Our Team
A multidisciplinary team of epidemiologists, data scientists, AI researchers, and public health practitioners united by a mission to transform population health through responsible innovation.
A multidisciplinary team of epidemiologists, data scientists, AI researchers, and public health practitioners united by a mission to transform population health through responsible innovation.
Solving complex public health challenges requires diverse expertise. Our team brings together deep technical capabilities in AI and data science with practical experience in epidemiology, health policy, clinical medicine, and community health.
Infectious disease modeling, chronic disease epidemiology, outbreak investigation, surveillance systems design
Deep learning, NLP, computer vision, causal inference, time series forecasting, reinforcement learning
Bayesian methods, spatial statistics, survival analysis, clinical trials, predictive modeling
Emergency medicine, infectious disease, preventive medicine, clinical informatics, patient care
Health systems research, policy analysis, implementation science, health economics, regulatory affairs
Bioethics, health equity research, community engagement, algorithmic fairness, participatory methods
Cloud infrastructure, data engineering, MLOps, security, interoperability standards, scalable systems
International development, tropical medicine, humanitarian response, low-resource settings, pandemic preparedness
We don't build AI for its own sake. Every system we develop addresses a real public health need, grounded in epidemiological science and validated through rigorous evaluation. Hype has no place in public health technology—only evidence matters.
Technology can reduce or amplify disparities. We proactively design for equity, ensuring our tools work for the most vulnerable populations, not just those with the best data infrastructure. If an algorithm exacerbates inequity, it's not fit for deployment—full stop.
No single organization will solve public health's grand challenges. We partner with health departments, academic institutions, community organizations, and technology companies, bringing together diverse perspectives to create solutions greater than the sum of their parts.
We work in complex adaptive systems where uncertainty is inherent and unintended consequences are possible. Intellectual humility—acknowledging what we don't know, learning from failures, iterating based on feedback—is essential to responsible AI development.
We work closely with leading institutions across academia, government, and the nonprofit sector.
Schools of public health, computer science departments, and medical schools bring research rigor and methodological innovation to our work. Joint projects range from foundational research to real-world implementation trials.
Designed to partner with state and local health departments, CDC, WHO, and international public health organizations. Collaborative framework ensures tools address real operational needs and incorporate deep domain expertise.
Community health centers, advocacy groups, and grassroots organizations keep us grounded in the lived experiences of the populations we aim to serve. They ensure our technology advances equity and respects community values.
Cloud providers, data platforms, and AI companies provide infrastructure and technical capabilities. These partnerships enable us to leverage cutting-edge technology while maintaining focus on public health outcomes.
Foundations focused on health, technology for good, and equity support our work through funding, strategic guidance, and connections to other changemakers in the ecosystem.
Global health initiatives, research consortia, and international development agencies extend our reach to low- and middle-income countries where AI can have transformative impact.
Diverse teams build better AI. Research shows that homogeneous teams are more likely to overlook biases and fail to anticipate how algorithms affect different populations. We actively recruit team members from underrepresented backgrounds in both public health and technology.
Diversity is not just demographic—it's also disciplinary, experiential, and cognitive. We value different ways of thinking, different life experiences, and different approaches to problem-solving.
Recruitment is just the beginning. We foster a culture where everyone's voice is heard, where dissent is welcomed, where mistakes are learning opportunities, and where psychological safety enables honest dialogue about difficult topics like bias and equity.
Regular bias training, inclusive leadership development, and participatory decision-making ensure that our commitment to DEI is not just rhetoric—it's embedded in how we work.
Advisory structure designed to incorporate expertise from public health, AI ethics, health policy, and community advocacy.
Framework to engage state epidemiologists, CDC leadership, and WHO technical officers for strategic guidance on public health priorities and operational realities of health departments.
Bioethicists, philosophers, and AI safety researchers ensure our work adheres to the highest ethical standards and anticipates potential harms before deployment.
Community health advocates and patient representatives ensure our technology serves the needs of diverse populations and respects community priorities and values.
We're building a world-class team to tackle some of the most important challenges in population health. Whether you're a data scientist passionate about social impact, an epidemiologist excited by AI's potential, or a software engineer who wants to build systems that save lives—there may be a place for you here.
Current Focus Areas: Machine learning engineering, epidemiological modeling, health equity research, data engineering, product management, partnerships & implementation.
We actively seek research partnerships with academic institutions, health departments, and community organizations. Whether you have a specific project in mind or want to explore possibilities, we'd love to hear from you.
Collaboration Models: Joint grant proposals, data sharing agreements, co-authored publications, student internships, visiting researcher programs.
We offer opportunities for graduate students, postdocs, and early-career professionals to gain hands-on experience applying AI to real-world public health challenges. Fellows work alongside our team on active projects while receiving mentorship and professional development.
Typical Duration: 3-12 months. Fields: epidemiology, biostatistics, data science, computer science, health policy.
Skilled volunteers contribute to public health in meaningful ways. Data scientists, developers, designers, writers, and subject matter experts can support specific projects on a volunteer basis, particularly for global health applications in low-resource settings.
Time Commitment: Flexible, ranging from a few hours for code review to ongoing project involvement.
Whether you're exploring career opportunities, seeking research collaborations, or interested in volunteering your skills—we'd love to connect.