Defense of Diane Puges

On June 17, 2025, Diane Puges completed her doctoral studies with her defense. In her presentation, “From Linear Orderings to Infinite Trees: Semidefinite Programming for Combinatorial Optimization and Extremal Combinatorics,” she presented her work applying semidefinite methods in a wide range of polynomial optimization applications.

Habilitation colloquium of Dr. Viktoriia Grushkovska

On April 29, 2025, Dr. Viktoriia Grushkovska held the habilitation colloquium entitled “Controlling Nonholonomic Systems: From Steering to Motion Planning in Complex Environments”.

In her talk, she reviewed traditional techniques based on Lie bracket theory for motion planning under nonholonomic constraints and introduced a novel approach that provides a unified solution to these challenges and beyond. The presentation included a discussion on future perspectives in the design of control algorithms for nonholonomic systems operating in complex, dynamic environments, where additional challenges arise due to moving targets and obstacles, external disturbances, and limited knowledge of the mathematical models of both the system and the environment.

Improving magnetic resonance imaging with mathematics

The special research area “Mathematics of Reconstruction in Dynamical and Active Models”, funded by the Austrian Science Fund FWF, was launched in March 2025. Researchers from the University of Klagenfurt, led by Barbara Kaltenbacher (Department of Mathematics), will be contributing their expertise on inverse problems. The aim is to develop new mathematical tools for active, dynamic and model-based imaging modalities.

Defense of Melanie Siebenhofer

Melanie Siebenhofer successfully defended her dissertation, entitled “A Provably Good Connection: Spanning Trees, Edge Expansion, and Semidefinite Programming”, on March 20, 2025, with an mpressive presentation. This marks the fifth defense by a doctoral candidate from the doc.funds doctoral school “Modeling – Analysis – Optimization of discrete, continuous, and stochastic ystems.”
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