activity
20232025
most citedJoint Signal Recovery and Graph Learning from Incomplete Time-Series

1 citations · 2 across the 6 of their papers we have counts for

collaborators

6 papers

cs.SI2025

The Effect of Network Topology on the Equilibria of Influence-Opinion Games

Yigit Ege Bayiz, Arash Amini, Radu Marculescu +1

Online social networks exert a powerful influence on public opinion. Adversaries weaponize these networks to manipulate discourse, underscoring the need for more resilient social n…

cs.LG20241 cited

Clustering Time Series Data with Gaussian Mixture Embeddings in a Graph Autoencoder Framework

Amirabbas Afzali, Hesam Hosseini, Mohmmadamin Mirzai +1

Time series data analysis is prevalent across various domains, including finance, healthcare, and environmental monitoring. Traditional time series clustering methods often struggl…

cs.RO2024

Scalable Networked Feature Selection with Randomized Algorithm for Robot Navigation

Vivek Pandey, Arash Amini, Guangyi Liu +4

We address the problem of sparse selection of visual features for localizing a team of robots navigating an unknown environment, where robots can exchange relative position measure…

cs.LG20231 cited

Joint Signal Recovery and Graph Learning from Incomplete Time-Series

Amirhossein Javaheri, Arash Amini, Farokh Marvasti +1

Learning a graph from data is the key to taking advantage of graph signal processing tools. Most of the conventional algorithms for graph learning require complete data statistics,…

math.OC2023

Data-Driven Distributionally Robust Mitigation of Risk of Cascading Failures

Guangyi Liu, Arash Amini, Vivek Pandey +1

We introduce a novel data-driven method to mitigate the risk of cascading failures in delayed discrete-time Linear Time-Invariant (LTI) systems. Our approach involves formulating a…

eess.SY2023

Quantification of Distributionally Robust Risk of Cascade of Failures in Platoon of Vehicles

Vivek Pandey, Guangyi Liu, Arash Amini +1

Achieving safety is a critical aspect of attaining autonomy in a platoon of autonomous vehicles. In this paper, we propose a distributionally robust risk framework to investigate c…