Joshua Proctor is currently a research manager and senior research scientist at IDM leading the Data, Dynamics and Analytics (DDA) team. DDA is focused on applying modern data science, machine-learning, and statistical techniques to a wide-range of data sources, i.e., surveillance of infectious diseases, household surveys, genome sequencing of viruses or parasites, and demography. We aim to leverage the growing success of these modern analytic and numerical methods to advise on near-term public policy questions facing the global health community. In conjunction, we also identify the short-comings of current methodological approaches and develop novel, principled algorithms to face the challenges of surveillance data.

Joshua Proctor earned a Ph.D. in Mechanical and Aerospace Engineering from Princeton. Before graduate school, Joshua earned a Bachelor of Science in Aeronautics and Astronautics Engineering, and a Bachelor of Arts in English Literature, both from the University of Washington, Seattle. His doctoral research focused on investigating the effects of neural feedback on rapidly running insects (specifically, cockroaches). The research required the development of complex mathematical models describing legged locomotion, the application of dimensionality reduction techniques, and the characterization of these nonlinear dynamical systems through bifurcation analyses [1,2]. The research led to several important discoveries about the role of neural feedback during running, while also inspiring better robotic designs for maneuverability, stability, and control.

Joshua joined IDM in 2011 after his Ph.D. and contributed to the development of the compartmental modeling simulations software. This software package is soon to be released to the global health community. Joshua then joined the nascent Applied Mathematics group at IDM where he developed algorithms and novel mathematical methods for the study of infectious disease data. He became interested in equation-free modeling and data-driven analyses, inspired by the mathematical developments around fluid dynamics at Princeton University. Generally, equation-free modeling does not require a set of pre-determined, derived from first-principles equations. Instead, the time-series data is utilized to discover dynamic models and/or dynamic characteristics. This led to a number of important methodological innovations [3,4,5,6], including a book describing the mathematical method Dynamic Mode Decomposition [7]. These methodologies are poised to have a substantial impact on infectious disease modeling.

During his previous position in the Applied Mathematics group, Joshua also focused on leveraging current data science and machine-learning methodologies for epidemiological and demographic applications. For example, he is interested in characterizing genomes of viruses or parasites and their metadata (GPS location, age, gender, sex, etc.) to better understand the transmission dynamics of infectious diseases [8,9]. Understanding these modern genomic data sets can help inform disease surveillance efforts for elimination and eradication efforts. Joshua is also interested in questions around demography, specifically in the family planning group and estimating child mortality.