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, nonequilibrium thermodynamics, and optimization, and borrows lightly from topology.
I currently work on variational inference problems, noise propagation in biological networks, selforganizing edge flows, and functional form game theory (with exciting applications to multiagent 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
Principal Tradeoff Analysis
A. Strang, D. SeWell, A. Kim, K. Alcedo, D. Rosenbluth (NeurIPS 2022)
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

A. Strang, D. SeWell, A. Kim, K. Alcedo, and D. Rosenbluth. Principal Tradeoff Analysis. arXiv (2022)

H. Kim, D. SanzAlonso, and A. Strang. Hierarchical Ensemble Kalman Methods with Sparsity Promoting Generalized Gamma Hyperpriors. arXiv (2022)

C. Cebra, and A. Strang. Similarity Suppresses Cyclicity: Why Similar Competitors Form Hierarchies. arXiv (2022)

S. Agrawal, H. Kim, D. SanzAlonso, and A. Strang. A Variational Approach to Inverse Problems with Gamma Hyperpriors. SIAM Uncertainty Quantification (2022)

A. Strang, K. C. Abbott, and P. J. Thomas. The Network HHD: Quantifying Cyclic Competition in TraitPerformance Models of Tournaments. SIREV (2022)

A. Strang, K. C. Abbott, and P. J. Thomas. The Network HHD: Quantifying Cyclic Competition in TraitPerformance Models of Tournaments. SIREV (2021)

Patterson, Amy, A. Strang and K. C. Abbott. When and where we can expect to see early warning signals in multispecies systems approaching tipping points: insights from theory. the American Naturalist (2021)

Strang, Alexander. Solutions to the Minimum Variance Problem using Delaunay triangulation. SIAM Discrete Mathematics (2020).

Strang, Alexander. Applications of the HelmholtzHodge decompositions to networks and random processes. Case Western Reserve University, Dissertation (2020).

Calvetti, Daniela, M. Pragliola, E. Somersalo, and A. Strang. Sparse reconstructions from few noisy data: analysis of hierarchical Bayesian models with generalized gamma hyperpriors. Inverse Problems (2019).

Calvetti, Daniela, E. Somersalo, and A. Strang. Hierarchical Bayesian models and sparsity: l2magic. Inverse Problems (2019).

Strang, Alexander, K. C. Abbott, and P. J. Thomas. How to avoid an extinction time paradox. Theoretical Ecology (2019).

Strang, Alexander, O. Haynes, N. D. Cahill, and D. A. Narayan. Generalized relationships between characteristic path length, efficiency, clustering coefficients, and density. Social Network Analysis and Mining 8 (2018).

Strang, Alexander, O. Haynes, R. Florez, and D. A. Narayan. Enumerating shortest paths and determining edge betweeness centrality in Cartesian products of paths and cycles. Bulletin of the ICA (2018)