Yingzhao Lian

Yingzhao Lian is a former researcher at École Polytechnique Fédérale de Lausanne (EPFL), contributing to advanced control systems and optimization. Their work focuses on data-driven predictive control methodologies applied to building automation, energy management, and mixed traffic systems. Key collaborations include projects with the National Natural Science Foundation of China and Swiss National Science Foundation. Lian's research emphasizes practical implementation, such as experiments in the Polydome building testbed and real-time control on embedded systems like the TI LaunchPad microcontroller. Their contributions bridge theoretical frameworks (e.g., DeePC algorithms, Willems' Fundamental Lemma) with real-world applications in HVAC systems, demand response programs, and energy harvesting sensor networks.

Education: No explicit education details provided.

Research Interests: Specializes in data-driven control theory, optimization under constraints, and energy-efficient systems. Recent work addresses challenges in adaptive building control, distributed traffic flow smoothing, and fault detection using kernel methods. Their approach often integrates physics-based principles with data-driven techniques to ensure robustness and scalability.

Grants/Funders: Supported by initiatives including BMBF-Project ENSure II, NSF Creative Research Groups, and the NCCR Automation Project. Research units affiliated include EPFL's La3 and La departments, focusing on control systems and embedded technologies.

Labs/Teams: Active in teams developing control algorithms for smart buildings, autonomous vehicles, and power grids, with emphasis on distributed computing architectures.