Reviewer and collaborator profile

Evidence-linked areas for grant review and scientific collaboration.

I am a computational scientist and Assistant Scientist at Johns Hopkins Bloomberg School of Public Health. I develop mechanistic models, statistical methods and research software for decisions under uncertainty. This page is a concise guide to areas in which I can review proposals or contribute to a scientific collaboration; my full CV provides the complete record.

Last substantively reviewed: 22 October 2026.

Review areas

Established areas

  • Mechanistic infectious-disease modeling, including within-host, population, spatial, stochastic and multi-scale models.
  • Bayesian and simulation-based inference, including incomplete data, uncertainty assessment and data assimilation.
  • Model and forecast evaluation, including operational scenario modeling, numerical qualification and comparisons under stated objectives.

Developing and collaborative areas

  • Scientific machine learning for dynamical systems, focused on methods that incorporate or evaluate scientific structure.
  • Monitoring, surveillance and experimental design, developed jointly with domain and field teams that define the scientific decision, feasible measurements and costs.

“Developing” identifies an active methods direction, not a claim of the same depth of evidence as the established areas.

Selected evidence

Scope

My role is mathematical, statistical and computational. Collaborations on monitoring or instrument settings require domain partners to define feasible protocols, calibration procedures and field constraints. I do not present clinical trial conduct, wet-laboratory virology or oceanographic instrument engineering as independent areas of expertise.

For a review request or an initial collaboration discussion, please include the scientific area, decision or review criterion, expected time commitment and any conflict-of-interest constraints. Email me or consult my publications and research software.