Biography

I am a PhD student in the Research Group Data Mining and Machine Learning at the University of Vienna, supervised by Prof. Claudia Plant and co-advised by Prof. Martin Pawelczyk. Before that, I completed my M.Sc. in Computer Science at the University of Vienna, graduating with distinction in 2025. I am interested in understanding how neural networks organize information in their embedding space, and in developing principled methods to make learned representations more robust, fair, safe, and trustworthy.

Broadly, my interests span representation learning, adversarial robustness, fairness in machine learning, and AI safety. I enjoy research that combines empirical work with theoretical analysis, and I am drawn to questions where careful reasoning about geometry and information can lead to simple, principled solutions.

Research Areas

Representation Learning

Understanding how deep networks structure information in their embeddings, from clustering to reinforcement learning.

Fairness

Building models whose decisions do not rely on sensitive attributes such as race, age, or gender, with formal notions of fairness backed by theoretical guarantees.

Adversarial Robustness

Studying how and why neural networks fail under perturbations and adversarial attacks, and developing principled ways to make them more resilient.

AI Safety

Making powerful models reliable and aligned with human intent, with an interest in unlearning, deception detection, and interpretability.

News

Publications

* denotes equal contribution

FairSplit: Decomposing the Embedding Space for Fair Classification

FairSplit: Decomposing the Embedding Space for Fair Classification

A. Shkabrii, A. Beer, Y. Sadikaj, C. Plant
Conference on Neural Information Processing Systems (NeurIPS), 2026
Outperforming 1000 Euclidean Layers With Hyperbolic Goal-Conditioned RL

Outperforming 1000 Euclidean Layers With Hyperbolic Goal-Conditioned RL

T. Klein, T. Lang, A. Shkabrii, L. Miklautz, Y. Velaj, S. Tschiatschek
arXiv preprint, 2026
Understanding and Improving Hyperbolic Deep Reinforcement Learning

Understanding and Improving Hyperbolic Deep Reinforcement Learning

T. Klein*, T. Lang*, A. Shkabrii, A. Sturm, K. Sidak, L. Miklautz, C. Plant, Y. Velaj, S. Tschiatschek
International Conference on Learning Representations (ICLR), 2026
H-SPLID: HSIC-based Saliency Preserving Latent Information Decomposition

H-SPLID: HSIC-based Saliency Preserving Latent Information Decomposition

L. Miklautz*, C. Shi*, A. Shkabrii*, T. T. Davarakis, P. Lam, C. Plant, J. Dy, S. Ioannidis
Conference on Neural Information Processing Systems (NeurIPS), 2025
Breaking the Reclustering Barrier in Centroid-based Deep Clustering

Breaking the Reclustering Barrier in Centroid-based Deep Clustering

L. Miklautz*, T. Klein*, K. Sidak*, C. Leiber, T. Lang, A. Shkabrii, S. Tschiatschek, C. Plant
International Conference on Learning Representations (ICLR), 2025
ReSL: Enhancing Deep Clustering Through Reset-based Self-Labeling

ReSL: Enhancing Deep Clustering Through Reset-based Self-Labeling

A. Shkabrii, T. Klein, L. Miklautz, S. Tschiatschek, C. Plant
Workshop on Spurious Correlation and Shortcut Learning (SCSL) and Workshop on Scaling Self-Improving Foundation Models (SSI-FM) @ International Conference on Learning Representations (ICLR), 2025
Non-Redundant Image Clustering of Early Medieval Glass Beads

Non-Redundant Image Clustering of Early Medieval Glass Beads

L. Miklautz, A. Shkabrii, C. Leiber, B. Tobias, B. Seidl, E. Weissensteiner, A. Rausch, C. Böhm, C. Plant
IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2023

Contact

I am always open to collaboration and happy to chat about research ideas. If you're working on something related or just want to connect, feel free to reach out!