Trustworthy Research for Understandable, Safe, Technology

We are applying state of the art computer vision approaches and explainable machine learning techniques to support wildlife conservation efforts. This interdisciplinary project bridges machine learning with ecological science to create transparent decision-making tools.

This project investigates how large embedding models encode geographical and temporal information, with implications for understanding cultural biases and historical shifts in AI systems.

This study explores how voice-based, conversational LLM agents can function as “research translators” in interdisciplinary collaborations.

The Aligned Machine aims to builds a benchmark of human-aligned similarity by comparing AI model outputs with human judgments of meaning, using an interactive platform designed to support public engagement with AI research.

This project addresses gaps in responsible AI for digital health by developing explainable and adversarially robust machine learning models for sleep monitoring.

We aim to turn the “bug” of adversarial attacks into a feature for improving AI transparency, trustworthiness, and alignment with human goals. In this project, we are developing an open-source adversarial probing platform for LLMs.