Dr. Alisa Rupenyan-Vasileva

Work at ZHAW

Position

• Head of Industrial AI group
• Head of Industrial AI group

Focus

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

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

• International Federation for Automatic Control - Industry Committee
• IEEE member
• Innosuisse (innovation expert)
• NCCR Automation

ORCID digital identifier

ORCID ID: 0000-0002-2170-8564

Projects

• Continuous optimization and control for advanced manufacturing / Project leader / laufend
• Digital Manufacturing as a Service Technology / Project leader / laufend
• Smart urban green spaces / Team member / laufend
• Intelligent planning for robot-based manufacturing / Project leader / laufend

Publications

• Nobar, Mahdi; Keller, Jürg; Rupenyan, Alisa; Khosravi, Mohammad; Lygeros, John,

  2024.

  Guided Bayesian optimization : data-efficient controller tuning with digital twin.

  IEEE Transactions on Automation Science and Engineering.

  Available from: https://doi.org/10.1109/TASE.2024.3454176

• König, Christopher; Ozols, Miks; Makarova, Anastasia; Balta, Efe C.; Krause, Andreas; Rupenyan, Alisa,

  2023.

  Safe risk-averse bayesian optimization for controller tuning.

  IEEE Robotics and Automation Letters.

  Available from: https://doi.org/10.1109/LRA.2023.3325991

• Kavas, Barış; Balta, Efe C.; Tucker, Michael; Rupenyan, Alisa; Lygeros, John; Bambach, Markus,

  2023.

  Layer-to-layer closed-loop feedback control application for inter-layer temperature stabilization in laser powder bed fusion.

  Additive Manufacturing.

  78(103847).

  Available from: https://doi.org/10.1016/j.addma.2023.103847

• Rupenyan, Alisa; Balta, Efe C.,

  2023.

  Robotics and manufacturing automation

  .

  In:

  The impact of automatic control research on industrial innovation : enabling a sustainable future.

  Wiley.

  pp. 169-189.

• Yan, Jiaqi; Chakrabarty, Ankush; Rupenyan, Alisa; Lygeros, John,

  2024.

  MPC of uncertain nonlinear systems with meta-learning for fast adaptation of neural predictive models [paper].

  In:

  IEEE 20th International Conference on Automation Science and Engineering (CASE).

  IEEE 20th International Conference on Automation Science and Engineering (CASE), Bari, Italy, 28 August - 1 September 2024.

  IEEE.

  pp. 1910-1915.

  Available from: https://doi.org/10.1109/CASE59546.2024.10711717

• Zagorowska, Marta; Ortmann, Lukas; Rupenyan-Vasileva, Alisa; Mercangöz, Mehmet; Imsland, Lars,

  2024.

  Tuning of Online Feedback Optimization for setpoint tracking in centrifugal compressors [paper].

  In:

  12th IFAC Symposium on Advanced Control of Chemical Processes (ADCHEM), Toronto, Canada, 14-17 July 2024.

  Elsevier.

  pp. 881-886.

  Available from: https://doi.org/10.1016/j.ifacol.2024.08.448

Publications before appointment at the ZHAW

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

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