1. Model-data fusion for vegetation models and also some other things
Florian Hartig,
University of Regensburg

2. Personal Background Born close to Frankfurt, Germany
Studied physics in Berlin and Uppsala (Sweden)
PhD in Leipzig on agent-based simulations of land use decisions
Postdoc in Leipzig on biodiversity patterns and community assembly in tropical rainforests
Lecturer (Biostatistics) at Uni Freiburg
Currently professor (Theoretical Ecology) at Uni Regensburg

3. Research Areas/Interests
Community ecology / biogeography / macroecology / theoretical ecology
Statistics and machine learning
Classical biostatistics: inference, model selection, …
Some machine learning applications
Simulation-based inference
Approximate inference for stochastic simulations in ecology and evolution (Synthetic Likelihood, Approximate Bayesian Computation, … )
Calibration / data-assimilation / model-data fusion
Model-data connection, mostly for dynamic vegetation models

4. Bayesian model-data fusion for vegetation models
Hartig et al., J. Biogeogr., 2012

5. BayesianTools R package https://cran. r-project
Algorithms for model-data fusion
Sampling Algorithms
Various Metropolis algorithms
Differential Evolution
DREAM
Sequential Monte Carlo
Support for
Bayesian tasks (plots, statistics)
Optimization
Sensitivity Analysis
Plotting
Parallelization

6. Links to „Computer Science meets Ecology“
Machine learning approaches
Filling sparse trait matrices
„trait-matching“ / “syndromes”: predicting ecological interactions or characteristics based on trait data
Model-data fusion
Efficient MCMC and SMC algorithms
Model-data integration for dynamic vegetation models
Simulation
General interest in HPC methods
Simulating large (10^9) numbers of individuals in parallel