We connect provable foundations, actionable methods, and interpretability systems across large models, human workflows, and scientific discovery.
Assumptions, guarantees, and limits made explicit.
Evidence that supports debugging, decisions, and intervention.
Tools that make model behavior visible and usable.
When is an explanation provable, useful, and theoretically justified?
How can we understand and intervene in language, vision, and multimodal models?
What infrastructure makes interpretability scalable, reproducible, and deployable?
How should humans query, evaluate, refine, and act on explanations?
How can interpretability support scientific discovery and hypothesis generation?
When is an explanation provable, useful, and theoretically justified?
We study stability, faithfulness, controllability, consistency, influence, and evaluation criteria for actionable explanations.
How can we understand and intervene in language, vision, and multimodal models?
We develop mechanistic analyses, representation interventions, model editing, unlearning, and steering methods that make model behavior actionable.
What infrastructure makes interpretability scalable, reproducible, and deployable?
We build benchmarks, pipelines, tooling, and service patterns for repeatable explanation workflows and interpretable model monitoring.
How should humans query, evaluate, refine, and act on explanations?
We design human-in-the-loop interfaces, feedback protocols, evaluation tasks, and decision workflows that turn interpretability into action.
How can interpretability support scientific discovery and hypothesis generation?
We apply PAI methods to protein language models, healthcare, autonomous systems, material science, and other discovery settings.