Defensio by Teresa Rauscher

On October 7, Teresa Rauscher successfully defended her dissertation on “Imaging with Nonlinear Ultrasound Waves: Modeling, Analysis and Numerics,” becoming the 10th graduate of the doc.funds doctoral school “Modeling-Analysis-Optimization of discrete, continuous and stochastic systems.” The thesis was reviewed by Prof. Mechthild Thalhammer, University of Innsbruck, and Prof. Lehel Banjai, Heriot-Watt University, Edinburgh, who also served as examiners. The dissertation was supervised by Barbara Kaltenbacher, in collaboration with Prof. Vanja Nikolić, Radboud University, Nijmegen. Read more →

Defensio by Phuoc Truong Huynh

On October 1, 2025, Phuoc Truong Huynh successfully completed his doctoral studies with his thesis on “Parameter identification from optimized measurements, with applications in acoustics.” He is thus the eighth doctoral graduate of the doc.funds doctoral school “Modeling-Analysis-Optimization of discrete, continuous, and stochastic systems.” The examiners (and reviewers of the dissertation) were Prof. Tapio Helin, LUT University Finland, and Prof. Vincent Duval, INRIA, France. The thesis was supervised by Barbara Kaltenbacher, AAU, and Prof. Daniel Walter, Humboldt University of Berlin.

GPU4U Pilot Project (Student-focused GPU access within DHInfra.at)

The GPU4U project initiated by AICS and ITEC (PI: J. Wachter & Mathias Lux) is being launched as a pilot within the DHInfra.at infrastructure initiative. DHInfra.at is developing a national Machine Learning infrastructure primarily for Digital Humanities, with CLARIAH partners and DH research projects receiving priority access. GPU4U serves as an exploratory pilot to understand how computational resources might be utilized by a broader student community, while the infrastructure’s core mission remains focused on Digital Humanities research.

Addressing the GPU Access Gap for Students

As part of this pilot exploration, GPU4U is granted access to computational resources for select student projects. The following three use cases will be evaluated as the infrastructure is being set up:

Use Case 1: Supporting Resource-Intensive Student Projects: One workshop per semester where students from various faculties can present project ideas and apply for limited resource allocation. Approximately five selected projects per semester may receive temporary access to the DHInfra cluster (featuring 12x H200 GPUs and multiple L40s) for tasks such as LLM fine-tuning, VR simulations, or other computational workloads.

Use Case 2: Limited LLM Inference Access for Educational Purposes: One GPU may be allocated to provide controlled access to LLM inference (via Ollama and various models) through an API for specific educational use cases. This would enable experimentation with prompting strategies, context size, temperature, and other parameters within structured learning environments. The inference could be integrated into select courses, such as a pilot “Introduction to Databases” course, to explore how such tools might support learning in non-technical disciplines.

Use Case 3: Experimental VR-Based Teaching Support: This use case explores the potential for VR in teaching through streaming solutions, particularly in Game Studies and Engineering contexts, which could reduce dependency on individual high-performance workstations. Implementation would depend on local infrastructure availability, such as VR headset access, and remains subject to further evaluation.

PI: Jasmin Wachter (AICS) & Mathias Lux (ITEC)

Further Reading: https://www.dhinfra.at/

https://www.dhinfra.at/2025-09-09-gpu4u-pilot-use-case/

Mathe läuft bei uns

As Team “Mathe läuft bei uns”, the sporty mathematicians Teresa Rauscher, Johannes Schmucker, and Angelika Wiegele took on the USI Team Run Challenge 2025. The event was held as part of the Klagenfurter Altstadlauf, where participants had to complete a 5 km course in temperatures exceeding 30 degrees Celsius. The USI awarded a prize to the team with the smallest difference between their own time and the average time of all teams.
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