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20192026
most citedLearning neural state-space models: do we need a state estimator?

10 citations · 30 across the 18 of their papers we have counts for

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13 papers · 1 filter

eess.SY2026

Joint State-Parameter Inference Enhances Estimation Performance in Model-Based Digital Therapeutics for Type 1 Diabetes

Milad Banitalebi Dehkordi, Vihangkumar V. Naik, Manas Mejari +2

Blood glucose estimation is the cornerstone of model-based decision support (DS) and Automated Insulin Delivery (AID) systems. Control systems that rely on physiologic/compartmenta…

eess.SY2026

Learning reduced-order latent linear models for Kalman filtering of nonlinear systems

Manas Mejari, Milad Banitalebi Dehkordi, Dario Piga

We propose a filtering-oriented end-to-end learning framework to identify reduced-order models explicitly tailored for state estimation in high-dimensional nonlinear systems. An au…

eess.SY2026

Rao-Blackwellized Stein Gradient Descent for Joint State-Parameter Estimation

Milad Banitalebi Dehkordi, Manas Mejari, Dario Piga

We present a filtering framework for online joint state estimation and parameter identification in nonlinear, time-varying systems. The algorithm uses a Rao-Blackwellization techni…

eess.SY2025

Learning Low-Dimensional Embeddings for Black-Box Optimization

Riccardo Busetto, Manas Mejari, Marco Forgione +2

When gradient-based methods are impractical, black-box optimization (BBO) provides a valuable alternative. However, BBO often struggles with high-dimensional problems and limited t…

eess.SY2024★ 2 cited

Bias correction and instrumental variables for direct data-driven model-reference control

Manas Mejari, Valentina Breschi, Simone Formentin +1

Managing noisy data is a central challenge in direct data-driven control design. We propose an approach for synthesizing model-reference controllers for linear time-invariant (LTI)…

eess.SY2023

Data-Driven Synthesis of Configuration-Constrained Robust Invariant Sets for Linear Parameter-Varying Systems

Manas Mejari, Sampath Kumar Mulagaleti, Alberto Bemporad

We present a data-driven method to synthesize robust control invariant (RCI) sets for linear parameter-varying (LPV) systems subject to unknown but bounded disturbances. A finite-l…