activity
20172023
most citedHigher-Order Moment-Based Anomaly Detection

12 citations · 33 across the 12 of their papers we have counts for

collaborators

15 papers

eess.SY2023★ 1 cited

Conformance Testing for Stochastic Cyber-Physical Systems

Xin Qin, Navid Hashemi, Lars Lindemann +1

Conformance is defined as a measure of distance between the behaviors of two dynamical systems. The notion of conformance can accelerate system design when models of varying fideli…

eess.SY2023

Convex Optimization-based Policy Adaptation to Compensate for Distributional Shifts

Navid Hashemi, Justin Ruths, Jyotirmoy V. Deshmukh

Many real-world systems often involve physical components or operating environments with highly nonlinear and uncertain dynamics. A number of different control algorithms can be us…

eess.SY2023

A Neurosymbolic Approach to the Verification of Temporal Logic Properties of Learning enabled Control Systems

Navid Hashemi, Bardh Hoxha, Tomoya Yamaguchi +3

Signal Temporal Logic (STL) has become a popular tool for expressing formal requirements of Cyber-Physical Systems (CPS). The problem of verifying STL properties of neural network-…

eess.SY2022★ 1 cited

Risk-Awareness in Learning Neural Controllers for Temporal Logic Objectives

Navid Hashemi, Xin Qin, Jyotirmoy V. Deshmukh +4

In this paper, we consider the problem of synthesizing a controller in the presence of uncertainty such that the resulting closed-loop system satisfies certain hard constraints whi…

math.MG2021

Generalized Outer Bounds on the Finite Geometric Sum of Ellipsoids

Navid Hashemi, Justin Ruths

General results on convex bodies are reviewed and used to derive an exact closed-form parametric formula for the boundary of the geometric (Minkowski) sum of ellipsoids in -…

cs.LG2021

Performance Bounds for Neural Network Estimators: Applications in Fault Detection

Navid Hashemi, Mahyar Fazlyab, Justin Ruths

We exploit recent results in quantifying the robustness of neural networks to input variations to construct and tune a model-based anomaly detector, where the data-driven estimator…