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Middlesex University

Academic institutioneurope · gb
Official website
Research library13linked papers
Opportunities0open roles
Selected work

Representative Papers

Metropolis-Hastings Sampling of Phylogenetic Networks: Correcting for Symmetries

Aug 12, 2026

This study addresses the under-sampling of symmetric phylogenetic networks in Metropolis–Hastings sampling over leaf-labeled networks, which arises from neglecting internal symmetries. To correct this bias between leaf-labeled and fully labeled representations, the authors propose a symmetry-aware approach based on quotient Markov chains. A key innovation is the use of μ-vectors to efficiently determine the size of a network’s automorphism group. They prove that orchard networks—including trees—have trivial automorphism groups and thus require no correction. By integrating μ-vector computation, graph automorphism algorithms, and quotient chain construction, the method substantially accelerates automorphism group evaluation, enhancing both the accuracy and efficiency of posterior sampling. A Python implementation is provided to facilitate practical application.

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Learning the Word Problem: Geodesic Lengths and Cryptographic Applications

Jul 28, 2026

This work addresses the word problem in group theory by introducing WPNet, the first end-to-end graph neural network architecture that encodes unreduced words as dynamic graphs and clusters algebraically equivalent elements in a continuous embedding space. Without requiring explicit reduction, WPNet identifies geodesic representatives and predicts their lengths directly. This approach achieves the first direct learning of geodesic length in non-abelian groups, effectively solving the word problem for Baumslag–Solitar groups BS(1,2) and Artin groups. Furthermore, the method successfully breaks the Wagner–Magyarik public-key cryptosystem, demonstrating that cryptographic schemes based on the word problem are vulnerable to practical structural leakage, thereby posing a tangible risk to their viability as post-quantum candidates.

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SafeStep: AI-powered Travel Assistance for Elderly People with Frailty or Dementia

Jul 23, 2026

This study addresses the heightened risk of trip disruptions and safety concerns faced by older adults with frailty or cognitive impairments during urban travel. To mitigate these challenges, the authors propose an AI-driven personalized mobility assistance system that innovatively integrates large language models (e.g., GPT) with the Anticip8 behavior prediction engine. By incorporating graph-structured route modeling, the system introduces a novel trip graph representation capable of forecasting individualized failure scenarios at each stage of a journey and generating as well as evaluating context-aware intervention strategies. In 26 real-world mobility trials, the system significantly improved participants’ likelihood of reaching their destinations, along with self-reported confidence and perceived safety, with the combined GPT–Anticip8 approach yielding the best performance.

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Recent publications

Latest Papers

Metropolis-Hastings Sampling of Phylogenetic Networks: Correcting for Symmetries

Aug 12, 2026

This study addresses the under-sampling of symmetric phylogenetic networks in Metropolis–Hastings sampling over leaf-labeled networks, which arises from neglecting internal symmetries. To correct this bias between leaf-labeled and fully labeled representations, the authors propose a symmetry-aware approach based on quotient Markov chains. A key innovation is the use of μ-vectors to efficiently determine the size of a network’s automorphism group. They prove that orchard networks—including trees—have trivial automorphism groups and thus require no correction. By integrating μ-vector computation, graph automorphism algorithms, and quotient chain construction, the method substantially accelerates automorphism group evaluation, enhancing both the accuracy and efficiency of posterior sampling. A Python implementation is provided to facilitate practical application.

0 citationsRead paper

Learning the Word Problem: Geodesic Lengths and Cryptographic Applications

Jul 28, 2026

This work addresses the word problem in group theory by introducing WPNet, the first end-to-end graph neural network architecture that encodes unreduced words as dynamic graphs and clusters algebraically equivalent elements in a continuous embedding space. Without requiring explicit reduction, WPNet identifies geodesic representatives and predicts their lengths directly. This approach achieves the first direct learning of geodesic length in non-abelian groups, effectively solving the word problem for Baumslag–Solitar groups BS(1,2) and Artin groups. Furthermore, the method successfully breaks the Wagner–Magyarik public-key cryptosystem, demonstrating that cryptographic schemes based on the word problem are vulnerable to practical structural leakage, thereby posing a tangible risk to their viability as post-quantum candidates.

0 citationsRead paper

SafeStep: AI-powered Travel Assistance for Elderly People with Frailty or Dementia

Jul 23, 2026

This study addresses the heightened risk of trip disruptions and safety concerns faced by older adults with frailty or cognitive impairments during urban travel. To mitigate these challenges, the authors propose an AI-driven personalized mobility assistance system that innovatively integrates large language models (e.g., GPT) with the Anticip8 behavior prediction engine. By incorporating graph-structured route modeling, the system introduces a novel trip graph representation capable of forecasting individualized failure scenarios at each stage of a journey and generating as well as evaluating context-aware intervention strategies. In 26 real-world mobility trials, the system significantly improved participants’ likelihood of reaching their destinations, along with self-reported confidence and perceived safety, with the combined GPT–Anticip8 approach yielding the best performance.

0 citationsRead paper