Research

 

I am interested in stochastic processes and modelling in biological systems, networks and the interplay of structure and dynamics, and Bayesian inference and inverse problems. My work relies heavily on linear algebra, non-equilibrium thermodynamics, and optimization, and borrows lightly from topology.

I currently work on variational inference problems, noise propagation in biological networks, self-organizing edge flows, and functional form game theory (with exciting applications to multi-agent training and visualization). My published work ranges from the study of extinction events and large deviations, to geometric solutions to moment closure problems, to the characterization of network structure in tournaments.

 
 

Highlights

 

The Network HHD: Quantifying Cyclic Competition in Trait Performance Models of Tournaments

A. Strang, K. C. Abbott, P. J. Thomas (SIREV 2021) 

Solutions to the Minimum Variance Problem using Delaunay Triangulation

A. Strang (SIAM Journal of Discrete Mathematics 2021) 

The Network HHD: Quantifying Cyclic Competition in Trait Performance Models of Tournaments

A. Strang, K. C. Abbott, P. J. Thomas (SIREV 2021) 

Solutions to the Minimum Variance Problem using Delaunay Triangulation

A. Strang (SIAM Journal of Discrete Mathematics 2021) 

When and where we can expect to see early warning signals in multispecies systems approaching
tipping points: insights from theory

A. Patterson, A. Strang, K. C. Abbott (AmNat 2021) 

Sparse reconstructions from few noisy data:
analysis of hierarchical Bayesian
models with generalized gamma hyperpriors.

D. Calvetti, , M. Pragliola, E. Somersalo, A. Strang (Inverse Problems 2021)

Publications

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