Manuel Mazzara

About

Manuel Mazzara is a professor of Computer Science at Innopolis University (Russia) with a research background in software engineering, service-oriented architectures and programming, concurrency theory, formal methods and software verification. He cooperated with european and US industry, plus governmental and inter governmental organizations such as the United Nations, always at the edge between science and software production. The work conducted by Manuel and his team in recent years focuses on the development of theories, methods, tools and programs covering the two major aspects of software engineering: the process side, describing how we develop software, and the product side, describing the results of this process.

Employment

Employment history is unavailable.

Education

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Projects & Funding

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Publications (230)

• Design Pattern Prediction From Source Code Using LLM–Based Feature Engineering and SVM Classification
  IET Software 2026 DOI: 10.1049/sfw2/7163249
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• A Spiking and Memory-Enhanced State-Space Model for Hyperspectral Image Classification
  IEEE Geoscience and Remote Sensing Letters 2026 DOI: 10.1109/LGRS.2026.3663905
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• Concept Disentangled Transformer for Hyperspectral Image Classification
  IEEE Transactions on Geoscience and Remote Sensing 2026 DOI: 10.1109/TGRS.2026.3715319
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• HGT-Autoscaler: Dependency-Aware Proactive Scaling of Microservices Using Heterogeneous Graph Transformers
  IEEE Access 2026 DOI: 10.1109/ACCESS.2026.3712255
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• Iformer: Irregular Spatial–Spectral Transformer for Hyperspectral Image Classification
  IEEE Transactions on Geoscience and Remote Sensing 2026 DOI: 10.1109/TGRS.2026.3710374
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• Label-Free Land-Cover Mapping From Airborne LiDAR via SAM Distillation With Convolutional LoRA Adapters
  IEEE Access 2026 DOI: 10.1109/ACCESS.2026.3689916
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• Relational Graph–Guided Selective State-Space Model for Hyperspectral Image Classification
  IEEE Transactions on Geoscience and Remote Sensing 2026 DOI: 10.1109/TGRS.2026.3722968
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• Saliency-Modulated Group Dynamic Attention for Hyperspectral Image Classification
  IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2026 DOI: 10.1109/JSTARS.2026.3703962
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• Spiking Morphological Mamba for Hyperspectral Image Classification
  IEEE Geoscience and Remote Sensing Letters 2026 DOI: 10.1109/LGRS.2026.3719487
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• WorldMamba: World-State Representation Learning for Hyperspectral Image Classification
  IEEE Geoscience and Remote Sensing Letters 2026 DOI: 10.1109/LGRS.2026.3719470
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