Research Zeilinger Overview

D-MAVT News

Talent and serendipity

Taking a position at ETH Zurich was the most important decision in his professional life. Marco Mazzotti is still convinced of this today. Now, at the end of January 2025, he will retire, and he already knows that he will greatly miss the exchange with young researchers and the work on challenging scientific questions.

13.11.2024

COMITES award for Vicentiti and Cattaneo

Francesco Vicentini and Marco Cattaneo, doctoral students at the Department of Mechanical and Process Engineering, have received the COMITES award for scientific excellence within Italian community in Zurich. COMITES are representative bodies of the Italian community, elected by Italians residing abroad in each consular district.

08.11.2024

Outstanding Teaching Assistant Award

Portrait Felix Lange and Michael Weinold

Felix Michael Lange and Michael Weinold receive the 'Outstanding Teaching Assistant Award' of the D-MAVT for the spring semester 2024.

31.10.2024

Could this new tech stop your glasses from fogging up?

BBC News, Dr. Iwan Hächler

30.10.2024

The Hottest Startups in Zurich in 2024

Wired, Prof. Bradley Nelson, Spin-off Nanoflex Robotics

29.10.2024

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Recent Journal Publications

Contact

Prof. Dr. Melanie Zeilinger

Associate Professor at the Department of Mechanical and Process Engineering

Inst. Dynam. Syst. u. Regelungst.
Leonhardstrasse 21
8092 Zürich
Switzerland

Additional Information

When safety is critical, control systems are traditionally designed to act in isolated, clearly specified environments, or to be conservative against the unknown. The goal of our research is to make high performance control available for safety-critical systems that act in varying, uncertain environments, and which are potentially large-scale, composed of numerous interconnected subsystems, and, most importantly, involve human interaction.

A new opportunity to address these challenges is offered by modern computing, sensor and communication technologies, providing increasing computational power and data at previously unseen detail and scale. We develop intelligent control systems that can exploit data, while offering system theoretic guarantees. Our research integrates control, learning and optimization to provide new theoretical frameworks and highly efficient computational tools in three core areas.