Health Data Scientist · Disease Ecologist · Geospatial Analyst
Mapping disease risk. Informing decisions.
I work at the intersection of epidemiology, ecology, and computation — leveraging environmental and health data to characterize, predict, and map disease risk from the individual to population level, producing digital health tools and insights for research, surveillance, and policy.
Get in touch Learn moreI consult across the full research pipeline, from study design and data integration through modeling, tool development, and delivery. My expertise spans traditional biostatistics and machine learning, with a focus on bringing socio-ecological and environmental data into risk prediction and statistical inference tools.
End-to-end support from scoping, proposal writing, and data acquisition through analysis, validation, and stakeholder delivery.
Incorporating climate, land use, remote sensing, and socio-ecological datasets into health and disease research frameworks using Google Earth Engine, R, and Python. I welcome opportunities to integrate these data into digital health tools, working with startups, public health agencies, and application-driven organizations to embed environmental context into their products and pipelines.
Machine learning models, interactive dashboards, and automated pipelines that turn complex data into risk prediction tools for surveillance, research, and policy.
Traditional and advanced biostatistics for health research — survival analysis, longitudinal modeling, mixed effects models, classification, and study design across human and animal health systems.
Interested in working with CKG Scientific Consulting? I welcome inquiries from organizations, research teams, and institutions.
I hold a PhD in Integrative Biology with a minor in Statistics from Oregon State University, where I was an NSF Graduate Research Fellow and conducted a long-term cohort study of African buffalo — applying longitudinal mixed-effects models, survival analysis, and genomic pipelines to track individual animals over time and characterize multi-pathogen dynamics.
I subsequently spent two years at Stanford University as a Postdoctoral Scholar and three years as a Senior Scientist in the Department of Biology, Institute for Human-centered AI, and Doerr School of Sustainability — including a one-year fellowship as a Stanford Human-centered AI Fellow (2023–2024). At Stanford, I led analytics for the Disease Ecology in a Changing World program, developing risk models, machine learning frameworks, and operational tools for neglected disease research across the globe.
Temperature dependence of mosquitoes: comparing mechanistic and machine learning approaches
Human footprint is associated with shifts in the assemblages of major vector-borne diseases
Phylogenetic and biogeographical traits predict unrecognized hosts of zoonotic leishmaniasis
Data-driven predictions of potential Leishmania vectors in the Americas
Human-mediated impacts on biodiversity and the consequences for zoonotic disease spillover
Co-infection best predicts respiratory viral infection in a wild host
Global patterns of the fungal pathogen Batrachochytrium dendrobatidis support conservation urgency
The benefit of hedgerow access on the health and growth rate of pasture-raised broiler chickens
Age of first infection across a range of parasite taxa in a wild mammalian population
Evolutionary consequences of feedbacks between within-host competition and disease control
Shrinking skinks: lizard body size declines in a long-term forest fragmentation experiment
Detection of pathogen exposure in African buffalo using non-specific markers of inflammation
For a complete list, see my Google Scholar profile.