Alisa Rupenyan

Alisa Rupenyan

she/her

Professor, industrial AI for autonomous manufacturing systems

Posts by Collection

publications

Safe risk-averse bayesian optimization for controller tuning

Published in IEEE Robotics and Automation Letters, presented on IEEE ICRA 2023, 2023

RAGoOSe is a novel data-driven approach that combines safe learning with risk-averse Bayesian optimization to safely tune controllers in high-precision systems with variable noise, demonstrating improved performance over traditional methods in both synthetic and real semiconductor manufacturing applications.

Recommended citation: Christopher König, Miks Ozols, Anastasia Makarova, Efe C Balta, Andreas Krause, Alisa Rupenyan (2023). "Safe risk-averse bayesian optimization for controller tuning." IEEE Robotics and Automation Letters. 8(8208 - 8215).
Download Paper | Download Slides

Robotics and Manufacturing Application

Published in The Impact of Automatic Control Research on Industrial Innovation: Enabling a Sustainable Future, John Wiley & Sons, 2024, 2024

A book chapter, focused on advanced control methods, suitable for industrial applications in robotics and precision motion systems.

Recommended citation: Alisa Rupenyan, Efe C. Balta (2024). "Robotics and Manufacturing Application." The Impact of Automatic Control Research on Industrial Innovation: Enabling a Sustainable Future, John Wiley & Sons, 2024.
Download Paper

Time-varying safe data-driven optimization

Published in The Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS 2024), 2024

We propose a method for optimization with unknown and time-varying optimization objective and constraints, based on Bayesian optimization.

Recommended citation: Jialin Li, Marta Zagorowska, Giulia De Pasquale, Alisa Rupenyan, John Lygeros, NeurIPS, 2024
Download Paper | Download Slides

Efficient safe learning for controller- and process parameter tuning

Published in Engineering Applications of Artificial Intelligence, volume 143, 1 March 2025, 109894, 2025

We propose a method for safe and fast data-driven optimization by formulating a series of optimization problems instead of a grid search.

Recommended citation: M Zagorowska, C König, H Yu, EC Balta, A Rupenyan, J Lygeros Engineering Applications of Artificial Intelligence, 2025
Download Paper

In-situ controller autotuning by Bayesian optimization for closed-loop feedback control of laser powder bed fusion process

Published in Additive Manufacturing, Elsevier, volume 99, 5 February 2025, 104641, 2025

We experimentally apply Bayesian Optimization in LPBF to tune an in-layer PI controller to modulate laser power, assessing its performance on wedge geometries prone to overheating.

Recommended citation: Barış Kavas, Efe C Balta, Michael R Tucker, Raamadaas Krishnadas, Alisa Rupenyan, John Lygeros, Markus Bambach, Additive Manufacturing, volume 99, 2025
Download Paper

research

Autonomy - performance

Published:

Methods for increasing the performance of manufacturing systems under unknown constraints (read more…)

Industrial robotics

Published:

Our research in industrial robotics enables robot-based systems to autonomously and precisely perform manufacturing-related tasks

research_group

Our Research Group

Published:

Welcome to the Industrial AI Research Group. Our team focuses on developing solutions for smart manufacturing and industrial systems, in particular industrial autonomous robotics, data-driven optimization for manufacturing, and integration of AI-based methods in various aspects of manufacturing.

talks

teaching

Artificial Intelligence 1

Undergraduate course, ZHAW, Computer Science, 2014

This is an introductory course on the basics of Artificial Intelligence, with a strong focus on applications. It covers topics from deep learning and from planning and search.

MLDM

Undergraduate course, ZHAW, Computer Science, 2024

This is an introductory course in Machine Learning. The lecture notes can be found here.