Rupenyan Alisa

• Scientific and technical supervision of projects in the field of advanced control algorithms, merging machine learning with optimal control for mechatronics and process control
• Multidisciplinary background combining expertise in laser physics, robotics and control, and machine learning
• Data-driven optimization and automation of manufacturing processes
  - Process and performance optimization
  - Data-driven methods for fault detection/prediction
  - Data-driven process control
  - Learning-based control, predictive control

• Data-driven insights for smart grinding / Innosuisse Nr 31695.1 IP-ENG
• Décolleteur 4.0  / Innosuisse Nr 32835.1 IP-ENG
• Data-Driven Adaptive Control for High-End Motion Systems / Innosuisse Nr 46716.1 IP-ENG
• Adaptive Coating Tracker & In-situ Verification (ACTIV) / Innosuisse Nr 37896.1 IP-ENG

• Mohammad Khosravi, Varsha Behrunani, Piotr Myszkorowski, Roy S. Smith, Alisa Rupenyan and John Lygeros, Performance-driven cascade controller tuning with Bayesian optimization,  IEEE Transactions on Industrial Electronics, 2021 (early access)

• Alisa Rupenyan, Mohammad Khosravi and John Lygeros, Performance-based Trajectory Optimization for Path Following Control Using Bayesian Optimization, Computing research repository, abs/2103.15416, 2021

• Christopher König, Mateo Turchetta, John Lygeros, Alisa Rupenyan and Andreas Krause, Safe and efficient model-free adaptive control via Bayesian optimization, 2021 IEEE International Conference for Robotics and Automation, 2021

• Markus Maier, Alisa Rupenyan, Christian Bobst and Konrad Wegener, Self-optimizing grinding machines using Gaussian process models and constrained Bayesian optimization, The International Journal of Advanced Manufacturing Technology, volume 108, pages 528–552, 2020

siehe auch Research Collection ETH