Outstanding Research: AICS Researchers Impress with Top Achievements in AI and Cybersecurity

The Carinthian regional government has once again honored outstanding scientific work in the field of digitalization. The Department for Artificial Intelligence and Cybersecurity is delighted to receive two award-winning theses at once.

Dr. techn. DI Jasmin Wachter, BA BSc was awarded the 2025 Digitalization Scholarship, endowed with €1,600, for her dissertation “A utility-based approach to security in robotics”. In her PhD thesis in computer science, she investigates economic and technical incentives for increased security in complex cyber-physical systems: She develops game-theoretic models for analyzing network security and presents, among other things, an optimization-based approach for efficient network hardening under budget constraints.

Veronika Semmelrock, MSc BSc, was also recognized for her master’s thesis “Investigating the grounding bottleneck for a large-scale configuration problem”. In her work, she analyzes scalability issues in Answer Set Programming and demonstrates how her newly developed “constraint-aware guessing” approach significantly reduces the memory requirements of large AI configuration problems.

Both works make an important contribution to Carinthia’s digital future and underscore the excellent research quality at the AICS department.

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/

 

Thema für eine Bachelor- oder Masterarbeit: Dark Pattern against Privacy

“Dark Patterns” sind manipulative Designstrategien auf Webseiten oder Apps, die Nutzer dazu bringen, unerwünschte Handlungen auszuführen, wie z.B. mehr Daten preiszugeben oder unerwünschte Abonnements abzuschließen. In diesem Projekt sollen Dark Patterns speziell im Hinblick auf Datenschutz klassifiziert und bestehende Taxonomien angepasst werden, um ihren Einfluss auf den Datenschutz besser zu verstehen. Zusätzlich wird die Verbreitung solcher Praktiken in Österreich untersucht und eine erste rechtliche Einschätzung zu deren Compliance mit Datenschutzgesetzen vorgenommen.

Bei Interesse melden Sie sich bitte bei Frau Jasmin Wachter (jasmin [dot] wachter [at] aau [dot] at)!

Trends in Recommendations Systems – A Netflix Perspective

Thursday April 7th 2022 | 05.30 pm (CET) | via Zoom

Anuj Shah, Ph. D. | Senior Machine Learning Research Practitioner at Netflix |

Click here to register for the meeting:

https://zoom.us/meeting/register/tJYvdO-gqzMiEtKOfNIgcZAZOQ8jA3i_b3Pi

 

Abstract:

Recommendation systems today are widely used across many applications such as in multimedia content platforms, social networks, and ecommerce, to provide suggestions to users that are most likely to fulfill their needs, thereby improving the user experience. Academic research, to date, largely focuses on the performance of recommendation models in terms of ranking quality or accuracy measures, which often don’t directly translate into improvements in the real-world. In this talk, we present some of the most interesting challenges that we face in the personalization efforts at Netflix. The goal of this talk is to sunshine challenging research problems in industrial recommendation systems and start a conversation about exciting areas of future research.

 

Bio:

Anuj Shah is a Senior Machine Learning Research Practitioner at Netflix. For the past 10+ years, he’s been working on an applied research team focused on developing the next generation of algorithms used to generate the Netflix homepage through machine learning, ranking, recommendation, and large-scale software engineering. He is extremely passionate about algorithms and technologies that help improve the Netflix customer experience with highly personalized consumer-facing products like the Continue Watching row, the Top 10 rows amongst many others. Prior to Netflix, he worked on machine learning in the Computational Sciences Division at the Pacific Northwest National Laboratory focusing on technologies at the intersection of proteomics, bioinformatics and Computer Science for 8 years. He has a Ph.D. from the Computer Science department at Washington State University and a Masters in C.S. from Virginia Tech