Our Research
Multimodal Informatics for
Animal and Comparative Health
Progress in animal health requires integrating modalities — genomic, pathological, imaging, clinical, acoustic, behavioral, environmental, sensor — that have historically been studied in isolation. We build the AI and informatics methods that make that integration tractable, with One Health questions surfacing as a natural extension of the work.
Our Research Themes
Three modality-family pillars organize our work, each spanning multiple data types rather than a single one. Sensing and behavioral data appear today as cross-cutting projects and are likely to become a fourth pillar over time.
Genomic & Molecular
Representation learning and integrative analysis across genomic, transcriptomic, proteomic, and other molecular modalities. We treat molecular data as one component of a broader multimodal picture, with applications across companion animal, comparative, and wildlife health contexts.
Imaging & Pathology
Computational methods for histopathology, radiology, ophthalmic imaging, and other image-based modalities in animal health. We focus on representations that travel across imaging types, scales, and species, supporting comparative and translational study.
Clinical & Longitudinal
Methods for electronic health records, clinical notes, and longitudinal patient trajectories. We build tools for prediction, decision support, and population-scale analysis grounded in real veterinary clinical data and designed to integrate with other modalities.
Building the Future
As a newly established lab, we're assembling the team, partnerships, and infrastructure to tackle problems that span the modalities of animal and comparative health.
Companion Animal, Comparative & Wildlife Health
We work across companion animal, comparative, and wildlife health contexts, with One Health questions surfacing as a natural consequence of integrating modalities across species and environments.
Cross-Cutting: Sensing & Behavioral
Acoustic, video, sensor, and environmental signals appear across our work today as cross-cutting projects. We anticipate organizing this work as a fourth pillar — Sensing & Behavioral — as it matures.
Open Data & Reproducible Methods
Multimodal integration depends on curated data, interoperable standards, and reproducible pipelines. We build and release these alongside our methods.
Join Our Mission
We're actively seeking graduate students, postdocs, and collaborators who are excited about shaping the next generation of AI systems for health. Whether you're coming from computer science, veterinary medicine, biomedical informatics, or public health—if you're passionate about making a real-world impact, we want to hear from you.
Get in Touch