Data driven approach for performance assessment of linear and nonlinear Kalman filters. - Laya Das; Babji Srinivasan; Raghunathan Rengaswamy (ACC, 2014)

On-line performance monitoring of PEM fuel cell using a fast EIS approach. - Laya Das; Babji Srinivasan; Raghunathan Rengaswamy (ACC, 2015)

Multivariate Control Loop Performance Assessment With Hurst Exponent and Mahalanobis Distance. - Laya Das; Babji Srinivasan; Raghunathan Rengaswamy (IEEE Trans. Control. Syst. Technol., 2016)

A Framework for Efficient Information Aggregation in Smart Grid. - Amit Joshi; Laya Das; Balasubramaniam Natarajan; Babji Srinivasan (IEEE Trans. Ind. Informatics, 2019)

Data-Driven Approaches for Diagnosis of Incipient Faults in DC Motors. - Sai Munikoti; Laya Das; Balasubramaniam Natarajan; Babji Srinivasan (IEEE Trans. Ind. Informatics, 2019)

Hidden representations in deep neural networks: Part 2. Regression problems. - Laya Das; Abhishek Sivaram; Venkat Venkatasubramanian (Comput. Chem. Eng., 2020)

Hidden representations in deep neural networks: Part 1. Classification problems. - Abhishek Sivaram; Laya Das; Venkat Venkatasubramanian (Comput. Chem. Eng., 2020)

Graph neural network based approximation of Node Resiliency in complex networks. - Sai Munikoti; Laya Das; Balasubramaniam Natarajan (CoRR, 2020)

Bayesian Graph Neural Network for Fast identification of critical nodes in Uncertain Complex Networks. - Sai Munikoti; Laya Das; Balasubramaniam Natarajan (SMC, 2021)

Robust and efficient swarm communication topologies for hostile environments. - Vipul Mann; Abhishek Sivaram; Laya Das; Venkat Venkatasubramanian (Swarm Evol. Comput., 2021)

A unified theory of emergent equilibrium phenomena in active and passive matter. - Venkat Venkatasubramanian; Abhishek Sivaram; Laya Das (Comput. Chem. Eng., 2022)

Object detection-based inspection of power line insulators: Incipient fault detection in the low data-regime. - Laya Das; Mohammad Hossein Saadat; Blazhe Gjorgiev; Etienne Auger; Giovanni Sansavini (CoRR, 2022)

Scalable graph neural network-based framework for identifying critical nodes and links in complex networks. - Sai Munikoti; Laya Das; Balasubramaniam Natarajan (Neurocomputing, 2022)

Uncertainty-aware deep learning for digital twin-driven monitoring: Application to fault detection in power lines. - Laya Das; Blazhe Gjorgiev; Giovanni Sansavini (CoRR, 2023)

There is more to graphs than meets the eye: Learning universal features with self-supervision. - Laya Das; Sai Munikoti; Mahantesh Halappanavar (CoRR, 2023)

Anomaly detection for automated inspection of power line insulators. - Laya Das; Blazhe Gjorgiev; Giovanni Sansavini (CoRR, 2023)

A general framework for quantifying aleatoric and epistemic uncertainty in graph neural networks. - Sai Munikoti; Deepesh Agarwal; Laya Das; Balasubramaniam Natarajan (Neurocomputing, 2023)

Simulation-driven deep learning for locating faulty insulators in a power line. - Blazhe Gjorgiev; Laya Das; Seline Merkel; Martina Rohrer; Etienne Auger; Giovanni Sansavini (Reliab. Eng. Syst. Saf., 2023)

A testbed for studying the interactions between human operators and advanced control systems. - Ritu Ranjan; Laya Das; Niket S. Kaisare; Rajagopalan Srinivasan (Comput. Chem. Eng., 2023)

Uncertainty-aware deep learning for monitoring and fault diagnosis from synthetic data. - Laya Das; Blazhe Gjorgiev; Giovanni Sansavini (Reliab. Eng. Syst. Saf., 2024)

Challenges and Opportunities in Deep Reinforcement Learning With Graph Neural Networks: A Comprehensive Review of Algorithms and Applications. - Sai Munikoti; Deepesh Agarwal; Laya Das; Mahantesh Halappanavar; Balasubramaniam Natarajan (IEEE Trans. Neural Networks Learn. Syst., 2024)