News
I am happy to share that I presented our work at The Modeling, Estimation and Control Conference (MECC) 2025 under the Invited Session — “Integrating Machine Learning and Control Theory for Sustainable Transportation Solutions”.
- Paper ID: MoDT6.4
- Time: 16:15–16:30
- Location: Sheraton Hotel, Pittsburgh, PA, United States
- Title: RCUKF: Data-Driven Modeling Meets Bayesian Estimation
- Authors: Kumar Anurag, Kasra Azizi, Francesco Sorrentino, and Wenbin Wan
Our work introduces the RCUKF framework, an algorithm that integrates Reservoir Computing and the Unscented Kalman Filter for accurate state estimation of nonlinear systems. By bridging data-driven learning with Bayesian filtering, RCUKF aims to enhance prediction reliability in real-world, safety-critical applications such as autonomous systems and sustainable mobility platforms.
It was a wonderful experience to present alongside inspiring researchers pushing the boundaries of machine learning and control theory.