Research

Collaborative Knowledge Synthesis

Reliable and resilient knowledge-graph representation and synthesis from large language models and semantic knowledge bases through algorithms, interfaces, and systems for evidence-based human-AI collaboration in the context of food systems and translational science (real-world evidence-based claims).

Integration of Distributed Knowledge Fragments

Integration and linking of distributed knowledge fragments: resilient and reliable integration of fragmented knowledge in knowledge graphs through repeatable algorithms and methodologies in distributed and incomplete contexts, possibly applying federated learning principles in the context of food systems.

Value Alignment and Knowledge Integration in GenAI

Aligning GenAI output with situated values and improving the reliability of generative AI through human-in-the-loop, knowledge-based interpretation of AI behavior and neuro-symbolic approaches for integrating structured knowledge into generative AI.

Trustworthy Conversational AI Using FAIR Data and Services

Improving the accuracy and trustworthiness of conversational AI by incorporating FAIR data and services, neurosymbolic reasoning, and user interaction.

Human-AI Ethics and Responsible Innovation

Address epistemic and ethical challenges of human-AI collaboration with generative AI models in the context of food systems.