My research area is Artificial Intelligence with a focus on large-scale constraint-based reasoning, optimization, and machine learning.
To demonstrate these techniques, we focus on two large-scale constraint-satisfaction domains, Sudoku and circle packing.
We have developed a large-scale, constraint-based network model of the metabolic partnership between astrocytes and glutamatergic neurons that allows for a quantitative appraisal of the extent to which stoichiometry alone drives the energetics of the system.
Comparing with other optimization algorithms, simulations suggest that RNN has strong ability to search for exact solutions under the condition of large scale constraints.
In conjunction with the adjoint-variable sensitivity information, the enhanced aggregation method is utilized to efficiently reduce the computational effort arisen from large-scale strength constraints.
For a wCDM model, when combined with WMAP7+HST+SN, we find w = -1.071 ± 0.078, and H 0 to be 71.3 ± 1.7 s-1 Mpc-1 km, which is competitive with the latest large-scale structure constraints from large spectroscopic surveys such as the SDSS Data Release 7 (DR7) and WiggleZ.
To solve large-scale problems, tightening constraints, and simplified formulations are proposed.
Most recently, he has begun working on combining data from the optical Dark Energy Survey DESS) with SPT, to improve large-scale structure-based constraints on neutrino mass and dark energy.
MicrobesFlux converts the constraint-based flux analysis model on the reconstructed network to a large-scale optimization problem with constraints, and employs IPOPT (an optimization solver based on an interior-point algorithm) to find an optimal solution [ 24].
However, in large-scale analyses, often grid constraints are neglected.
