5 citations · 5 across the 2 of their papers we have counts for
6 papers
Hamilton-Jacobi equations on graphs with applications to semi-supervised learning and data depth
Jeff Calder, Mahmood Ettehad
Shortest path graph distances are widely used in data science and machine learning, since they can approximate the underlying geodesic distance on the data manifold. However, the s…
On Vertex Conditions In Elastic Beam Frames: Analysis on Compact Graphs
Soohee Bae, Mahmood Ettehad
We consider three-dimensional elastic frames constructed out of Euler-Bernoulli beams and describe extension of matching conditions by relaxing the vertex-rigidity assumption and t…
Instances of Computational Optimal Recovery: Dealing with Observation Errors
Mahmood Ettehad, Simon Foucart
When attempting to recover functions from observational data, one naturally seeks to do so in an optimal manner with respect to some modeling assumption. With a focus put on the wo…
Optimizing Consistent Merging and Pruning of Subgraphs in Network Tomography
Mahmood Ettehad, Nick Duffield, Gregory Berkolaiko
A communication network can be modeled as a directed connected graph with edge weights that characterize performance metrics such as loss and delay. Network tomography aims to infe…
Approximability Models and Optimal System Identification
Mahmood Ettehad, Simon Foucart
This article considers the problem of optimally recovering stable linear time-invariant systems observed via linear measurements made on their transfer functions. A common modeling…
Graph Reconstruction from Path Correlation Data
Gregory Berkolaiko, Nick Duffield, Mahmood Ettehad +1
A communication network can be modeled as a directed connected graph with edge weights that characterize performance metrics such as loss and delay. Network tomography aims to infe…