Selected work

Representative Papers

Semantic-aware Random Convolution and Source Matching for Domain Generalization in Medical Image Segmentation

Dec 01, 2025

This paper addresses single-source domain generalization (SSDG) for medical image segmentation—i.e., training a model on a single source domain (e.g., CT) and achieving robust cross-modal (e.g., to MR), multi-center, and multi-cardiac-phase segmentation without access to target-domain data or fine-tuning. We propose Semantic-Aware Random Convolution (SARConv), which applies anatomy-guided, label-aware augmentation to source images, and a source-domain matching intensity mapping strategy that adaptively calibrates target-domain intensity distributions during inference. Integrated into mainstream segmentation architectures, these components jointly mitigate semantic and distributional shifts between domains. Evaluated on multiple cross-modal benchmarks, our method achieves state-of-the-art performance, with segmentation accuracy in certain scenarios approaching that of in-domain supervised baselines—establishing a new benchmark for SSDG in medical image segmentation.

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Learn, Check, Test -- Security Testing Using Automata Learning and Model Checking

Sep 26, 2025

Addressing the challenge of modeling and formally verifying security in black-box industrial cyber-physical systems (CPS)—such as those in extended supply chains or classified environments—this paper proposes an end-to-end security analysis framework integrating active automata learning with model checking. Our key contribution is the Context-aware Propositional Mapping (CPM) mechanism, which automatically transforms learned Mealy machines into Kripke-like structures, enabling nondeterministic modeling, semantic enrichment, and scalable instantiation of safety properties. The method requires no internal system knowledge, relying solely on input-output interactions to infer behavioral models and verify security properties of communication protocols. We validate its generality, effectiveness, and toolchain reusability across multiple protocols—including NFC and UDS—demonstrating significant improvements in automation and formal assurance for black-box CPS security testing.

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Recent publications

Latest Papers

Semantic-aware Random Convolution and Source Matching for Domain Generalization in Medical Image Segmentation

Dec 01, 2025

This paper addresses single-source domain generalization (SSDG) for medical image segmentation—i.e., training a model on a single source domain (e.g., CT) and achieving robust cross-modal (e.g., to MR), multi-center, and multi-cardiac-phase segmentation without access to target-domain data or fine-tuning. We propose Semantic-Aware Random Convolution (SARConv), which applies anatomy-guided, label-aware augmentation to source images, and a source-domain matching intensity mapping strategy that adaptively calibrates target-domain intensity distributions during inference. Integrated into mainstream segmentation architectures, these components jointly mitigate semantic and distributional shifts between domains. Evaluated on multiple cross-modal benchmarks, our method achieves state-of-the-art performance, with segmentation accuracy in certain scenarios approaching that of in-domain supervised baselines—establishing a new benchmark for SSDG in medical image segmentation.

0 citationsRead paper

Learn, Check, Test -- Security Testing Using Automata Learning and Model Checking

Sep 26, 2025

Addressing the challenge of modeling and formally verifying security in black-box industrial cyber-physical systems (CPS)—such as those in extended supply chains or classified environments—this paper proposes an end-to-end security analysis framework integrating active automata learning with model checking. Our key contribution is the Context-aware Propositional Mapping (CPM) mechanism, which automatically transforms learned Mealy machines into Kripke-like structures, enabling nondeterministic modeling, semantic enrichment, and scalable instantiation of safety properties. The method requires no internal system knowledge, relying solely on input-output interactions to infer behavioral models and verify security properties of communication protocols. We validate its generality, effectiveness, and toolchain reusability across multiple protocols—including NFC and UDS—demonstrating significant improvements in automation and formal assurance for black-box CPS security testing.

0 citationsRead paper