Hi, I'm Josh — Assistant Scientist in the Department of International Health within the Bloomberg School of Public Health at Johns Hopkins University.

I determine what actions to take, what experiments to run, and what measurements are worth collecting in systems where interventions are costly and uncertainty is unavoidable. To do this, I build open scientific computing infrastructure — declarative modeling, simulation, and generative decision support — for partially observed systems across health, environmental, and earth sciences, integrating generative modeling (mechanistic, statistical, and hybrid), Bayesian inference, numerical solver design, and model evaluation. Prediction alone is not enough; the structural assumptions in every model must be tested and defended before anyone acts on the output.

Applications include infectious disease forecasting and intervention timing, surveillance design, marine and terrestrial ecology, and cultural transmission dynamics. The infectious disease tools contribute to CDC-funded multi-model scenario evidence used for public-health planning; other applications include wildlife disease surveillance design in sub-Saharan Africa and cross-scale ecological modeling.

Domains → Tools: Infectious disease (op_engine, flepimop2, Flu Hub) · Wildlife & zoonotic disease (op_engine, trade-study) · Cultural evolution & genomics (VBPCApy, pp-eigentest) · Marine ecology (op_system, trade-study) · Cross-domain methods (trade-study, pp-eigentest)

🌐 jcmacdonald.dev · Publications · CV · @jcm-sci

Status: Released on PyPI · JOSS submission in preparation

Recovers hidden population structure from datasets with substantial missing entries — applied to cultural, genetic, and survey data where complete records are rare. Used in Macdonald et al. (2024, Evolutionary Human Sciences). Variational Bayesian PCA for incomplete data with native per-entry missingness handling, full posterior uncertainty quantification, automatic component pruning, built-in model selection, C++-accelerated kernels, and a scikit-learn-compatible API. Current work focuses on package hardening and a JOSS submission.

Status: Active development · Developed for CDC-funded flu scenario modeling

Simulation engine for CDC-funded influenza scenario modeling and wildlife disease modeling. Portable Array-API numerical kernels support explicit, IMEX, fully implicit, and stochastic integration, while provider integrations add backend-native control flow, compilation, checkpointing, and schedule validation. NumPy has mutable/preallocated fast paths; JAX adds device-resident adaptive discovery and differentiable frozen-schedule replay; PyTorch supports eager fixed-step autograd; and CuPy is supported through dense Array-API paths and a tested sparse adapter.

Status: Active development

Declarative specification language and compiler for structured dynamical systems. A restricted expression parser lowers explicit equations or transition diagrams through a typed intermediate representation to vectorized Python AST and code objects. The same compiled right-hand side works with NumPy, JAX transformations, and PyTorch autograd, with support for flat, block, and PyTree state layouts plus validated operators, reactions, routing, and constraints.

Status: Released on PyPI

Define factors, build parameter grids, run hierarchical study phases, and extract Pareto fronts — for any domain where you compare alternatives against competing objectives. Protocol-driven architecture with proper scoring rules, experimental design (full factorial, LHS, Sobol, Halton, Morris screening), Pareto optimization, adaptive NSGA-II search, and Bayesian model stacking.

• trade-study (Python) — wraps scoringrules, pymoo, arviz, SALib, and optuna.

Status: Private pre-release · Companion to arXiv:2409.12129 · Public release planned with paper

Determines how many meaningful patterns exist in a dataset, separating signal from noise. The current analysis plan centers a sequential fitted-model rank selector and treats the earlier ensemble/consensus approach as a supplementary negative result. NumPy is the reference path, JAX is opt-in, C++ is selected only when measured faster, and sparse inputs are guarded before dense spectral computation. Applied in Macdonald et al. (2024, Evolutionary Human Sciences).

Configuration-driven orchestration engine for CDC-supported infectious disease forecasting and scenario analysis. Plugin architecture decouples model specification, numerical integration, and output persistence.

Flexible Pipeline for Modeling Pathogens. Contributed 5×–20× runtime speedups to the simulation backend through profiling-driven optimization.

MIDAS Network coordination hub for CDC-funded influenza scenario modeling. Contributing team lead submissions and model evaluation infrastructure.

Lead model developer for ACCIDDA's CDC-funded seasonal influenza scenario modeling across the 2024/25, 2025/26, and 2026/27 seasons. Set up the ACCIDDA flu model and operational pipeline (2024/25). Extensive work with external modeling packages drove contributions that led to FlepiMoP2 (2025/26). Supervising an undergraduate student in a scientific overhaul of the flu model (2026/27). These contributions form part of the multi-model evidence produced by the CDC Flu Scenario Modeling Hub for public-health planning.

Technical lead supervising implementation of an age- and immune-status-structured Hib model for the Navajo Nation to evaluate the impact of long-running vaccination programs.

Benchmarking the FlepiMoP backend revealed architectural limitations that motivated a clean-sheet redesign. The result is the op_system + op_engine + flepimop2 stack: a restricted specification compiler, an Array-API-polymorphic operator-partitioned solver with backend-native execution providers, and a configuration-driven campaign orchestrator. In adaptive differentiable workflows, op_engine separates discrete mesh discovery from differentiable frozen-schedule replay; gradients are therefore conditional on the recorded mesh, which is refreshed when accuracy diagnostics fail or parameters move materially.

• Dengue antibody-dependent-enhancement modeling and Bayesian analysis
• Outcome-independent structural scores for scientific predictions
• COVID-19 observation-noise sensitivity experiments
• Diphtheria outbreak-vaccination modeling
• CCHF surveillance and control design across wildlife and livestock systems
• VBPCApy package hardening and JOSS manuscript preparation

These capabilities are deployed to design interventions, optimize surveillance strategies, and determine what experiments and measurements are worth their cost.

Modeling · Generative models (ODE/PDE/stochastic/hybrid), scientific AI/ML (physics-embedded surrogates), stability and bifurcation analysis, simulation-based inference, global sensitivity analysis (Sobol, PRCC, Morris)

Inference · Hierarchical Bayesian models, variational inference, profile likelihood, posterior predictive checks, identifiability analysis, uncertainty quantification

Computing · Python, Julia, C++; ODE/PDE solver design and benchmarking, operator splitting (IMEX), vectorization, CI/CD, type-checked codebases

Evaluation · Proper scoring rules, calibration diagnostics, Pareto front analysis, reduced-order forecasting models with scientific constraints by construction

Full details: jcmacdonald.dev/cv