Our programs are designed as long-horizon research tracks with clear theoretical foundations, empirical milestones, and publication objectives.
We investigate how model architectures, objective design, and formal constraints affect reasoning quality and failure modes in high-stakes environments.
Current work focuses on optimization geometry, sample efficiency, and calibration in long-tail distributions common to scientific and biomedical datasets.
We develop formal analysis tools for convergence, identifiability, and compositionality, making model behavior tractable beyond benchmark performance alone.
Projects include sequence-level representation learning, phenotype inference, and multi-modal integration with careful handling of experimental uncertainty.
We build language systems for scientific discovery: extraction, synthesis, and grounded retrieval across technical literature and biomedical corpora.
Shared infrastructure includes dataset curation standards, reproducible experiment stacks, and benchmark protocols that preserve scientific comparability.
Define assumptions, success criteria, and intended contribution boundaries.
Develop analytical expectations and prove key properties where possible.
Run controlled studies, ablations, and stress tests under documented protocols.
Publish manuscripts and reference artifacts for external verification and reuse.