The Adaptive Stress Testing for Robust AI (ASTRA) toolbox provides tooling to support model developers and testing in the full life cycle of making more robust AI Systems through the application of adaptive stress testing and adversarial training.
(2021) Robust Deepfake Detection project for the Deep Learning course at ETH. Authors: David Kamm, Nicolas Muntwyler, Alexander Timans, Moritz Vandenhirtz
Course materials for Causal Intelligence: causal inference, causal discovery, causal machine learning, counterfactual reasoning, robust AI, explainable AI, and decision intelligence.
Research collaboration conducted from the Palace of Science (Belgrade) in collaboration with Prof. El Mahdi El Mhamdi (École Polytechnique, Paris). This collaboration originated in the context of a French Government Scholarship (BGF – Bourse du Gouvernement français) awarded for PhD cotutelle studies
Framework using UMAP-DBSCAN for unsupervised discovery of multi-modal Hidden Bias Subgroups (HBSs) in AI failure spaces. Implements a scalable Multi-Domain MMD Objective to mitigate latent Acquisition Bias and enhance robustness in clinical Ocular Disease Recognition (ODR).
Learned influence-topology control for robust multi-agent LLM networks.
A local AI solution for accurate text extraction from receipts. Runs multiple modes concurrently with multi-threading and Ollama for robust and efficient local deployment.
AURA (Adversarial Use for Reliable Assessment) is a research framework for generating and benchmarking architecture-agnostic adversarial perturbations against OCR systems while preserving human readability. Inspired by human visual cognition, Gestalt principles, and perceptual illusions, AURA evaluates structural, geometric, photometric, and ...
A hierarchical AI safety architecture with asymmetric supervisory control.
'Robust Deepfake Detection' project for the Deep Learning course at ETH Zurich, 2021. Authors (alphabetic): David Kamm, Nicolas Muntwyler, Alexander Timans, Moritz Vandenhirtz.
Repository with the project of the Explainable and Reliable Artificial Intelligence course at UniTS (2024-2025).
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