We aim to address fundamental challenges in the design and analysis of
algorithms for modeling, estimation and control of data-intensive hightech systems.

Major societal challenges in energy, health, and computing depend on
advancements in high-tech mechatronic systems, which rely on numerous
sensors and actuators. These machines benefit from large amounts of data and modular complexity to achieve unprecedented precision and
throughput. Our goal is to utilize sensor and actuator data to design and
analyze decision algorithms for state estimation, system identification, and fault detection in advanced high-tech machines. The focus is on ensuring the statistical optimality of algorithms, their numerical robustness, and explainability. These advancements, closely tied to applications in, e.g., the semiconductor industry, aim to serve as key enablers for meeting the extreme performance requirements of future high-tech machines by improving control, diagnostics, sensor and data collection optimization, and energy efficiency.