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  • Arun K. Tangirala

Arun K. Tangirala

Active fault tolerant active vibration dual-mode Model Predictive Control for a smart flexible beam
Design Principles for Perfect Adaptation in Biological Networks with Nonlinear Dynamics
Probabilistic Adaptive Slow Feature Analysis for State Estimation and Classification
On biological networks capable of robust adaptation in the presence of uncertainties: A linear systems-theoretic approach
On biological networks capable of robust adaptation in the presence of uncertainties: A linear systems-theoretic approach
Sloppiness: Fundamental study, new formalism and its application in model assessment
Sloppiness: Fundamental study, new formalism and its application in model assessment
Joint clustering and prediction approach for travel time prediction
An Early Warning System for Earthquake
Discovering adaptation-capable biological network structures using control-theoretic approaches
Discovering adaptation-capable biological network structures using control-theoretic approaches
Bayesian Optimal Experiment Design for Sloppy Systems
Bayesian Optimal Experiment Design for Sloppy Systems
Development of a collision warning system using Explainable AI for Indian Urban Traffic
A Multivariate Analysis Framework for Vehicle Detection From Loop Data Under Heterogeneous and Less Lane Disciplined Traffic
Systems-Theoretic Approaches to Design Biological Networks with Desired Functionalities
Systems-Theoretic Approaches to Design Biological Networks with Desired Functionalities
Optimal Filtering and Residual Analysis in Errors-in-Variables Model Identification
A new index for information gain in the Bayesian framework⁎
A new index for information gain in the Bayesian framework⁎
Identification of Errors-in-Variables Models Using Dynamic Iterative Principal Component Analysis
A systems-theoretic approach towards designing biological networks for perfect adaptation
A systems-theoretic approach towards designing biological networks for perfect adaptation
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Founded in 2024, the School brings together several faculty with expertise in various areas of Data Science and AI, to work together on impactful problems of direct relevance to the society.

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