Ethical AI is often discussed as principle and rarely delivered as something a person can actually use. Our work in this program is applied: tools and methods aimed at anyone who needs to judge whether a claim, a citation, or a generated answer can be trusted.
That audience is deliberately broad. Students, journalists, clinicians, librarians, and researchers face the same underlying problem, and it does not require a technical background to matter to them.
A verification tool for citations and sourced claims, built for general use rather than only for academics. AI systems produce references that look correct and are not; CiteCheck exists to make checking them fast enough that people actually do it.
Research at the undergraduate and graduate level. Agent systems are increasingly used to plan syntheses, screen candidates, and read literature at scale — which makes their failure modes a chemistry problem as much as a computing one.
Experimental and computational work, emphasizing problems where a result changes practice rather than only the literature.
Process, reaction, and systems engineering — scaling what works at the bench into something that survives production constraints.
Multi-agent architectures for scientific tasks: how they coordinate, where they fail, and what verification they need before their output is trusted in a lab.
Students and collaborators: we take on research partners.
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