Prof. Dr. Alisa Rupenyan-Vasileva

Prof. Dr. Alisa Rupenyan-Vasileva

ZHAW School of Engineering

Technikumstrasse 71
8400 Winterthur

+41 58 934 43 92
alisa.rupenyan@zhaw.ch

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

ORCID digital identifier

ORCID ID: 0000-0002-2170-8564

Social media

Projects

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.
Balula, S. et al. (2024) 'Data-driven reference trajectory optimization for precision motion systems', Control Engineering Practice, 144(105834). doi: 10.1016/j.conengprac.2023.105834.
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.