Bayesian Ecological Modeling
Hierarchical generative models for phenology and species distributions.
Stan · NumPyro · JAX
Moore, M.A., Emery, N.C., and Elmendorf, S.C.
Miles A. Moore
Computational Ecologist · DOE CSGF Fellow · PhD Student, CU Boulder
I build and apply statistical and simulation-based models for studying biological systems under uncertainty. By combining Bayesian inference, machine learning, and high-performance computing, I investigate ecological processes across scales.
Research
My work sits at the intersection of ecology, statistics, and computation. I am developing methods that handle the complexity and uncertainty inherent in natural systems.
ML for Ecological Inference
Neural networks + kernel methods paired with mechanistic models.
Python · PyTorch
High-Performance Simulation
Supercomputing for large-scale biological simulations.
Julia · MPI · Fortran
Uncertainty Quantification
Probabilistic solutions for complex systems (ODEs, Transcendentals).
Selected work
Clark, E.I., Moore, M.A., Khoury, C.K., Hulke, B.S., Kane, N.C. and Elmendorf, S.C.
Soil moisture and active layer thickness across Alaska, USA and Northwestern Canada
Moore, M.A., Hoy, E.E., Clayton, L.K., Schaefer, K., Bourgeau-Chavez, L.L., et al.
Recent
This semester: graduate-level Bayesian statistics in Applied Math and a deep dive into neural networks and transformers.
Reflections on a rigorous semester covering numerical methods and high-performance computing in C++.
Clark et al. 2025 — 46 years of sunflower multi-environment trials reveal how climate drives optimal flowering phenology.
Toolkit
Common Languages
- Python
- R
- Julia
- Stan
- Bash
Recent Methods
- Bayesian Inference
- Gaussian Processes
- Neural Networks
- Ordinary Differential Equations
- Predictive Machine Learning
Computing Platforms
- High-Performance Computing / SLURM
- OpenMP / MPI (Parallel Computing)
- Docker
- Git / GitHub
- Quarto / R Markdown