We study how meaning moves -- measuring, modeling, and exploiting semantic instability in AI systems to advance both security and scientific understanding.
Semantic Substrate Dynamics Theory (SSDT) is the foundation the group is built on: the claim that meaning behaves as a dynamical system with measurable structure. Word senses, diagnostic codes, and learned representations do not hold still. They shift when models process their own outputs and when real-world usage moves underneath them. SSDT gives us the vocabulary and the geometry to measure that motion, track where it accumulates, and mark the point where it crosses from noise into failure. See DriftWell as an example
Meaning has shape, and shape can be measured. This area applies Ollivier-Ricci curvature and related tools from differential geometry to the embedding spaces where language models hold meaning. Curvature marks where a semantic region is tightly bound and where it is about to fragment. Graph structure shows how concepts connect and where those connections reorganize. Followed over time, the same geometry reveals where meaning bends, where a frame shifts, and where a trajectory is about to break. It turns semantic movement into something with coordinates, direction, and rate. Read about Ollivier-Ricci Curvature in SSDT
Agentic systems chain retrieval, reasoning, and generation, and meaning can move at every hop while the workflow looks stable. We study where that happens and what it costs. A pipeline can carry the right answer forward or quietly reframe it, and the difference rarely shows on the surface. The same instability that degrades reliability by accident can be induced on purpose, which is why adversarial semantic manipulation sits inside this work rather than beside it. The question throughout is whether an agentic pipeline still means what it started with by the time it answers. See how AiVisor exposed a structural flaw in how these systems get scored
Healthcare is where semantic drift stops being academic. Clinical ontologies shifting or a governance model that loses clinical specificity causes changes in meaning-in-use that carry direct costs and patient-care risk. Our work focuses on coding drift detection and shifts across large inpatient cohorts, denial and revenue cycle prediction, VTE and SSI surveillance, and governance frameworks that keep AI aligned to care trajectories. See AI governance in TwinPath AI|Why governance is needed
As agents take over ingestion, triage, coding, and other pipeline processes, humans are increasingly disconnected from direct evidence and start seeing workflow products that upstream AI has already shaped. An object can look stable while the meaning underneath it moves -- from accident to attack, or from outage to state-linked operation -- with nothing on screen to signal the shift. This area builds the supervisory layer that catches it: semantic-dynamics metrics that flag when a stable object no longer carries stable meaning, and orchestration that routes reframing and human-agent disagreement to a person while letting settled outputs pass. The goal is to keep the human in command of meaning, not just in the loop. See an example analyst tool with semantic frame-shift capabilities
AIVisor, an agentic retrieval-augmented LLM for student advising, was used to examine how personalization affects system performance across multiple evaluation dimensions. Results showed a consistent trade-off: personalization reliably improved reasoning quality and grounding,...
For a large healthcare system, ignoring costs associated with managing the patient encounter denial process (staffing, con- tracts, etc.), total denial-related amounts can be more than $1B annually in gross charges. Being able to predict a denial before it occurs has the...
Despite the many successes of artificial intelligence in healthcare applications where human–machine teaming is an intrinsic characteristic of the environment, there is little work that proposes methods for adapting quantitative health data-features with human expertise insights....
We welcome inquiries from prospective graduate students, collaborators, and organizations interested in supporting this research. If your interests involve meaning and reliability in AI, human-AI systems, or applied AI research, we would be glad to hear from you.