Prof. Dr. Alisa Rupenyan-Vasileva

Work at ZHAW

Position

Rieter Stiftungsprofessur Industrial AI

Focus

Industrial AI, Automation of manufacturing systems, robotics, process optimization, neurosymbolic AI

Teaching

• CAS Machine Intelligence - Machine learning module
• Machine Learning and Data Mining
• Artificial Intelligence I

Experience

• Senior scientist, Automatic control laboratory
  ETH Zurich
  08 / 2020 - 07 / 2023
• Group leader, Automation and control group
  inspire AG
  02 / 2018 - 07 / 2023
• Head of Application Development
  Qualysense AG
  01 / 2014 - 09 / 2017
• Postdoctoral fellow (ETH fellow on individual grant)
  ETH Zurich
  01 / 2011 - 09 / 2013
• Postdoctoral researcher
  University of Amsterdam
  01 / 2010 - 12 / 2010

Education and Continuing education

Education

• PhD / Physics
  Vrije Universiteit Amsterdam, The Netherlands
  01 / 2005 - 12 / 2009
• MSc / Laser physics and optics
  Sofia University, Bulgaria
  10 / 2004 - 12 / 2005
• BSc / Engineering Physics
  Sofia University, Bulgaria
  10 / 1999 - 12 / 2004

Network

Membership of networks

• SATW Topical Platform AI
• International Federation for Automatic Control - Industry Committee
• IEEE - senior member
• Innosuisse (innovation expert)
• NCCR Automation

ORCID digital identifier

ORCID ID: 0000-0002-2170-8564

Social media

Linkedin

Projects

• Cloud-Enabled Learning Controllers for HVAC Application / Project leader / ongoing
• Robotic 3D printing toolkit / Project leader / ongoing
• Continuous optimization and control for advanced manufacturing / Project leader / ongoing
• Digital Manufacturing as a Service Technology / Project leader / ongoing
• Intelligent planning for robot-based manufacturing / Project leader / ongoing
• Smart urban green spaces / Team member / completed

Publications

Articles in scientific journal, peer-reviewed

• Rupenyan, A., Bayanduryan-Levasgani, N. and Khosravi, M. (2026) 'Data-driven high-performance motion system optimization in practice', Control Engineering Practice, 172(106904). doi: 10.1016/j.conengprac.2026.106904.
• Nobar, M. et al. (2026) 'Guided multi-fidelity Bayesian optimization for data-driven controller tuning with digital twins', IEEE Robotics and Automation Letters, 11(5), pp. 5294–5301. doi: 10.1109/lra.2026.3671557.
• Wu, M., Rupenyan, A. and Corves, B. (2025) 'Autogeneration and optimization of pick-and-place trajectories in robotic systems : a data-driven approach', Robotics and Computer-Integrated Manufacturing, 97(103080). doi: 10.1016/j.rcim.2025.103080.
• Wu, M., Rupenyan, A. and Corves, B. (2025) 'Iterative learning control with mismatch compensation for residual vibration suppression in delta robots', Nonlinear Dynamics, 113(16), pp. 21631–21651. doi: 10.1007/s11071-025-11299-6.
• König, C. et al. (2025) 'Adaptive Bayesian optimization for high-precision motion systems', IEEE Transactions on Automation Science and Engineering, 22, pp. 15627–15637. doi: 10.1109/tase.2025.3565776.
• Kavas, B. et al. (2025) 'In-situ controller autotuning by Bayesian optimization for closed-loop feedback control of laser powder bed fusion process', Additive Manufacturing, 99(104641). doi: 10.1016/j.addma.2025.104641.
• Zagórowska, M. et al. (2025) 'Efficient safe learning for controller tuning with experimental validation', Engineering Applications of Artificial Intelligence, 143(109894). doi: 10.1016/j.engappai.2024.109894.
• Liao-McPherson, D. et al. (2024) 'Layer-to-layer melt pool control in laser powder bed fusion', IEEE Transactions on Control Systems Technology. doi: 10.1109/TCST.2024.3464118.
• Nobar, M. et al. (2024) 'Guided Bayesian optimization : data-efficient controller tuning with digital twin', IEEE Transactions on Automation Science and Engineering, 22, pp. 11304–11317. doi: 10.1109/TASE.2024.3454176.
• Guidetti, X. et al. (2024) 'Force controlled printing for material extrusion additive manufacturing', Additive Manufacturing, 89(104297). doi: 10.1016/j.addma.2024.104297.
• Kavas, B. et al. (2023) 'Layer-to-layer closed-loop feedback control application for inter-layer temperature stabilization in laser powder bed fusion', Additive Manufacturing, 78(103847). doi: 10.1016/j.addma.2023.103847.
• König, C. et al. (2023) 'Safe risk-averse bayesian optimization for controller tuning', IEEE Robotics and Automation Letters. doi: 10.1109/LRA.2023.3325991.

Book chapters, peer-reviewed

Rupenyan, A. and Balta, E. C. (2023) 'Robotics and manufacturing automation', in The impact of automatic control research on industrial innovation : enabling a sustainable future. Wiley, pp. 169–189.

Written conference contributions, peer-reviewed

• Wu, M., Rupenyan, A. and Corves, B. (2025) 'Singularity-avoidance control of robotic systems with model mismatch and actuator constraints', in 2025 European Control Conference (ECC). IEEE, pp. 2545–2550. doi: 10.23919/ecc65951.2025.11187077.
• Li, J. et al. (2024) 'Safe time-varying optimization based on Gaussian processes with spatio-temporal kernel', in Conference on Neural Information Processing Systems, NeurIPS 2024, Vancouver, Canada, 10-15 December 2024. NeurIPS. doi: 10.21256/zhaw-31795.
• Yan, J. et al. (2024) 'MPC of uncertain nonlinear systems with meta-learning for fast adaptation of neural predictive models', in IEEE 20th International Conference on Automation Science and Engineering (CASE). IEEE, pp. 1910–1915. doi: 10.1109/CASE59546.2024.10711717.
• Zagorowska, M. et al. (2024) 'Tuning of Online Feedback Optimization for setpoint tracking in centrifugal compressors', in IFAC-PapersOnLine. Elsevier, pp. 881–886. doi: 10.1016/j.ifacol.2024.08.448.

Oral conference contributions and abstracts

• Rupenyan, A. and Sawicki, B. (2025) 'From research to practice in manufacturing : interactive session I', in 2025 European Control Conference (ECC). IEEE, p. 3013. doi: 10.23919/ecc65951.2025.11187228.
• Rupenyan, A. (2025) 'From research to practice in manufacturing', in 2025 European Control Conference (ECC). IEEE, p. 3010. doi: 10.23919/ecc65951.2025.11186949.

Publications before appointment at the ZHAW

An up-to-date list of publications can be found on Google Scholar.

go back