Computer Sciences
Natural language processing and machine learning
Professor in the Department of Computer Sciences
Adversarial machine learning, privacy, and formal methods
Associate Professor in the Electrical and Computer Engineering Department
Theory and algorithms for deep learning with foundation models
Associate Professor in the Department of Computer Sciences
Algorithmic and theoretical foundations of reliable machine learning
Professor in the Department of Computer Sciences
Mobile security, adversarial ML, and systems security research
Associate Professor in the Electrical and Computer Engineering Department
Machine learning, coding theory, and optimization
Assistant Professor in the Department of Computer Sciences
Fundamentals of data-driven systems and machine learning
Professor in the Department of Biostatistics
Image analysis, computer vision, and ML in biostatistics
Assistant Professor in the ECE Department
Machine learning, statistical inference, and crowdsourcing
Assistant Professor in the Department of Statistics
LLM evaluations, high dimensional statistics, and deep learning theory