Institution profile

Aston University

Academic institutioneurope · gb
Official website
Research library60linked papers
Opportunities0open roles
Selected work

Representative Papers

Decoding Phenotypes: A Framework for Fusing Genomic Language Models and Neuroimaging

Aug 09, 2026

Cross-modal heterogeneity poses a significant challenge to the effective integration of genomic and neuroimaging data, thereby limiting precise diagnosis of neurological disorders. To address this issue, this work proposes GeneFuse, a novel multimodal learning framework that, for the first time, incorporates a pretrained genomic language model (GLM) into imaging-genomics fusion tasks. GeneFuse preserves sequence context through genotype-conditioned feature modulation (GCFM) and dynamically adjusts the contribution of genomic features via an uncertainty-aware residual fusion mechanism. Evaluated under APOE-stratified scenarios, the proposed method achieves AUROC scores of 0.77 and 0.83 on NC vs. MCI and NC vs. AD classification tasks, respectively, substantially outperforming existing fusion approaches.

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Beyond Peak TOPS/W: A System-Level Perspective on Hybrid Digital, Analogue and Neuromorphic Computing

Aug 04, 2026

This work addresses the high energy consumption and data-movement bottlenecks confronting AI deployment in edge and distributed settings by proposing a digitally orchestrated hybrid computing architecture that synergistically integrates analog and neuromorphic computing. Physical computing units are selectively introduced only where they offer substantial energy-efficiency gains, while a unified digital control layer manages uncertainty and ensures fault tolerance. Departing from conventional peak TOPS/W metrics, the study establishes a deployment-oriented, system-level efficiency evaluation framework. By combining photonic computing, in-memory computing, and neuromorphic hardware with programmable digital control, mature software stacks, and comprehensive system-level energy accounting, the work delineates the architecture’s suitability for matrix operations and event-driven tasks, systematically uncovering its energy-efficiency potential, software requirements, and engineering challenges to provide both a theoretical foundation and practical pathway for efficient AI system design.

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Privacy-Preserving Industrial Ergonomics: mmWave-Based Automated REBA Scoring and Pose Estimation

Jul 01, 2026

This study addresses the inefficiency of manual REBA assessments in industrial settings and the privacy concerns associated with vision-based approaches by proposing, for the first time, an end-to-end multitask learning framework leveraging millimeter-wave radar to enable privacy-preserving automatic REBA scoring through 3D human skeletal reconstruction. The method integrates biomechanical constraints and temporal smoothness losses, and employs an oversampling strategy to mitigate data imbalance in high-risk postures. Evaluated on the MMFi dataset, the model achieves a REBA risk-level classification accuracy of 77.78%, with a mean absolute error of 0.93 for high-risk samples and an inference latency of only 5.70 milliseconds.

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Recent publications

Latest Papers

Decoding Phenotypes: A Framework for Fusing Genomic Language Models and Neuroimaging

Aug 09, 2026

Cross-modal heterogeneity poses a significant challenge to the effective integration of genomic and neuroimaging data, thereby limiting precise diagnosis of neurological disorders. To address this issue, this work proposes GeneFuse, a novel multimodal learning framework that, for the first time, incorporates a pretrained genomic language model (GLM) into imaging-genomics fusion tasks. GeneFuse preserves sequence context through genotype-conditioned feature modulation (GCFM) and dynamically adjusts the contribution of genomic features via an uncertainty-aware residual fusion mechanism. Evaluated under APOE-stratified scenarios, the proposed method achieves AUROC scores of 0.77 and 0.83 on NC vs. MCI and NC vs. AD classification tasks, respectively, substantially outperforming existing fusion approaches.

0 citationsRead paper

Beyond Peak TOPS/W: A System-Level Perspective on Hybrid Digital, Analogue and Neuromorphic Computing

Aug 04, 2026

This work addresses the high energy consumption and data-movement bottlenecks confronting AI deployment in edge and distributed settings by proposing a digitally orchestrated hybrid computing architecture that synergistically integrates analog and neuromorphic computing. Physical computing units are selectively introduced only where they offer substantial energy-efficiency gains, while a unified digital control layer manages uncertainty and ensures fault tolerance. Departing from conventional peak TOPS/W metrics, the study establishes a deployment-oriented, system-level efficiency evaluation framework. By combining photonic computing, in-memory computing, and neuromorphic hardware with programmable digital control, mature software stacks, and comprehensive system-level energy accounting, the work delineates the architecture’s suitability for matrix operations and event-driven tasks, systematically uncovering its energy-efficiency potential, software requirements, and engineering challenges to provide both a theoretical foundation and practical pathway for efficient AI system design.

0 citationsRead paper

Privacy-Preserving Industrial Ergonomics: mmWave-Based Automated REBA Scoring and Pose Estimation

Jul 01, 2026

This study addresses the inefficiency of manual REBA assessments in industrial settings and the privacy concerns associated with vision-based approaches by proposing, for the first time, an end-to-end multitask learning framework leveraging millimeter-wave radar to enable privacy-preserving automatic REBA scoring through 3D human skeletal reconstruction. The method integrates biomechanical constraints and temporal smoothness losses, and employs an oversampling strategy to mitigate data imbalance in high-risk postures. Evaluated on the MMFi dataset, the model achieves a REBA risk-level classification accuracy of 77.78%, with a mean absolute error of 0.93 for high-risk samples and an inference latency of only 5.70 milliseconds.

0 citationsRead paper